TYBA-SEM-VI-book-Cognitive-Psychology-English-Version-munotes

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LEARNING, FORGETTING AND
IMAGERY - I

Unit Structure
1.0 Objectives
1.1 Introduction
1.1.1 Learning
1.2 How Does Classical Conditioning Work?
1.2.1 Stage -1 Before Conditioning:
1.2.2 Stage -2 During Conditioning.
1.2.3 Stage -3 After Conditioning
1.3 Learning Through Operant Conditioning
1.3.1 The Basic Principle of Operant Conditioning Is:
1.4 Types of Behaviours:
1.4.1 Reinforcements in Operant Conditioning
1.4.2 Reinforcement Schedule:
1.4.3 Different Types of Schedules:
1.5 Learning Through the Observ ation
1.6 Learning: Encoding, Storage and Retrieval
1.7 Level of Processing:
1.8 Encoding Specificity:
1.9 Summary
1.10 Questions
1.11 Reference
1.0 OBJECTIVES
 To understand the concept of Learning and learning theories
 To understand the process of enco ding, storage and retrieval munotes.in

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Cognitive Psychology
2  To understand the concept of classical conditioning with experiments
 To understand the concept of operant/Instrumental conditioning with
experiments
1.1 INTRODUCTION
As humans, we are all concerned with learning and rememberin g new
information so that we can apply it when needed. Such as acquiring
concepts while studying and attempting to recall the material in order to
use it in the classroom, in exams, or in everyday life or learning to play
guitar . In everyday life, we frequ ently struggle to remember passwords
and phone numbers. On the other hand, we all often remember with the
untimely deaths of performers and celebrities.
This entire process is linked to memory. As we will see, many models for
memory have been proposed to explain its strengths and weaknesses.
Memory often works as a room to store the learned information.
1.1.2 Learning:
Psychologists often define learning as any relatively permanent change in
behaviour as a result of practice and experience.
Primary theori es of Learning:
To explain how and why people behave the way they do, a variety of
learning theories have arisen. Environmental impacts on the learning
process are central to these learning theories . Associations,
reinforcements, punishments, and observat ions are examples of
environmental influences.
The following are some of the most common learning theories:
 Classical conditioning
 Operant conditioning
 Social learning

Let’s start by taking a closer look at each theory and then comparing them
to one anot her.
1.2 HOW DOES CLASSICAL CONDITIONING WORK?
Learning through Classical conditioning
The theory of classical conditioning comes from a school of thought in
Psychology known as behaviourism.
Behaviourism is based on the assumption that:
1. All learning occ urs through interactions with the environment
2. The environment shapes behaviour
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3 Although the concept of classical conditioning has had a significant
impact on behaviourism , the man who discovered it was not a
psychologist. During his investigations on the digestive systems of dogs, a
Russian physiologist named Ivan Pavlov discovered the principles of
classical conditioning. Before being fed, the dogs in Pavlov's trials began
to salivate whenever they saw the white coats of his lab assistants.
So, how does c lassical conditioning explain learning? Learning occurs
when an association is formed between a previously neutral stimulus and a
naturally occurring stimulus, according to classical conditioning
principles. In Pavlov's experiments, for example, he combine d the natural
stimulus of food with the sound of a bell. The dogs would naturally
salivate in response to food, but after food was followed by sound of bell
multiple times , the dogs would salivate to the sound of the bell alone.
It is necessary to learn th e fundamental concepts of classical conditioning
in order to gain a better understanding of how it works. The formation of a
connection between two stimuli results in a trained response in classical
conditioning. This procedure is divided into three stages .
1.2.1 Stage -1: Before Conditioning:
The initial step in the classical conditioning process is to find a naturally
occurring stimulus that will elicit a response automatically. A naturally
occurring response is the food, in which response the dog natural ly
salivates. The unconditioned stimulus (UCS) causes an unconditioned
response (UCR) at this step of the process. For example, presenting food
(the UCS) causes a salivation response to occur naturally and effortlessly
(the UCR). There is also a neutral st imulus at this point that has no effect.
This neutral stimulus will not evoke a reaction unless it is combined with
the unconditional stimulus (UCS) .
Let's look at the two critical components of this stage.
The Unconditional stimulus (UCS) is the one that evokes a response in a
consistent, natural, and instinctive manner. When you smell one of your
favourite foods, for example, you may become really hungry. The
fragrance of the meal is the unconditioned stimulus in this case.
The unconditional response (UCR ) is an unlearned response to an
unconditioned stimulus that occurs naturally. The unconditioned reaction
in our example is a sensation of hunger triggered by the smell of food.
(An unconditioned stimulus is matched with an unconditioned response in
the pr econditioning phase. After that, a neutral stimulus is introduced .)
1.2.2 Stage -2 During Conditioning
The previously neutral stimulus is repeatedly matched with the
Unconditioned Stimulus (UCS) during the second stage of the classical
conditioning process . The previously neutral stimulus creates an
association as a result of this pairing. The previously neutral stimulus is
now referred to as the Conditioned stimulus (CS). This stimulus has now munotes.in

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Cognitive Psychology
4 been conditioned into the subject's response. The Conditioned s timulus
(CS) is a previously neutral stimulus that has been paired with
unconditioned stimulus multiple times and eventually triggers a
Conditioned response (CR) . Lets assume that in this case when you smell
your favourite food, a whistling sound is also ma de. Sound of whistle is
neutral stimulus here. Similarly, sound of bell or white lab coats were
neutral stimuli in Pavlov’s experiment.
(During the conditioning phase, a neutral stimulus is paired with an
Unconditioned stimulus. The neutral stimulus event ually becomes the
Conditioned stimulus)
1.2.3 Stage -3 After Conditioning
Once the UCS and the CS have formed a relationship, the Conditioned
stimulus alone will elicit a response even if the unconditioned stimulus is
not present. The Conditioned response (CR) is the resultant response.The
learnt response to a previously neutral stimulus is known as the
Conditioned response. The Conditioned response in our scenario would be
to feel hungry when you hear the whistle. IN Pavlov’s experiment,
Conditional respon se is dog’s salivation at the sound of bell or at the site
of lab coats. The Conditioned response is triggered by the Conditioned
stimulus alone in the after -Conditioning stage.

Source: Google images
Terms in Pavlov’s the experiment: -
It is necessary to understand the following terms in order to understand
Classical Conditioning. A Neutral Stimulus is one that evokes no response
at first. The ringing of a bell was introduced by Pavlov as a neutral
stimulus. A stimulus that causes an instinctive respons e is known as an munotes.in

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Learning, Forgetting and Imagery- I
5 Unconditioned Stimulus. The food was the Unconditional Stimulus in
Pavlov's experiment. An instinctive response to a stimulus is known as an
Unconditioned Response. In Pavlov's experiment, the Unconditioned
Reaction is the dogs drooling fo r food. A conditioned stimulus is one that
can elicit a Conditioned Response in the future.The Conditioned Stimulus
in this experiment was the ringing of the bell (after pairing with food
multiple times) , and the Conditioned Response was salivation at the s ound
of bell .
It is worth noting that the neutral stimulus transforms into the Conditioned
Stimulus. It is also vital to remember that the Unconditioned and
Conditioned Responses are identical save for the stimulus that elicits
them. The re sponse in this case was salivation, however the
Unconditioned Response was caused by food, but the Conditioned
Response was activated by the bell signalling the arrival of food.
1.3 LEARNING THROUGH OPERANT CONDITIONING
B.F. Skinner, a behavioural psychologist, was the first to describe operant
conditioning. Skinner argued that classical conditioning could not account
for all types of learning and was more interested in studying how
consequences of actions influence behaviour.
Operant conditioning, like classical conditi oning, is based on the
formation of associations. However, through operant conditioning,
associations are formed between an action and its consequences. When an
action produces a desired result, it is more probable that the activity will
be repeated in the future. However, if the behaviour results in a negative
outcome, the behaviour becomes less likely to occur in future .
For example, when lab rats press the lever after the green light comes on,
they will receive a food pellet as a reward. When they presse d the lever
following the red light , they received a slight electric shock. As a result,
the rats learn to press the lever when the light is green and avoid the red
light.

Source: Google images
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6 But Operant Conditioning doesn't just take place in experi mental settings
while training lab animals. It also plays an important role in everyday
learning. Reinforcement and punishment always take place in natural
settings, as well as in more structured settings like classrooms or therapy
sessions.
1.3.1 The Basi c Principle of Operant Conditioning is:
 The actions that are reinforced are stronger and are more likely to
occur again in the future. If you tell a joke in class and everyone
laughs, you're more likely to joke in a class again. Reinforcement is
any conse quence of a behaviour that is a desired outcome for the doer.

 The actions that result in punishment or negative consequences will be
lessened, and they will be less likely to occur in the future. If you tell
the same joke to a different class and no one laughs, you will be less
likely to tell it again in the future. If your teacher scolds you for
shouting an answer in class, you may be less inclined to disrupt the
class again.
1.4 TYPES OF BEHAVIOURS :
a. Respondent behaviour - Pulling your hand away from a hot fire or
jerking your leg when the doctor taps on your knee are examples of
automatic and reflexive actions. You are not required to learn these
habits. They just happen on their own, unintentionally.

b. Operant behaviour - These behaviours are under our conscious control,
on the other hand. Some may happen accidentally, while others are
planned, but the consequences of these actions determine whether or
not they happen again in the future. The effects of our activities on the
environment, as well as the consequences of those acts, are a crucial
element of the learning process.
1.4.1 Reinforcements in operant conditioning
Any occurrence that strengthens or increases the behaviour it follows is
referred to as reinforcement. Reinforcers are divided into two categories.
The behaviour increases in each of these circumstances of a
reinforcement.
a. Positive reinforce ment : These are positive events or outcomes that
occur as a result of the behaviour. A response or behaviour is
strengthened by the addition of praise or a direct reward in positive
reinforcement situations. If you work hard and your boss rewards you
with a bonus, this is a positive reinforce ment .

b. Negative reinforce ment : This involv es the elimination of unpleasant
occurrences or consequences after the exhibition of a behaviour . The
removal of something perceived as unpleasant strengthens a response
in these cases. For example, If you are doing homework so that your munotes.in

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Learning, Forgetting and Imagery- I
7 teacher does not scold you, your behaviour is being negatively
reinforced. Behaviour of d oing homework is letting you remove
unpleasant consequence if teacher scolding.
Punishments in the operant conditioning
Punishment is the occurrence of a negative event or outcome that leads to
a reduction in the behaviour that follows. There are two type s of
punishments available. The behaviour decreases in both of these
situations.
a. Positive Punishments - When an undesirable event or outcome is
presented order to weaken the response. For example, scolding for
misbehaving.

b. Negative Punishments - When a des irable event or outcome is
withdrawn after a behaviour happens, this is known as punishment by
removal. For example, taking away a child's video game after
disobedience.
1.4.2 Reinforcement schedule:
It is important to keep in mind that when using reinforc ement for
modifying the behaviour, scheduling the reinforce ments plays a significant
role to have a great impact in the behavioural changes.
1.4.3 Different types of Schedules:
a. Continuous reinforcement - It means delivering the reinforcement
immediately, e very time a response occurs.

b. Fixed - Ratio Schedule -involves delivering reinforcement after a
certain number of responses . For example - after every 5th time when
the same response occurs, it will get the reinforcement.

c. Fixed - Interval Schedule - involves delivering reinforcement after a
certain amount of time period. For example, reinforcement every 2
hours or in every 15 days.

d. Variable - ratio schedule - It involved delivering reinforcements
randomly, after the responses without having a fixed number o f time
of the response needs to occur. For example, first reinforcement may
be given after 3 trials and second after any random number of trails
such as 6 trails.

e. Variable - Interval schedule - It involved the deliverance of the
reinforcement randomly / without following the fixed time to reinforce
after the response occur s. For example, first reinforcement is delivered
at a random time such as after 15 minutes and second after 45 minutes,
third after 10 minutes, etc.
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8 1.5 LEARNING THROUGH THE OBSERVATIO N
Albert Bandura believed that not all the types of learnings happen through
the association or reinforcements but some also happen by observation.
He suggested that quite a bit of learning happens through perception and
observation . Kids notice the activ ities of everyone around them, especially
guardians and kin, and afterward copy these practices. In his notable Bobo
doll experiment, Bandura uncovered how effectively youngsters could be
directed to mirror even adverse activities. Kids who watched a video of a
grown -up thrashing an enormous inflatable doll were then substantially
more likely to duplicate those equivalent activities whenever allowed an
opportunity. On the other had, the group of kids that watched a grown -up
play nicely with the doll, displa yed same behaviour themselves.
Bandura noted that not all the learning are supposed to change the
behaviour of an individual . Children learn new things everyday from the
observation. Also, it will be used or appears when it's needed or there is
any motivati on for it.
Main differences between these 3 Learning theories.
Classical
Conditioning Operant
Conditioning Observational
learning
Learning occurs by
forming
associations
between naturally
occurring stimuli
and a previously
neutral stimulus Learning oc curs when behaviours are followed by
either reinforcement
or punishment Learning occurs
through observation
The neutral stimulus
must occur
immediately before
the naturally
occurring one The consequences
must quickly follow
the behavior Observations can
take place at any
time
Focuses on
automatic, naturally
occurring
behaviours Focuseson
voluntary
behaviours Focuses on the give -
and-take interaction
between social,
cognitive, and
environmental
influences

1.6 LEARNING: ENCODING, STORAGE AND
RETRIEVAL
Learning is the process of accumulating knowledge that can be recovered
later to assist us in achieving our objectives if all goes well. As a result, munotes.in

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Learning, Forgetting and Imagery- I
9 we'll look at the aspects that aid or hinder learning, the factors that can
contribute to forgetting, or t he inability to recall previously acquired
information when needed. We will examine how well learned knowledge
or information is retained or not , throughout time spans ranging from a
few minutes to weeks, months, and years. This chapter focuses on how
know ledge is stored in (i.e., learned) and retrieved (i.e., remembered) from
long-term memory, as well as how information might be lost (forgotten)
when it is needed.
There are the main stages that are involved in the process of learning and
remembering (or fo rgetting) the information.
i. ENCODING
ii. STORAGE
iii. RETRIEVAL.
Encoding :
Encoding involves the initial experience, perceiving and learning the
information. This simply means paying attention to the stimulus and
understanding it.
There are mainly 3 types of encodi ng methods:
1. Visual (Picture)
2. Acoustic (Sound)
3. Semantic (Meaning)
For example, how do you remember a phone number that you looked up in
the phone book? If you can see it, use visual coding, but if it repeats itself,
it is acoustic (by sound) coding. Semant ic encoding is encoding
information by understanding its meaning.
There is evidence that this is the most important coding system in short -
term memory (STM) - the acoustic coding. When a person is presented
with a list of numbers and letters, he tries to keep them in STM by
studying them verbally.
The essay is a verbal process, regardless of whether the list of items is
presented acoustically (someone reads it out loud) or visually (on a sheet
of paper). The most important coding system in long -term memory (LTM)
seems to be semantic coding ( coding based on meaning of the stimulus ).
However, the i information in the LTM can also be optically and
acoustically encoded .
1.7 LEVEL OF PROCESSING:
In their levels of processing hypothesis, Craik and Lockhart (1972)
emphasi zed the importance of encoding. According to this theory,
‘surface' or ‘shallow' encoding of materials leads to poor retention,
whereas ‘deep', more significant encoding leads to better retention and
remembering. On this account, simple repetitious practise does not aid
memory, but deeper , semantic processing does. Furthermore, according to munotes.in

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10 this viewpoint, learning does not have to be purposeful. Incidental
learning, which occurs as a result of paying attention to the content in
some way, can be pow erful if the material is thoroughly processed. The
Levels of Processing theory was put to the test early on (Craik & Tulving,
1975).


Source: Google images
Mnemonics :
There are various encoding strategies which help to enhance the
memory .These strategie s are called as mnemonics. These are used in
various situations of our life. For example, when we prepare for the big
speeches, or prepare to remember something without fail. There are
different types of mnemonics:
a. Categorization is the key principle of th e mnemonic. Grouped or
clusters of the information to the familiar group would help more to
encode and recall the information easily as compared to the words which
are non -categorized. Many studies proved that categorization technique
helps effectively by memorizing rather than non -categorization. For
example, instead of remembering one’s phone number as 20835397, one
may form groups of 2 digits making it easier to remember such as 20 83
53 97.

b. Method of loci is another method in which mental imagery is used
to enhance the recall of the information. For example -if you want to
remember a list of items such as purse, tree, table etc. then first memorize
a familiar road and important locations on that road. Now, lets say the first
location is a shop. Now combi ne first item to be remembered with first
location and form an interactive imagery. For example a shop selling
purses, so on and so forth. When it is time to remember the items, you can
just take mental walk through the road and locations will help you
remember items as well. More weird the imagery, better the recall.

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11 c. The Pegword method is similar to the method of loci, but here one
uses a sequence of highly imaginable nouns linked by rhymes to the
number sequence. standard example is ‘One is a bun, two is a shoe, three
is a tree, four is a door, five is a hive, six is sticks, seven is heaven, eight is
a gate, nine is wine and ten is a hen.’ To recall up to 10 items in sequence,
using the pegword method, you would image the first item interacting with
a bun, the second interacting with a shoe, and so on.
1.8 ENCODING SPECIFICITY:
encoding specificity principle states that recall is better if the retrieval
context is similar to the encoding context (Brown &Craik, 2000; Nairne,
2005; Tulving& Rosenbaum, 200 6). For example, assume that you are in
the garden and you go to your room to get something ;but once you arrive
in your the room, you have no idea why you are there, and once you go
back to the garden, you will remember what exactly you wanted from the
room. This example shows that, to have a better recall, individuals are
supposed maintain same context for encoding and retrieval .
Storage
This has to do with the nature of memory, that is where the information is
stored, how long the memory lasts (duratio n), how much information can
be stored at any time (capacity), and the type of information stored.Store
information affects how we retrieve it. There has been a large amount of
research on the difference between short -term memory (STM) and long -
term memory (LTM) with this regard.
Miller has given a magical number of 7+/ -2 storage capacity of STM.
However, Miller did not specify how much information can be stored in
each location.This is because if we can "chunk" information together, we
can store more infor mation in our short -term memory. Information can
only be stored for a short time in the STM (0 -30 seconds). On the other
hand, the capacity of long -term memory (LTM) is considered unlimited
and it lasts for a lifetime.
Retrieval
Recalling the encoded infor mation is called a retrieval. If we can't
remember something, it is probably because we can't get it back. When
asked to retrieve something from memory, the difference between STM
and LTM becomes very clear. STM is stored and retrieved sequentially.
For example, if a group of participants were given a list of words to
remember and then asked to recall the fourth word on the list, the
participants would go through the list in the order they heard it to retrieve
information.
LTM is stored and retrieved by t he link or cue . That's why you can
remember what you think if you go back to the room where you first
thought about it. It stores o rganizational information that may be useful for
research. You can arrange information in sequence (for example,
alphabetical ly, by size, or by time). Imagine a patient discharged from the munotes.in

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12 hospital whose treatment involves taking different medications at different
times, changing dressings, and exercising. If the doctor gives these
instructions in the order they should be taken during the day (i.e., in
chronological order), it helps the patient to remember them. Some of the
important phenomenon’s with respect to LTM include:
Context effect: it was provided by Godden and Baddeley’s (1975) study of
scuba divers who learned lists ei ther under water or on dry land and were
then tested either 20 feet underwater or on land. It was found that lists
learned under water were better recalled under water than on land and lists
learned on dry land were better recalled on land than under water . Overall,
recall in the same context as study was some 50 per cent better.
State dependent memory effects: It occurs if memory is better when
internal physiological conditions at learning are reinstated at testing.
Mood dependent memory: It means that mem ory is better when the mood
at learning is reinstated at the time of remembering the learned
information .
1.9 SUMMARY
Learning is defined as a relatively permanent change in behaviour . As
individuals , we are learning almost everywhere; as we observe and a ttend
to a given information or even our surroundings. learning will take place
as we observe, identify and eventually exercise what we att end to, putting
things into action and if all those actions become habits one can say that
they have learned somethin g new.
Learning is also linked to memory, since whatever an individual observes
in the process of encoding and retrieving is the information in order to put
forththe learned information in to action.
The process of learning might vary from person to perso n and also
depending on the context, which we can understand through the learning
theories such as
a) Classical conditioning which focuses on learning that occurs based
on conditioning that takes place with a given construct, or simply
put learning from pair ed association.
b) Operant conditioning also known as instrumental conditioning, which
explains how an individual learns with the help of reinforcements either
positive or negative,
c) Social learning, a theory given by popular social psychologist Albert
Bandura; aims to explain how an individual's environment can bring
change in the behaviour which is primarily due to learning that Bandura
termed as vicarious learning.
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Learning, Forgetting and Imagery- I
13 Memory is an integral part of learning process that occurs through a
threestep proces s of encoding, storage and retrieval. Encoding is paying
attention to and understanding the material to be learned, storage is
retaining the material and retrieval is remembering it from storage
whenever needed. Learning will not happen if any of these sta ges is
disrupted.
Another influential theory of memory which also explains learning is
levels of processing. According to this theory information can be
processed at either shallow, phonemic or semantic level and deeper the
level of encoding better the recall.
1.10 QUESTIONS
1. Explain in detail theories of learning.
2. What are levels of processing?
3. Explain the process of encoding, storage and retrieval
4. Describe reinforcement along with types of schedules
5. What are Mnemonics and its types?
6. What is learning through observation?
1.11 REFERENCES
 Gilhooly, K.; Lyddy,F.&Pollick F. (2014). Cognitive Psychology,
McGraw Hill Education.

 Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
Laboratory. (5thed.). Sage Publications (Indian reprint 2015)

 Matli n, M.W. (2013). Cognitive Psychology, 8thed., international
student version, John Wiley & sons

 Solso, R.L., Maclin, O.H., & Maclin, M.K. (2013). Cognitive
Psychology. Pearson education, New Delhi, first Indian reprint 2014

 Ashcraft, M. H. &. Radvansky, G . A. (2009). Cognition. (5th ed),
Prentice Hall, Pearson education

 https://www.google.com/search?q=brook%27s+imagery+task&client
=safari&hl=en -
us&prmd=ismvn&sxsrf=ALiCzsYfZiCnswvYVpazeGdwH1R_VQXU
Q:1651993649
358&so urce=lnms&tbm=isch&sa=X&ved=2ahUKEwionZD1q8_3Ah
U1LqYKHWfpDvkQ_AUoAXoECAIQAQ&biw=414&bih=712&dpr
=2#imgrc=1XpQWfzCLrhWWM
 https://www.google.com/search?q=mental+scanning+kosslyn&client=
safari&hl=en -
us&prmd=nisv&sxsrf=ALiCzsYqGtJFG5W 7lClD80Yl7QbE6BlCQ:1
651993922043&source=lnms&tbm=isch&sa=X&ved=2ahUKEwjZspmunotes.in

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14 P3rM_3AhVRyosBHfbQC5kQ_AUoAnoECAIQAg&biw=414&bih=
712&dpr=2#imgrc=AISt_JIRzcfzrM
 https://www.google.com/search?q=mental+scanning+kossl yn&client=
safari&hl=en -
us&prmd=nisv&sxsrf=ALiCzsYqGtJFG5W7lClD80Yl7QbE6BlCQ:1
651993922043&source=lnms&tbm=isch&sa=X&ved=2ahUKEwjZsp
P3rM_3AhVRyosBHfbQC5kQ_AUoAnoECAIQAg&biw=414&bih=
712&dpr=2#imgrc=YMMfy2gOlOSC4M&imgdii=DkF7gOUFf4vuT
M
 https://www.google.com/search?q=rat+lever+pressing+experiment+im
ages&tbm=isch&ved=2ahUKEwjVw5v4rM_3AhWug2MGHZ8eDpk
Q2cCegQIABAC&oq=rat+lever+pressing+experiment+images&gs_lc
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AuMzMuM5gBAKABAbABBcABAQ&sclient=mobile -gws-wiz
img&ei=RG13YtWREK6HjuMPn724yAk&bih=712&biw=414&clien
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15 2
LEARNING, FORGETTING AND
IMAGERY - II
Unit Structure
2.0 Objectives
2.1 Forgetting
2.1.1 Functional Approaches to Forgetting:
2.2 Everyday/ Real World Memory
2.3 Laboratory Studies
2.4 Field Studies
2.5 Flashbulb Memory
2.6 Eyewitnesses' Testimony
2.7 Imagery and Concepts
2.7.1 Imagery and Visuo - Spatial Process
2.7.2 Image Scanning and Comparing
2.7.3 Critical View of Imagery Research and Theory
2.8 Ambiguity of Images
2.8.1 Neuropsychology /Neuroscience of Imagery
2.9 Summary
2.10 Questions
2.11 Referen ces
2.0 OBJECTIVES
 To understand the concept of forgetting
 To understand the functional approaches to forgetting
 To understand the concept of everyday/real world memory
 To understand the concepts of Imagery



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16 2.1 FORGETTING
Forgetting refers to failure to retrieve or recall the information which was
available from the memory.
 Interference : According to interference theory, forgetting is the result
of different memories interfering with one another. The more similar
two or more events are to one anothe r;the more likely interference will
occur. For example, if you are trying to recall answer to an answer in
the exam but instead answer to another question is coming to your
mind, it is forgetting through interference.

 Decay: According to memory tracing t heory, physical and chemical
changes in the brain cause memory track to decay or weather away .
Information in short -term memory lasts for a few seconds, and if it is
not repeated, the traces of neurochemical memory quickly fade. Trace
theory proposes that the time between memory and recall of this
information determines whether the information will be retained or
forgotten. If the time interval is short, more information will be
recalled. If a long time passes, a lot of information will be forgotten
and mem ory will fade.

 The retrieval failure theory: According to this theory, t he main cause
behind forgetting the information is, it never made the information into
a long -term memory properly.

 The cue dependent theory of forgetting: Sometimes the information
is actually present in memory, but it cannot be recalled unless some
recovery hint is given. These hints are items present at the time the
actual memory is encrypted. For example, all of a sudden if you pass
through any particular smell of food, it takes y ou back to your
childhood memories related to that day. The smell is the memory cue
here.
2.1.1 Functional approaches to Forgetting:
Though forgetting seemed like a negative word, there are some incidents
or memories where the individual would never want t o recall it.
1. Retrieval - Induced Forgetting (RIF): The RIF model was
developed by Anderson et al. (1994; Anderson, 2005) and addressed the
forgetting of memories that appear to be caused by the retrieval of related
memories. For example, if you focus on r etrieving memories of what went
well during the holiday, it may reduce your memory of unpleasant things
that happened.

2. Directed Forgetting: In the DF model, participants were instructed
to forget some things but remember others. A concrete example is
provided by short -term chefs who must try to forget previous orders and
keep only the current order until it is replaced by the next order (Bjork,
1970)
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17 3. Think/No -Think (TNT): The TNT model is a relatively new type
of task that reflects situations where a pers on does not want to regain a
memory in the face of a strong signal for that memory. For example, if
you get into an auto accident at a traffic light on your way to work or
school, you may not want to remember that event every time you pass
those lights. To date, it has been explored using verbal materials rather
than real -life traumatic stimuli.
2.2 EVERYDAY/ REAL WORLD MEMORY
Everyday memory refers to the memory activities that frequently occur in
a person's everyday environment. Examples of everyday memo ry include
remembering names, remembering the plan for the day, remembering
groceries, remembering the amount of medicine you take, remembering
phone numbers, directions, route to your workplace or other recent events
of interest. Thus, the distinguishing feature of everyday memory and
related research is that it involves the performance of tasks that take place
in a natural way in the real world. This is in contrast to conventional
memory lab tasks, in which individuals may be asked to do things that are
unlike what they would in the real world, such as memorizing lists of
words.
It is important to realize that everyday memory studies can take place both
in the laboratory and outside the laboratory. In laboratory studies,
individuals were asked to perform memory tasks that they might perform
on a daily basis in the real world, such as sifting through shopping lists,
remembering phone numbers. or memorize information from a news
program. In field studies, individuals are monitored throughout their day
and th eir memory function on specific daily tasks is recorded. For
example, one can measure how accurately an individual takes medication
over a period of time, using microelectronic displays for remote
behavioural monitoring. The advantage of laboratory studies on everyday
memory is that the experimenter has very precise control over the
conditions under which memory occurs and can accurately standardize the
material to be memorized from one individual to another. Such studies are
also closer to the real life an d hence have greater external validity. The
downside of these studies is that the lab environment may not reflect all
the variables that act on real -world individuals and affect their everyday
memory. The advantage of fieldwork or naturalism is that one ca n study
events that have real consequences for the individual participating in the
study, but the disadvantage is that the researcher has little control over and
knowledge of what is going on in the natural environment. Both types of
studies will be discus sed here.
Older adults tend to worry about some types of memory performance on a
daily basis, but not others. For example, Reese et al. reported that older
adults had little difficulty remembering important dates, but were more
concerned about difficulty r emembering names. These authors report that
the elderly fear that a decline in daily memory functions may lead to a loss
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18 interest to the elderly, and the focus of their concern seems to b e on the
daily functions with which they malfunction.
2.3 LABORATORY STUDIES
Laboratory studies often provide evidence of age -related decline in
everyday memory processes. One of the most comprehensive studies was
conducted by West et al. They found evi dence of age -related decline in a
series of daily memory lab tasks. The tasks involved asking subjects to
remember names, locations of items, grocery lists, faces, phone numbers,
and current events. In another study, Frieske and Park studied the memory
of older and younger adults for news presented on radio, television, or in a
newspaper. For all three formats, older adults remembered less
information than younger adults, and both groups did better with
television than the other two formats. Because televis ion contains both
visual and auditory information, these two sources of information appear
to aid memory in young and old. These laboratory studies have forced the
elderly to study unfamiliar subjects, and it is certain that learning new
information, even if it is of an everyday kind, will be affected by age.
2.4 FIELD STUDIES
The picture of daily memory decline with age is quite different when we
study memory in a natural context. In a series of studies, Park and his
colleagues looked at how accurately o lder adults remembered when to
take their medication, using microelectronic displays to record the date
and time of taking the medication. Park and associates. (1992) reported
that adults aged six ty to seventy made almost no medication errors over a
one-month period, even though they were taking at least three different
medications. In contrast, the older adults in the study, aged seventy -eight
to ninety, made more mistakes, but were significantly helped by the
introduction of memory and drug organizers. In a follow -up life
expectancy study of thirty -five to seventy -five-year-olds who were taking
blood pressure medication, Morrell et al. found that adults between the
ages of sixty -five and seventy -five made the fewest medication errors of
all ages and almost never forgot to take their blood pressure medication.
They hypothesized that the reason for this high level of adherence was that
older adults had sufficient cognitive resources to take the medication, and
also had health beliefs and schedules compatible with taking the
medication exactly.
In a follow -up study, a complex set of cognitive, psychosocial, and
contextual variables were used to understand medication adherence in a
sample of rheumatoid arthritis patients taking multiple medications (Park
et al. 1999). These patients were given a large variety of cognitive tests
and completed questionnaires about their health beliefs, lifestyle, stress
levels, and self -perceived performance. These variables are used in
structural equation models to predict adhesio n. In this study, 47% of older
adults (fifty -five to eighty -four) made no mistake in taking the medication
over a one -month period, while the middle -aged participants had a
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19 non-complia nce was reporting a busy and environmentally demanding
lifestyle. Health beliefs, anxiety, and depression are not good predictors of
compliance. Although age is not a predictor of non -compliance, people
with low cognitive abilities of all ages are also mor e likely to become non -
compliant.
We see the work on flash memory as a prime example of research rooted
in a phenomenon of everyday life but using methods derived from
laboratory studies.
2.5 FLASHBULB MEMORY
Flash memory is the vivid memory of a dramatic event and the
circumstances under which that event was experienced or heard.
Flash memory is a type of autobiographical memory. Some researchers
believe that there are reasons to distinguish flash memories from other
types of autobiographical memories, as they are based on factors of
personal importance, consequence, emotion, and surprise. Others believe
that ordinary memories can also be accurate and lasting if they are very
special, have personal meaning or are repeated.
Flash memory has six characterist ics: location, current activity, informant,
relevant influence, other influence, and consequence. Arguably, the main
determinants of a flash memory are a high degree of surprise, a high
degree of consequence, and perhaps emotional arousal.
The term flash m emory was coined by Brown and Kulik in 1977.They
formulated the Special Mechanism Hypothesis, which argues for the
existence of a particular biological memory mechanism, when activated by
an event beyond the degree of surprise and significant consequences,
creates a detailed and circumstantial record of the experience. Brown and
Kulik believe that although flash memories are permanent, they are not
always accessible from long -term memory. The flash memory mechanism
hypothesis specifically argues that flash memories have distinctive
characteristics that are different from those produced by "ordinary"
memorization. The representations produced by the special mechanism are
detailed, precise, vivid and resistant to oblivion. Most of the early
properties of flash memory have been debated since Brown and Kulik first
coined the term. Over the years, four flash memory models have emerged
to explain the phenomenon: the photographic model, the full model, the
emotional integration model, and the importance -oriented mod el.
Additional studies were performed to test the validity of these models.
Most people feel they have exceptionally detailed and vivid memories of
the circumstances in which they first learned of dramatic and significant
events such as the World Trade Cen ter attack , attack on September 11,
2001 , attacks on the London transport system on 7 July 2005 , Princess
Diana's death on August 31, 1997 and, for older readers, the assassination
of John F. Kennedy on November 23, 1963. Brown and Kulik (1977)
examined th e memories for Kennedy assassination and called these
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20 events important to the individual caused the activation of a special
memorization mechanism and recorded as permanent information abo ut
the event and surrounding contextual information around factors such as
who provided the information , where news was learned and what the
individual did after hearing the news.
Weaver (1993) examined the time progression of normal memories (of
meeting friends or roommates) and flash memories (of when President
Bush first announced the start of the War , the first Gulf War on television)
for a total of one year. The memory is brought up three times: within two
days, after three months and after 12 months. Weaver found that accuracy,
as indicated by consistency, decreased quite markedly after three months,
but then stabilized, for both flash and non -flash memories. Both types of
memories are exactly the same (matching the original memos). The main
differenc e that emerged was that the participants were more confident in
flash memory, but this did not translate to increased accuracy.
In general, it seems that flash memory is susceptible to the same types of
forgetting and distortion as normal memory. The benef it of memories of
flash bulb events may be due to their specificity that helps to reduce noise
from similar memories (Cbelli & della Sala, 2008) and repetition effects
(Bonhannon, 1988)
2.6 EYEWITNESSES' TESTIMONY
An important real -life area where memory i s placed with considerable
confidence is the legal system, where witnesses are asked what they
remember about the events surrounding a crime. The law tends toward the
conventional idea that memory is like a videotape that a witness can play
back and relate to with precision. The juries were certainly influenced by
the confidence that witnesses seemed to have and by the volume of details
reported. However, several factors suggest that witness testimony should
be treated with caution. Some witnesses may not h ave seen many of the
events they were asked to report. For example, if you're walking down the
street and a man quickly steps out of the bank as you pass, jumps into a
waiting car, and gets kicked out, you may not be there at the time at that
point . But th en you'll be asked to give a detailed report on the man's
height, hair color, clothing and outfit, color and license plate number, as it
turns out you witnessed a bank robbery. Witnesses inside the bank may
realize that a burglary is happening, but their m emories may be affected
by stress and anxiety. Indeed, Deffenbacher et al. (2004) in a meta -
analytical review found a clearly variable impact of stress and anxiety on
recall of faces and details at crime scenes compared with anxiety and low
stress. In addi tion, if the crime involved a weapon, witnesses focused their
attention on it and w ould be unable to report details unrelated to the
weapon (Tollestrup et al., 1994).
From these and related studies, such as Elizabeth Loftus's study of
implanting false auto biographical memories, it seems likely that post -
event questions and clues can alter memories of the event. These effects
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21 information alters and distorts the memory of previously learne d material.
These results are consistent with the idea that memory can change and
regenerate instead of being fixed and simply reproduce what was
originally perceived, as Bartlett (1932), Neisser (1967) and others
suggested a long time ago.
2.7 IMAGERY AN D CONCEPTS
When we think of a concept, such as "cat," most of us experience a visual
image of a cat, which can be enhanced by visual meows or auditory
meows. Visuals convey information about the appearance of an object,
and images associated with a concep t can seem to be important in using
that concept. To what extent do these images convey useful information
and how do we use them? Image related concepts would be expected to be
important for views representing knowledge, such as those proposed by
Barsalou . Barsalou's conceptual simulation view proposes that knowledge
of concepts is based on reconstructing previous experiences with members
of the category. For example, experience sitting in a chair. These
reconstructions or simulations can often be reported as images. Images
partially reproduce the actual experience, but can generally be
distinguished as less vivid and under human control than the actual
perceptual experience. It should be noted that there are rare medical
conditions such as Charles Bonnet s yndrome, in which people have
extremely vivid but uncontrollable hallucinatory images that are visually
indistinguishable from world perception.(Plummer et al., 2007;
Santhouseetal., 2000).
Although images can be found in all sensory domains, most imaging
studies focus on visual imagery because for most of us, vision is the
dominant channel of perception. and so we'll focus on visuals. Images can
be viewed as representing the appearance of objects, and such knowledge
of what members of general categories lo ok like is an important part of
conceptual knowledge. We will now look at the image results, including:
the relationship between image and perception , image digitization , mental
rotation , image ambiguity and neuroscientific approaches to imaging.
2.7.1 Ima gery and Visuo - Spatial Process
We begin by asking to what extent the imagination of an object uses the
same processes as real perception. With regard to visual images, this is
often discussed in the literature regarding the degree of overlap between
image and visual -spatial processing. We have all found that closing our
eyes helps when trying to imagine an object . This everyday observation is
consistent with the idea that the same mental machinery is involved in
seeing as in imagining. Several experimental studies have reported an
interference between visual tasks and simultaneous visual -spatial
processing, supporting the idea that visual language and perception are
based on the same mental and neural resources. These types of results
were first reported by Brooks (1968) in a series of studies that have
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22 Brooks asked participants to think of a capital "F" and then asked them to
imagine walking clockwise around the letter from a leading corner and
indicating whether each corner wa s on top or bottom of the letter.


Figure 2.1 : Brook’s imagery task
Image source: Google images
Brooks’ imagery task.
When you go around figure F, do you find all the corners at the top or
bottom, or not both? Starting at the lower left corner of the "F", the
answers should be "yes, yes, yes, no, no, no, no, no, no, yes". Participants
were asked to indicate their responses or mark a Y or N on a sheet of
paper with a Y and N in irregular rows. A spatial response (pointing) has
been found to slow perform ance compared to a verbal response. The
reverse pattern was found when the main task was verbal, that is,
remembering a phrase such as "a bird in the hand is out of the
undergrowth" and indicating for each word whether it was a noun or not.
These interfere nce patterns coincide with the visual imaging task, which
makes use of visual spatial resources.
A similar conclusion can be drawn from the study of Baddeley and
Andrade (2000) on reported vividness of images when images are
combined with a series of doubl e scores or count aloud from 1 to 10
several times (a verbal task). Participants were also asked to rate the
vividness of their images on a scale of 0 -10, where 0 means "No image at
all" and 10 means "Clear image and vivid as normal sight/hearing". For
visualizations, the self -reported vibrancy of the images was reduced by
typing, not by counting. When participants were tasked with creating an
auditory image of familiar sounds, such as the ringing of a telephone, the
vividness of the auditory image was repo rted to be reduced by counting
but not typing. into the void. Regarding Baddeley and Hitch's working
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23 results indicate that the visual image uses the visual -spatial notebook
portion of the working memory while the auditory image involv es the
tonal loop component. learning the position of working memory.

Figure 2.2: Baddeley and Andrade’s (2000) result
Image source: Google images
2.7.2 Image Scanning and Comparing
Images are generally created for a practical purpose. For example, you
may need to remove a large closet from a room. Will it be too wide to go
through the door? With the help of pictures, you can try to compare the
dimensions of the closet with the height and width of the door opening to
"see" if the closet will fit. Or you may have purchased a complicated
electrical device that requires multiple sockets to plug in. Are there
enough outlets in your bedroom? The images can be used to try to scan an
image of your bedroom to find and count electrical outlets. Several studies
have examined such image scanning and comparison, focusing primarily
on whether scanning and comparing images is like scanning and
comparing actual visual stimulus .

Figure 2,3: Map for the scanning task. Participants study the
map before the scanning task
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24 In a typical experiment, Kosslyn (1973) asked participants to study images
of objects such as an airplane, a submarine, and a clock tower. The
participants were then asked to take a picture of one of the objects and
focus on a part such as the left or the top of the object in the image. Next,
they were asked to look for a specific part such as the flag on the steeple
and indicate when they found that part. The times to report the finding of
the target part of the image varied a ccording to the distance of the target
from the starting point in the image. In this way, the spatially separated
image parts were also separated in the image to a corresponding extent.
These results support the idea that images are like pictures in the he ad.
Similar results were obtained from a map exploration study (Kosslyn et
al., 1978). Participants first studied a map of a fictional island that
contains seven landmarks. Participants first studied the map, then
imagined the map and were asked to focus o n one object and then scan the
map image to find a second named object. The time to report that the
second object was showed a very strong linear correlation (r = 0.97) with
the physical distance between the objects on the map. These results, in
turn, supp ort the view that the images encode relative distances with some
accuracy.


Figure 2.4: Scanning distance and reaction times
Image source: Google images
Other studies have asked people to compare images. For example, Finke
(1989) asked participants " What is the largest pineapple or coconut?" And
conclude that such comparisons are based on images. Moyer (1973) found
that the faster such questions of size are answered, the greater the
difference between real -life objects. Paivio (1975) also finds a simi lar
pattern when real objects are presented. As a result, people quickly agreed
that whales are bigger than cats, cats are bigger than toasters, suggesting
that related images encode dimensions in a similar way to a painting. The
basic finding that judgmen ts about the difference between elements
represented by symbols are easier to make for real objects that are very
different in practice is known as the symbol distance effect. Again, studies
of image comparison tasks have been done to support the idea that images
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25 processed by the eye of the mind. Similar conclusions can be drawn from
studies on the ability to mentally rotate the images of three -dimensional
objects as described in the above image.
2.7.3 Critical view of imagery research and theory
Although the results of scanning, comparing, and rotating images are
consistent with the idea that images function like images in the head, some
researchers have challenged this viewand now we wil l discuss these
assessments.
Pylyshyn (1981) suggested that the image scan results of Kosslyn et al.
(1978) may reflect the participants' tacit beliefs or knowledge about what
should happen in such tasks. Participants will tend to know that it takes
longe r to travel a longer distance and respond accordingly by inserting rest
periods according to the distance involved.
Pylyshyn (1981) tested participants using island materials similar to those
of Kosslyn et al. (1978). It replicates the original results whe n the analysis
task is given. However, when participants were asked to tell in what
direction one landmark was relative to another (northwest? Direct south?),
the distance between the landmarks did not affect the weather. Therefore,
if the digitization was explicitly requested, the participants produced
results similar to the digitization results. However, if the task does not
explicitly require a scan, the participant will not produce results that are
consistent with the scanned image. A study by Peterson (1983) in which
experimenters had different expectations about how scanning imaging
experiments might work. In a mapping task based on Kosslyn et al. (1978)
half of the testers were told that scanning an image of a map would be
faster than scanning a real map, and half were told the opposite. The
obtained results reflected the experimen ter’sexpectations. When the
perceptual scan needs to be faster, it's significantly faster than the 230 -
millisecond image scan. When the image scanning needed to be faster, th e
distance between the visual and perceptual condition was reduced to an
insignificant 41 milliseconds (and the image speed increased by a
significant 201 milliseconds compared to the condition with the opposite
expectation). Presumably, expectations were subtly selected by the
participants from the small unconscious cues given by the experimenters
and influenced how the participants were responded.
Pylyshyn (1973) criticized the image metaphor for theoretical reasons. He
points out that the image can be da maged arbitrarily (for example, cut in
half or torn into many small pieces), but that the actual picture can only be
transformed according to the important components added or remove. In
addition, we may perceive real life images without prior warning of t heir
content, but images must be purposefully constructed and based on our
knowledge of the subjects being captured. Thus, two people may form an
image of the same position in chess, but the professional player will "see"
the offensive and defensive relati onships in the image that the non -player
or novice Beginners will not "see", because experts already have the
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26 Pylyshyn (1973, 1981, 2002) has always argued for a modal propositional
representations as the basis of visual experie nce and argued that visual
experience has no real causal role in perceived visual experience, but
rather is what is known as an "epigenetic phenomenon". A practical
analogy is that the hum of a running washing machine is an epigenetic
phenomenon, i.e. a by -product, of the machine's operation, but does not
contribute to its operation. Likewise, Pylyshyn suggests that visual
experience is a by -product of basic cognitive processes, but has no real
functional role.
2.8 AMBIGUITY OF IMAGES
The famous Necker cub e and the Duck Rabbit figure (Jastrow, 1899) are
good examples of indistinct reversible shapes that often produce interlaced
and even interlaced structures. In Necker's cube, perception alternates
between a cube whose front face is on the right or left sid e, and in Duck
Rabbit, perception alternates between a duck turned sideways and a rabbit
turned sideways. Gestalt's cognitive theory proposes that ambiguous
figures cause unstable representations to be resolved into alternative
representations.


Figure 2 .5: Jastrow’s Duck -Rabbit Ambiguous (reversible)
Image source: Google images


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27

Figure 2.6: Necker cube: an ambiguous (reversible) figure
Image source: Google images
If the image is the same as the perception, then the image of characters
such as the duck and rabbit should also be ambiguous and reversible. To
investigate this possibility, Chambers and Reisberg (1985) showed their
participants a line -drawing version of a duck rabbit for 5 seconds and
asked them to visualize it for a later drawing task. All of the participants
said they saw him as a duck or a rabbit (but not both). They were then
shown other indistinct images and showed how these inversions changed
the focus of attention. The participants were then asked to imagine the
appearance of a du ck and rabbit and find alternative explanations for their
image. Finally, the children drew a picture of a rabbit and a duck and
reported how they felt about the drawing. It was found that although the
participants could easily reinterpret their drawings, that is, when they saw
a rabbit, they had drawn turn into a duck and vice versa, they were unable
to reverse the image. mental image of a duck (in a rabbit) or a rabbit (in a
duck), which they built at the beginning of the experiment. This supports
the ide a that paintings are not exactly the same as objects but always have
a fixed interpretation based on them. Similar results were reported later by
Chambers and Reisberg (1992). In this second study, participants learned
that a duck -and-rabbit figure was a d uck or rabbit, and then mimicked that
shape. After being tested with numbers comparing small differences from
baseline, participants were told that duck characters were more sensitive to
differences in the beak/ears of the image than to changes at the nose /back
of the duck's head. The opposite pattern is that of a rabbit.
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28 Chambers and Reisberg argue that in interpreting an image and shaping an
organism, one is primarily concerned with the face, and for the
interpretation of ducks the face is on the left ( beak) and for rabbit's right
face. Similar results showing difficulties in image reinterpretation were
also reported by Pylyshyn (2002). However, under certain circumstances,
when multiple clues and clues are provided, Mast and Kosslyn (2002) find
a visual inversion with a stimulus of looking like a young woman in a
certain way. direction and an old woman if turned 90 degrees outward. It
seems that inverting the image is possible sometimes, but usually very
difficult.
2.8.1 Neuropsychology /Neuroscience of imagery
If images are cognitive reconstructions, one would expect brain regions
known to be involved in perception to also be involved in images. Several
studies have examined this question. Roland and Friberg (1985) found
significant activating effects in the occipital lobes (which are highly
associated with visual perception), indexed by blood flow measurements,
when participants performed the image exercise. Farah et al. (1988b)
found similar results for visual images with a range of neuroscience
measur es, including event potentials. Zatorre et al. (1996) found similar
effects with auditory imaging. The formation and use of the auditory
image of the song has a secondary auditory cortex activation effect similar
to, but weaker, the activation acquired by listening to the song.
In related studies, Kosslyn et al. (1995) asked participants to form images
of different sizes and found not only increased occipital activation, but
also specific occipital region activation depending on the size of the image
being imaged. Fort. Ganis et al. (2004) compared the results of fMRI
when people took pictures of the numbers and actually saw the numbers.
This detailed comparison indicates that although similar brain regions are
involved in perceptual and visual task versions , the regions most activated
in imaging (occipital and temporal regions) are a reduced set of regions
activated during perceptual imaging. This is consistent with the fact that
people rarely confuse image with perception, with the exception of certain
pathologies.
Overall, a large number of studies, reviewed by Kosslyn and Thompson
(2003), often detect early involvement of the visual cortex in imaging
tasks, especially when the images are detailed.
Although there is a wealth of neuroscientific evidence that images and
cognition share brain mechanisms as suggested by the regenerative theory,
several neuropsychological studies have found instances in which people
with brain damage, visual perception is intact but vision is impaired.
'Pictures and others have i mages intact but lack visual perception.
(Bartolomeo, 2002). These cases of double dissociation support the idea
that although cognitive and visual brain regions overlap, they are not the
same.
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29 2.9 SUMMARY
Unlike many other psychological constructs, forg etting as well remains a
common phenomenon in our day -to-day life. It's so natural for individuals
to often forget or experience the feeling where one fails to recall any
information that is in fact, available with us.
Forgetting is caused due to many fact ors such:
Interference - different memories interfering with one another.
Decay - After a long time passes, a lot of information will be forgotten and
memory will fade.
Failure to recall - As the information never really makes it to the long -term
memory pro perly.
Even though there are factors that may cause an individual to forget
certain information or memory, there are instances where individuals
forget or try to forget purposely. Such as Retrieval - induced forgetting
(RIF), Directed forgetting & Think/n o-think (TNT).
Since, forgetting is also connected with memory one must also take into
consideration the various methods to understand memory such as
flashbulb memory I.e., a vivid memory of a dramatic experience.
Eyewitness memory, is like an episodic mem ory mostly something that a
person has witnessed.
Lastly, there are concepts which tend to be understood well with the help
of imagery, isn't it easy to simply recall the shape or sound of an object
that you read or thought of like we took an example of “ CAT” and the
Brooks Imagery task.
Forgetting, images and perceptions and the regions of the brains are
connected with these tasks and experiments that we have discussed above.
2.10 QUESTIONS
a. What are functional approaches to forgetting
b. Explain the term s “flashbulb” & “eyewitness” with reference to
memory
c. What is the neuroscience behind imagery
d. What is the visuo -spatial process in imagery?
e. What are the factors due to which forgetting takes place?
2.11 REFERENCES
 Gilhooly, K.; Lyddy,F.&Pollick F. (2014). Cognitive Psychology,
McGraw Hill Education

 Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
Laboratory. (5th ed.). Sage Publications (Indian reprint 2015) munotes.in

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Cognitive Psychology
30  Matlin, M.W. (2013). Cognitive Psychology, 8thed., international
student version, John Wiley & sons

 Solso, R.L., Maclin, O.H., & Maclin, M.K. (2013). Cognitive
Psychology. Pearson education, New Delhi, first Indian reprint 2014

 Ashcraft, M. H. &. Radvansky, G. A. (2009). Cognition. (5th ed),
Prentice Hall, Pearson education

 https://www.google.com/search?q=brook%27s+imagery+task&client=
safari&hl=en -
us&prmd=ismvn&sxsrf=ALiCzsYfZiCnswvYVpazeGdwH1R_VQXU
Q:1651993649358&source=lnms&tbm=isch&sa=X&ved=2ahUKEwi
onZD1q8_3AhU1LqYKHWfpDvkQ_AUoAXoECAIQAQ&biw=414
&bih=712&dpr =2#imgrc=1XpQWfzCLrhWWM

 https://www.google.com/search?q=mental+scanning+kosslyn&client=
safari&hl=en -
us&prmd=nisv&sxsrf=ALiCzsYqGtJFG5W7lClD80Yl7QbE6BlCQ:1
651993922043&source=lnms&tbm=isch&sa=X&ved=2ahUKEwjZsp
P3rM_3AhVRyosBHfbQ C5kQ_AUoAnoECAIQAg&biw=414&bih=
712&dpr=2#imgrc=AISt_JIRzcfzrM

 https://www.google.com/search?q=mental+scanning+kosslyn&client=
safari&hl=en -
us&prmd=nisv&sxsrf=ALiCzsYqGtJFG5W7lClD80Yl7QbE6BlCQ:1
651993922043 &source=lnms&tbm=isch&sa=X&ved=2ahUKEwjZsp
P3rM_3AhVRyosBHfbQC5kQ_AUoAnoECAIQAg&biw=414&bih=
712&dpr=2#imgrc=YMMfy2gOlOSC4M&imgdii= DkF7gOUFf4vuT M

 https://www.google.com/sear ch?q=rat+lever+pressing+experiment+im
ages&tbm=isch&ved=2ahUKEwjVw5v4rM_3AhWug2MGHZ8eDpk
Q2cCegQIABAC&oq=rat+lever+pressing+experiment+images&gs_lc
p=ChJtb2JpbGUtZ3dzLXdpei1pbWcQAzIECB4QCjoKCCMQ7wMQ
6gIQJzoHCCMQ7wMQJzoICAAQgAQQsQM6BAgAEEM6BwgAE
LEDEEM6CwgAEIAEE LEDEIMBOggIABCxAxCDAToFCAAQgA
Q6BggAEAUQHjoGCAAQCBAeOgQIABAYOgQIABAeOgQIABA
NOgQIIRAKUNFDWIyOAWCjjwFoAnAAeACAAaoCiAHIKJIBBj
AuMzMuM5gBAKABAbABBcABAQ&sclient=mobile -gws-wiz-
img&ei=RG13YtWREK6HjuMPn724yAk&bih=712&biw=414&clien
t=safari&prmd=nisv&hl=en -
us#imgrc=T 5b0Fq1UTPVbIM&imgdii=i6mHuhZEClgRDM



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31 3
PROBLEM SOLVING - I

Unit Structure
3.0 Objectives
3.1 Introduction
3.2 Creative Process
3.2.1 Creativity and Functional Fixedness
3.3 Problem and Problem Types
3.3.1 Problem Types
3.3.2 Advisory Problem
3.3.3 Non -Advisory Problem
3.4 Brief History and Ba ckground
3.4.1 Gestalt Psychology
3.5 Information Processing Approach
3.6 Summary
3.7 Questions
3.8 Reference
3.0 OBJECTIVES
 To understand the field of cognitive psychology and problem solving
 To understand the types of problem solving
 To understand the history & background of problem solving
 To understand in brief how creativity and thinking are related to
problem solving.
3.1 INTRODUCTION
Broadly speaking psychology is concerned with both humans as well as
animals with regard to the ir mind and behav iour. Specifically, the branch
of cognitive psychology focuses on the cognition , that is the working of
mind and how it represents with the information that we collect from our
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Cognitive Psychology
32 In our everyday life we collect a lot of information and use it very well.
But some days are different such as when we encounter circumstances that
are different than usual for example, when we misinterpret certain
information or miscalculate some of our decisions. Various cognitive
functions are used on a day -to-day basis for all of us from thinking,
memory, perception etc. In this chapter we will focus on one of the
cognitive processes called “problem solving”.
Each one of us at some point in our daily life encounters some so rt of
problem . We all have confronted some problem, which is naturally not so
easy to solve and also quite complex in nature. An individual might even
think of a few solutions but the difficulty is to choose the most appropriate
solutionthat not only solves the problem but also with less time or
resources.
While studying cognitive psychology and problem solving, we are trying
to answer questions such as what is the mental processing going on while
an individual chooses to solve a problem in the first place, what are the
options they are thinking a bout? How do es an individual process the
various solutions? What kind of mental tactics are used etc.
In our daily lives we face many problems which vary with the degree of
difficulty . In case of some problems we may not think much and
processing is also spontaneous and happens in a fraction of second for
example, taking staircase if you just missed getting inside a lift or catching
another train if you missed your usual one .But the same problems could be
very severe if you have a leg injury. The amount of information
processing will be more and you have to choose your options wisely.
Examining the area of problem solving helps us understand and answer
the questions asked above. Perhaps, you must have noticed that problem
solving is in a way thinking; wheth er it’s about the actual problem or its
solutions. Also, by answering the questions above we do not gain a
complete explanation on the cognitive structure or process involved in
actual problem solving.
Therefore, going forward we will understand thinking along with problem
solving, nature of problem and types of problems and also look at the
historical background.

Source: Gilhooly et al , 2014
Before we move on to exploring various types of problems, let us also see
how creat ivity is linked to problem solving.
Creativity is a cognitive process that involves something novel or out of
the box sort of perspective on a construct or in our case , we must say , on a Problem is a situatio n in which you have a goal but do not know how
to achieve it.
Thinking is a process of mental exploration of possible actions and munotes.in

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33 problem. The more the person is creative the more innovative the sol utions
can be.
3.2 CREATIVE PROCESS
There is a lack of a unanimous theoretical backup in the field of creativity .
One of the reasons is the actual nature of the topic which makes it difficult
to measure and has lead torelatively less attention from the re search
community. In spite of th ese gaps, creativity has recently become an
important area of study when it comes to psychology and understanding
the everyday applications of the same.
Wallas (1926) described the creative process as having four sequentia l
stages:
a. Preparation. Formulating the problem and making initial attempts
to solve it.
b. Incubation. Leaving the problem while considering other things.
c. Illumination . Achieving insight to the problem.
d. Verification . Testing and/or carrying out the solution.
3.2.1Creativity and functional fixedness
German psychologist Karl Duncker in 1945, described functional
fixedness as a mental block when using an object in a new way that is
required to solve a problem.
Functional fixedness is an obstacle to both creativit y and problem -solving
which sort of indicates how both the topics have a connection . When a
person cannot think of an alternative way of using a product or item ,
functional fixedness not only hinders the problem -solving process but it
also becomes a challe nge for creative thinking pattern as it blocks the “out
of the box” strategy.

3.3 PROBLEM AND PROBLEM TYPES
As discussed earlier, problem solving is, in a way thinking and in a
general context we can conclude that thinking is free floating. Unless we
are trying to look for some solutions to a problem or maybe when we are
about to make some important decision. When we indulge in similar
cognitive activities the process of thinking is more directed towards the
end goal or soluti on.
One question that is in fact quite obvious for us to ask is what is a
“problem” after all? Be it an animal or an individual who, when faced with
something that clearly states the end goal but not the road to the goal can Creativity is the a bility to generate, create, or discover new ideas,
solutions, and possibilities.
Functional fixedness is a mental block when using an object in a new way
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34 be termed as a problem. Every problem in fact has a solution and also
many options to choose from, not only that but problems can vary in the
degree of difficulty, time period, and many other factors could be
accountable in describing the nature and type of the problem.
3.3.1 Problem type’s
We can categorize the problems in various sections based on some
characteristics so one can group them together. Let's look at a few
categories of problem, starting with well -defined and ill -defined problem
which depends on how clear the problem is. Also, depending on whether a
problem needs a specific understanding of the subject matter or not, than
we can also categorizeit as knowledge -rich and knowledge -lean. Putting a
problem under one roof of category based on various factors helps not
only in g etting a more in -depth understanding , but also helps further
research on similar problems. Well-defined problemis a problem in which starting conditions, actions
available and goals are all completely specified.
Ill-defined problem is a problem in which st arting conditions, or actions
available or goals are not completely specified.
Knowledge -rich problems are problems that require extensive ly
speciali zed knowledge.
Knowledge -Lean problems are problems such as puzzles that do not
require specialist knowledg e.

With the help of above descriptions, we can see that problems that are well
defined have a structure to them, the characteristics of such a problem
includes the initial state or problem situation, rules/ strategies for solving
the problem and also the goal state also known as the solution to a
problem. For example, a mathematical problem such as “8 -2” in which
initial state (the problem statement and other information provided), steps
to solve that problem (mathematical rules) are clearly defined. One a lso
knows if correct solution has be obtained or not. On the other hand, if a
professor asks you to come up with an exciting theme for college festival,
as there are no defined steps to solve the problem, and even the initial state
isn’t objectively specif ied (“exciting’ can have multiple interpretations),it
is an example of ill defined problem.
Apart from the above -mentioned categories, problems can also be
classified as advisory and non -advisory problems.
3.3.2 Adversary problem
When you are solving a problem from a game , let'ssay, chess , where you
have an opponent who se goal is to stop or create barriers in the thinking
process of the solver , you are facing an adversary problem . Games like
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35 3.3.3 Non -Adversary problem
Unlike the adv ersary problem, in the case of non -adversary problem we do
not have an opponent which can be called as rational thinker or even
some one who thinks, the opponent here is immobile and is not making
attempts to disturb or trouble the thinking of the solver. Some examples of
advisory problems would be anagram puzzles or even some computer
programs. Non-adversary problems are problems in which the solver is dealing
with inert problem materials with no rational opponent.
Adversary problems are problems in which the solver has to deal with a
rational opponent such as in board games.
Source: Gilhooly et al., 2014
We further move to knowledge rich problems and knowledge lean
problems . As the name goes any problem t hat requires more in depth
understanding or specialized understanding of the procedures or rules is
called as the knowledge rich problem. On the other hand, we have the
knowledge lean problems which do not require the solver to have any
specific knowledge of any procedure or subject any one can solve such
problems. Some examples of knowledge rich problems could be medical
diagnosis, legal decision making etc . Whereas, e xamples of knowledge
lean problems include crossword puzzles, spot – the- difference pict ures
etc.
Lastly, we come to some problems that are based on the time - period
which are known as large scale and small -scale problems. The ones that
take longer periods to be achieved or completed such as building dams, or
writing a book . On the other han d, whose tasks that take less time or
efforts in solving such as, simple decision making like choosing the attire
for an event will be termed as small -scale problems.
It is crucial that research is generated with respect to all of these various
types of p roblem . However,research has taken place with few types like
non-advisory, knowledge lean, small -scale problems, well defined
problems. Let us see why these types are chosen over the others for
research.
Non- advisory problems are good to start with since we do not have to get
into the complexity of understanding and anticipating the opponents
thinking and game plan.Knowledge lean problems or puzzles are the best
since they do not need any specific understanding or degree of a particular
area, which makes the population pool of the participants way more to
choose from compared to a specific participate or sample pool. As most
participants will be able to solve a knowledge lean puzzle the response
increases giving the researcher an opportunity to generalize the
findings.Well - define d problems just like the knowledge lean ones have
more accessibility for majority of participants as the problem can be
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36 Further, s mall- scale problems fit the cr iteria of a lab study or research as
it requires much less time and it can be studied well with many
participants who just have to spare a few minutes for the response.
3.4 BRIEF HISTORY AND BACKGROUD
Humans have been called as the rational animals; a spe cies who has
intellectual capability like no other. If we turn around and see our history,
our evolution tells us how humans have been faced with numerous
problems such as language, learning, differentiating between causes for an
occurrence etc. Humans hav e also learnt to solve these problems with a
verity of alternatives and solutions. Using the principals of deductive and
inductive reasoning, insight and even trial and error methods.
Also, when we say humans are a species with intellect, meaning we can
put to use the principles of thinking or creativity and logic depending upon
the growth, exposure or simple the nature of the problem; the individual
may use all or some combinations of these strategies.
Apart from humans, animals too indulge in many proble m-solving
strategies from finding food to protecting themselves .Animal s use rule
learning - a process of discovering logical rule from the available
information or to gradually acquire the knowledge for sustaining in
different kinds of circumstances. In pas t, we have seen in psychological
experiments how animals use their problem -solving skills, some animals
even go further in using complex problem -solving abilities.
3.4.1 Gestalt psychology
Gestalt is a German word which means “configuration” or something
that’s brought together in such a way that it forms a whole picture or
object.
Gestalt psychology is a school of thought that views behaviour, cognition
or any worldly element as a whole. When we observe something, we do
not look at components of a system or structure separately but we see it as
a whole or complete object. For eg., when your friend comes in front of
you and you look at their face, you do not perceive his nose, eyes, cheeks
separately, instead you combine it together and recognize it as a w hole -
face of your friend. Therefore, Gestalt psychology is also known as
“Holism” or defined as “The whole is greater than the sum of its parts”.
One of the founding members of Gestalt Psychology - Max Wertheimer ,
was on a train during his vacations when the movement of the train got
him curious, upon which he explored the concept of perception. With this
began the work of gestalt psychology in the early 20th century.
Wertheimer did his experiments with stationary objects and showed
howwhen same objects shown rapidly, they are perceived as moving - a
phenomenon called as ‘optical illusion’ or what Wertheimer termed as
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37 Along with Max Wertheimer, Wolfgang Köhler & Kurt Koffka are major
contributors in the work & research in the field of g estalt psychology.
Wolfgang Köhler’s most popular experiment was with the apes about
insight learning at Island of Tenerife on the Canary Islands .IN his
experiment, Köhler hung some fruits up from the ground , and gave the
four chimpanzees two sticks and th ree boxes to help them retrieve the
fruits. After trying a few times the apes took some time to think and with a
few tri als they used the boxes to climb up and grab the fruit. They used a
popular strategy for problem solving known as insight.
At the begin ning of the chapter we discussed how animals use their
problem -solving abilities when it comes to livelihood .The experiments
done by Köhler explain how those apes could get a solution that came to
them suddenly like it happens with insight learning and or with trial -and-
error methods. The results of the experiments suggests that there is a
correlation between the intellect and the development of the brain, which
is quite similar with apes like humans.

3.5 INFORMATION PROCCESSIN G APPROACH
Back in the mid 19th century, the information processing approach was
highlighted once again and the reason behind the same was a new
programme that was developed with computers.
These digital computers could be programmed to solve problems . We
know that computers can solve calculations, or anything that is numerical,
for example; employee data, financial stats etc. But coming from cognitive
psychology perspectives, what is more interesting is that computers could
also solve problems that do no t involve numbers. Many chess players play
against digital computers, and if not to perfection but some computer
programs can identify or diagnose illness based on the symptoms that we
enter . Computers can also do simple language translations.
The fact tha t computers use similar techniques to solve problems as
humans is quite interesting and pa ves way for further study. Humans for
example , use available information to identify the problem well, use older
memories or information to get access to finding solu tions and also use
decision making techniques to choose a solution and apply the solution to
the problem .If you compare , computers use similar princip les when
triggered with problem solving. Box 1.1 Wolfgang Köhler Experiment with Apes
Köhler had put a Chimpanzee named Sultan, in a cage along with two
bamboo sticks .Some bananas were k ept outside the cage but Sultan
could not reach them as it is. The individual sticks too were not so long
to help grab the fruit . The only way was to put together the sticks to
reach the bananas. Sultan tried a few times but failed and accidentally
joined the sticks together making a one long stick out of it which allowed
him to get the bananas.
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38 Let's take an example of anagram to understand how computers and
humans solve problems.
Use the letters “ UDYTS ” to make a word . Let's try with using last two
letters S and T and try to build a few letters and then see if it fits all the
letters given in the anagram . We could solve this because we are familiar
with the word “STUDY” .Thisshows that we use strategies such as use of
long-term memory, retrieval of memory etc. When solving problems. In
the same way, a computer will access the stored words, earlier used words
from its programme memory to access information and find the correct
answer, which in this case is “ STUDY ”.
Programs that are similar to human thinking patterns are called as
Simulation, which is different from a common method used by machines
while mimicking the intelligence or thinking of human beings, p opularly
known as Artificial Intelligence.
Coming back to information processing in problem solving, lets discuss
some key elements:
Problem space
Which can be defined as an abstract or commonly used graph and line
format to represent the possible state of problem. This simply includes all
the information that we know about the problem.
Problem space has two sub types : State - action space and Goal -subgoal
space
State – action space
Which is a representation of changes of the problem in various states i.e .;
initial state – intermediate state – goal state. Series of operations can be
performed to transit from one state into another. This process is often
pictured with the help of a tree diagram.
A very famous example that demonstrates this action space is the game of
noughts and crosses (tic -tac-toe). Imagine that you are player one who has
put X in one of the boxes. Your opponent has eight possible moves to
make for each possible first move that you make. Once he plays, you now
have multiple number of move s out of which one move can be played, so
on and so forth.
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39

Figure 3.1: Sample tree diagram for noughts and cross problem.
Image source: Gilhooly 2014
There are three methods by which state action tree can be searched
systematically:
1. Depth first search : Considering only one move at a time. Although this
is an easy method as it doesn't load heavily on memory, this method
may not always guarantee attainment of goal
2. Breadth first search: This involves considering each possible move at
each stage and adding it to the tree. This is an algorithm move that
always guarantees attainment of goal.
3. Progressive deepening: This method is combination of first two
methods. It considers only limited number of moves in depth, backs it
up then searches for other alternati ve moves which are then stated in
depth. This continues till all the branches have been searched in
limited depth. If the goal is not attained then the depth level is
deepened.
In a method known as ‘hill climbing’ intermediate states are also stated
and p roblem solver works towards achieving them before achieving the
final state.
Goal – subgoal space
Which is a representation that shows how problem goal can be broken into
parts such as the sub goals and sub - subgoals. An everyday example of
this would b e, if I want to reach from my home in Kalyan to Vileparle, I
can break it down into sub goals of taking rikshaw from my home to
Kalyan station, then another sub goal of taking train to Dadar station,
followed by final sub goal of taking train from Dadar to Vileparle, thereby
achieving the final goal. The sub goals are often hierarchical where one
step can be done only after a step before that is completed.
One of the very famous example of solving problem by using sub goals
strategy is the problem of tower of Hanoi. The game consists of three pegs
and a few disks of different sizes. All the disks are put randomly in left munotes.in

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40 most peg. The task is to move disks from left peg to right peg while
making sure larger disk is never placed above a smaller disk. The pro blem
can be solved following step by step procedure.

Figure 3.2 : Tower of Hanoi problem. Image source:
Gilhooly et al., 2014
Information processing approach can be very helpful with well -defined
problem and also those that can represent the search thro ugh problem
spaces. Unfortunately, though, not all problems are well – defined and the
initial formulation is not adequate to solve the problem . Thisis where the
need for Insight comes in.
3.6 SUMMARY
 Problem solving is thinking oriented towards solving a s pecific
problem that involves both the construction of answers and the choice
of possible answers. We come across countless problems in our daily
life that make us develop reaction, strategies, select possible responses,
and test responses to solve a probl em. For example, try to solve this
problem: a dog has a 6 -foot rope tied around his neck and a bowl of
water is 3 feet away. How would the dog get to the pan? Solving this
problem involves generating possible answers (of which there are
few), choosing and testing them, and perhaps discovering the trick of
the problem.
 Problems can be classified in different categories such as well -defined
problems (those with clear initial state, goal and steps to achieve the
goals), ill -definedproblems(those with unclear p roblem space, steps
and goal), adversary problems (involving an opponent that tries to add
obstacles in the process of problem solving) , non -adversary problems
(no opponent involved), etc.
 Gestalt psychology and information processing approach are some of
the important perspectives on problem solving. Gestalt Psychology
advocates looking at the problem as a whole made up of
interconnected parts instead of looking at parts separately. The strategy
of insight problem solving comes from this perspective
 Information processing model compares human problem solver to a
computer and draws parallels between human cognition and computer
algorithm to solve problems. munotes.in

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Problem Solving - I
41
 Problem space is defined as an abstract or commonly used graph and
line format to represent the poss ible state of problem . It includes two
important types: State action space and Goal -subgoal space
 State action space represents changes of the problem in various states
i.e.; initial state – intermediate state – goal state. Series of operations
can be per formed to transit from one state into another.
 Goal - action pace represents how problem goal can be broken into
parts such as the sub goals and sub - subgoals
3.7 QUESTIONS
1. Explain the phenomenon of functional fixedness with examples.
2. Describe in detail t he information processing Approach
3. What is the role of Gestalt school in of thought in problem solving
4. Describe different types of problems
3.8 REFERENCES
 Gilhooly, K.; Lyddy,F.& Pollick F. (2014). Cognitive Psychology,
McGraw Hill Education

 Galotti, K. M. (2014). Cognitive Psychology: In and Out of the
Laboratory. (5th ed.). Sage Publications (Indian reprint 2015)

 Matlin, M.W. (2013). Cognitive Psychology, 8thed., international
student version, John Wiley & sons

 Solso, R.L., Maclin, O.H., & Maclin, M.K . (2013). Cognitive
Psychology. Pearson education, New Delhi, first Indian reprint 2014

 Ashcraft, M. H. &. Radvansky, G. A. (2009). Cognition. (5th ed),
Prentice Hall, Pearson education


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42 4
PROBLEM SOLVING - II
Unit Structure
4.0 Objectives
4.1 Insight problem solving
4.1.1 Comparing insight and non -insight
4.1.2 Neuroscience approach to insight versus non -insight tasks
4.1.3 Think aloud effects on insight versus non -insight problems
4.2 Recent theories of insight
4.2.1 Representational change:
4.2.2 Progress monitoring:
4.3 Knowledge rich (expert) problem solving:
4.3.1 Knowledge base
4.3.2 Memory base
4.3.3 Problem solving strategy
4.3.4 Speed and accuracy
4.3.5 Metacognition
4.4 Creative problem solving
4.4.1 What is a creative approach?
4.4.2 Divergent Production
4.4.3 Creative theory of Investment
4.4.4 Intrinsic motivation -based creativity
4.4.5 Extrinsic motivation -based creativity
4.5 Summary
4.6 Questions
4.7 Reference

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43 4.0 OBJECTI VES
 To understand the role of insight in problem solving
 To understand the recent theories of insight
 To understand the various strategies in problem solving
 To understand the concept of creative problem solving
4.1 INSIGHT REVISITED
The information p rocessing technique suggests solving problems by
looking at the problem as it is which isby either utilizing state-action space
search methods or goal-sub goal strategies. However, we have little
understanding of how problems can be solved by looking at the m in an
alternative way ie., insight . However, there has recently been a
development of interest in understanding insight difficulties, as first
advocated by the Gestalt school
Non-insight problems can be solved within the initial representation;
insight problems, on the other hand, necessitate a modification in the
initial representation in order to be solved . Let us look at some insight
problems:
 How would you arrange six matches to make four equilateral
triangles?
 In one month, a man married 10 differe nt women. All of the women
are still alive and are still married. There was no violation of any anti -
polygamy laws. How is it possible?
The initial representation in both of these cases needs to be rebuilt. In
Problem 1, there is a strong temptation to wor k in two dimensions, but the
answer necessitates the use of three dimensions to construct a little
pyramid with one triangle at the base and three triangles on the sides. In
Problem 2, the word “married” is commonly misinterpreted as 'got
married to,' but it should be reinterpreted as 'causes to get married,'
implying that the guy has the authority to perform marriage rituals.
4.1.1 Comparing Insight and Non -Insight
The subject of whether distinctions in processes between insight and Non
insight problem so lving can be established experimentally is a key focus
of research. The Gestalt viewpoint holds that insight issue solving requires
a specific 'restructuring' process, whereas some researchers argue that
insight problem solving emerges through conventional search and problem
analysis processes without the requirement for unique or uncommon
processes.
Ratings on feelings, regarding how close the solver is to the solution and
how confident they feel about solving the problem when they first hear it,
are one w ay for addressing the question of whether distinctive processes
are involved in insight tasks vs non -insight jobs. Metcalfe and Weibe munotes.in

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44 (1987) contrasted insight and non -insight tasks and discovered that the
'Feeling of Knowing One Could Solve' measured at t he outset was a better
predictor (correlation with solution = 0.4) for non -insight tasks than
insight tasks (correlation = 0.08).
Below the figure shows the 'feeling of warmth' (i.e. how close one felt to
solution) per 15 seconds during solving, which sho wed a consistent
increase in feeling near solution with non -insight problems but no increase
in warmth with insight tasks until the solution was reported. This finding
backs with the notion of abrupt restructuring in insight tasks.
Figure 4.1 Ratings of warmth for insight vs. non -insight difficulties. For
algebra, there is a continuous increase in ‘warmth,' whereas for insight
problems, there is a rapid jump in warmth shortly before the solution.
Image source: Gilhooly et al., 2014
4.1.2 Neuroscience Appr oach to Insight versus Non -Insight Tasks
In a 2004 study conducted by Jung -Beeman et al , researchers employed
functional magnetic resonance imaging (fMRI) and electroencep
halography (EEG) to see if there were any variations in brain activity
patterns betw een insight and non -insight problem solving. A total of 124
Remote Associate Test (RAT) items were employed in the investigation.
People must select a word that is an associate of three test words in this
task, such as ' What term connects the words "boot, " "summer," and
"ground"?
To differentiate between insight vs. non -insight solving, the researchers
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45 after each item. A self -reported insight solution was one in which
participants ha d a "Aha!" moment and we re confident that the solution
was right. A methodical approach of trying out one association after
another on each thing until an association that fit all three items was found
could lead to non -insight solutions.
When insight solu tions were compared to non -insight solutions, fMRI
showed greater activity in one specific brain area, the right anterior
superior temporal gyrus. Shortly before the solution, EEG recordings
revealed a spike in activity in the same location. These findings show that
different brain areas are involved in insight and non -insight solutions The
findings are support ed by a study which found that priming words given to
the right hemisphere resulted in more insight solutions in RAT tasks than
priming words receiv ed to the left.
4.1.3 Think Aloud Effects on Insight versus Non -Insight Problems
Another investigation into the differences between insight and non -insight
problem solving looked into the probable effects of thinking aloud on
insight and non -insight tasks. Schooler et al (1993) had participants do
three insight issues and four non -insight tasks while thinking aloud or not.
Thinking aloud lead to poorer performance on insight but not non -insight
tasks, according to the findings. This was regarded as supporti ng the
theory that insight tasks entail uniqueunconscious processes that are
difficult to articulate. However, other researchers have failed to duplicate
this finding, thereby calling into question its validity . Because the insight
problems in the Schooler et al. study were mostly spatial, Gilhooly et al.
pointed out that there was a confounding between insight tasks and spatial
tasks in the Schooler et al. study, and that the apparent negative effect of
thinking aloud on insight tasks was due to thinking a loud disturbs with
spatial tasks. Because thinking aloud necessitates the re -coding of spatial
thoughts into a verbal form for reporting, this interference occurs. Overall,
the evidence for distinct unconscious mechanisms in insight solving from
think alou d experiments is relatively modest.

Figure 4.3 : fMRI results for insight and non -insight problem solving .
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46 4.2 RECENT THEORIES OF INSIGHT
The empirical distinction between insight and non -insight problem solving
appears to be well established based on the findings discussed above.
However, the question of how to theoretically describe insight solving
remains open. Recently, two major approaches have emerged:
representational transformation and progress monitoring, sometimes
known as "the criterion for satisfactory progress theory."
4.2.1 Representational change:
As previously stated, Gestalt accounts of insight processes like
reorganisation were hazy. Researcher Ohlsson (1992) has given a more
specific description of insight in information processing terms in his
representational shift theory.
The following are the main stages and processes in representational
change theory:
 Perception of the issue: The problem is encoded by a person.

 Solving problems: Initial r epresentation -based heuristic search
procedures. These procedures use long -term memory to retrieve
possible actions or operators that modify the present state of the
problem into new states.

 Impasse : When it comes to insight tasks, the initial represent ation is
deceptive and prevents a solution. As a result, people are stuck in
impasse when they have a blank mind and can't think of any more
steps to try.

 Restructuring: Elaboration, re -encoding, or constraint relaxation are
used to create a new encodin g. Elaboration is the process of adding
information to an initial representation by identifying elements that
were previously overlooked. Instead of simply adding new features,
re-encoding involves totally modifying the encoding. For example: In
the proble m of the marrying man, changing the understanding of the
word "married" causes the problem to be re -encoded. Relaxing limits
on what is necessary in the objective or what actions are permissible is
referred to as constraint relaxation. In the nine -dot prob lem, removing
the limitation to operate within the square shape is an example of this
procedure. These reorganisation processes, according to Ohlsson,
occur outside of consciousness and involve autonomic processes such
as spreading activation.

 A partial u nderstanding: Following restructuring, the retrieval of
possible actions breaks the impasse and leads to a series of steps that
lead to a solution.

 Detailed information/Full insight: Following restructuring, retrieval of
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47 the solution that the solution can be expected within a limited mental
look-ahead.
Matchstick algebra problems were used to investigate the representational
change theory. An inaccurate Roman numeral equation is pres ented in
these exercises, and the participant's duty is to move one match to rectify
the equation.

Figure 4.1: Problem with matchstick algebra. To make this equation
correct, reposition one match.
Image source: Gilhooly et al., 2014
When we think of equa tions, we usually think of altering numerical values
but not operators (+, =). These issues necessitate re -encoding, which
entails breaking apart and rearranging groups of matches that form
conceptual units or 'chunks.' More harder issues necessitate a rel axation of
the limits on equation form.

Figure 4.2 Constraint relaxation is required in a matchstick algebra issue.
To make this equation correct, reposition one match.
Image source: Gilhooly et al., 2014
Knoblich and his colleagues. discovered that re -encoding chunks in the
first problem, such as changing VII to VI and II to III (correct answer for
matchstick problem 1: (VI=III+III) by relocating one match, was easier
than easing the constraint on the normal form of equations in the second
problem, such a s changing IV = IV + IV to IV = IV = IV.
Overall, the matchstick algebra problem solving experiments offered
support to the representational change hypothesis, but further study is
needed to evaluate how well the theory would expand to a wide range of
other issue areas.
4.2.2 Progress Monitoring:
The 'progress monitoring hypothesis' created by MacGregor et al., (2001)
is an alternative to representational change theory. Using incorrect
heuristics, according to this theory, is the main source of difficulties in
insight tasks. They advise that people track their progress against some
criterion while they look for steps that will assist them attain a solution.
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48 restructuring. The theory can be described using the nine -dot issue as an
illustration of how they used their technique.
Traditional explanations for the nine -dot task's difficulty propose a
fixation (set) on the square shape, excluding other possibilities.
Instructions to search outsid e the square, on the other hand, were shown to
be ineffective (Lung and Dominowski 1985 ) suggested other ineffective
limitations, such as believing that all lines begin and end with dots.
A different explanation including two important points was proposed by
the progress monitoring theory.
These are (1) the use of a maximisation heuristic, in which each move or
decision is an attempt to make as much progress as possible toward the
goal, and (2) the use of progress monitoring, in which the rate of progress
is constantly assessed, and criterion failure occurs if it is deemed to be too
slow and inefficient. A different tactic could then be pursued.
When applied to the nine -dot task, progress monitoring theory suggests
that (1) the maximisation heuristic would be for each move to cover as
many new dots as possible, and (2) progress monitoring would involve
comparing the rate of progress to the number of dots required to be
covered per line to solve, and criterion failure would occur if no move met
the criterion. A different tactic could then be pursued (e.g. extending
lines).
MacGregor et al. investigated the progress monitoring theory explanation
of the nine -dot task by administering two versions of the problem, version
A and version B, to participants.


Figur e 4.3: Nine -dot issue with a twist (version A). A variation of the
nine-dot issue that includes a tip to think outside the box. Participants task
is to connect all the dots using 4 straight lines and without lifting their
hand
Image source: Gilhooly et al. , 2014
If 'constraint relaxation' is all that's required to imagine 'beyond the box,'
then participants should perform better on version A than B, because
version A depicts a line that extends beyond the box. If criterion failure is
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49 to cover fewer dots in the next two steps, allowing them to see that they
are on the wrong track sooner. According to MacGregor and colleague s ,
just 31% of those given version A were successful, wherea s 53% of those
given version B were correct.

Figure 4.4. The nine -dot problem has been tweaked even more (version B).
There's also a suggestion to use the diagonal.
Image source: Gilhooly et al., 2014
More experiments on progress monitoring theory employe d coin
manipulation issues like the eight -coin problem shown below, in which
users must move only two coins so that each coin touches exactly three
others.


Figure 4.5: The eight -coin problem is presented in two different ways.
Participants task is to o nly move two coins to ensure that each coin is in
contact with at least three other others.
Image source: Gilhooly et al., 2014
If the approach is just to attain a short -term goal of getting one coin into
touch with three others, then the upper form of the issue has 'no move
available,' whereas the bottom version has 20 moves available. As a result,
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50 resulting in more solutions. In the lower version, a lot of time and effort
would be spent pursuing what appear to be possibly correct options that
would finally fail to produce a solution. In the upper form, 92 percent
solved the problem, compared to 67 percent in the lesser version, as
predicted by the hypothesis. The answer of the problem sh own in the
below given figure.

Figure 4.6: The answer to the eight -coin Problem
Image source: Gilhooly et al., 2014
Overall, the core argument of progress monitoring theory is that when
constraint relaxation occurs after criterion failure, insigh t is most likely to
emerge. The research mentioned above provides strong evidence for this.
As a result, the theory does a good job of explaining why people change
their strategies, but it's less clear how new strategies are really
implemented.
4.3 KNOWLED GE RICH (EXPERT) PROBLEM
SOLVING:
Most intellectual clinicians indicate that it requires around ten years of
extraordinary practice to acquire skill in a particular field, which has to be
the primary interest area of the individual. One cannot expect to have
expertise in other areas than the primary interest area. For example, if an
individual’s primary interest is in singing, then he cannot expect him to
expertise in designing or in dance.
Interestingly, e xperts' contrast from new learners during a few pe riods of
problem solving. We'll start with a portion of the benefits that work in the
beginning stages of problem solving, investigate contrasts in critical
thinking systems, and lastly think about more broad capacities, like
metacognition.
Beginners and s pecialists contrast considerably in their insight base, or
constructions .A researcher found in her investigation ofcritical thinking in
physical science that the learners just needed significant information about
the standards of physical science. you need the suitable constructions to
comprehend a theme appropriately. Specialists might perform particularly
well in the event that they have had preparing in an assortment of
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51 4.3.1 Knowledge base
The knowledge bases or schemas of novices an d experts differ
significantly. For example, In the mathematical problem solving, the
novices simply lack important knowledge about mathematical principles.
In order to properly understand a topic and solve problems , an individual
must have the necessary s chemas. Experts who have received training in a
variety of relevant settings may perform especially well.
4.3.2 Memory base
Experts differ from novices in their ability to remember information
related to their field of expertise. Expert’s memory skills ar e often very
specific. Expert chess players, for example, have a much better memory
for various chess positions than novices. Chess experts, according to one
estimate, can recall approximately 50,000 "chunks," or familiar
arrangements of chess pieces. Surp risingly, chess experts are only
marginally better than novices at remembering random chess piece
arrangements. In other words, experts' memory is significantly better only
when the chess arrangement fits into a specific schema. This better
memory could be result of years of practice they have had in the field.
4.3.3 Problem Solving Strategy
Experts are more likely than novices to use the means ends heuristic
effectively when confronted with a novel problem in their field. That is,
they divide a problem i nto several subproblems or sub goals that must be
solved in a specific order. The analogy approach is also used differently
by experts and novices. Experts are more likely to emphasize structural
similarity between problems when solving Physics problems. S urface
similarities, on the other hand, are more likely to distract novices.
4.3.4 Speed and Aaccuracy
Experts, as one might expect, solve problems much faster and more
precisely than novices. Their operations become more automatic, and a
specific stimulus situation also prompts a response. Experts may be able to
solve problems faster on some tasks because they use parallel processing
rather than serial processing. Parallel processing deals with two or more
items at the same time. Serial processing, on the other hand, only deals
with one item at a time. One study discovered that experts frequently
solved anagrams in less than 2 seconds. These experts typically solved the
anagrams so quickly that they must have been considering several
alternative solutions a t the same time. The novices, on the other hand,
solved the anagrams so slowly that they were most likely using serial
processing.
4.3.5 Metacognition
Experts are better than novices at monitoring their problem solving. for
example, they appear to be bette r at judging the difficulty of a problem
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52 Furthermore, when they realize they have made a mistake, they can
recover relatively quickly. Experts are unquestionably more skilled at
various stag es of problem solving, as well as in monitoring their progress
while working on a problem. Experts, on the other hand, perform poorly
on one metacognitive task. That is, e xperts, in particular, underestimate
the amount of time that novices will need to sol ve a problem in the
expert's area of specialisation. In contrast, novices are more accurate in
predicting that the problem will be difficult to solve.
4.4 CREATIVE PROBLEM SOLVING
Creative problem solving is a method for approaching a problem or a
challe nge in a novel way. The procedure aids in the redefining of issues
and opportunities in order to generate fresh reactions and solutions.
4.4.1 What is a Creative Approach?
We say, it is about taking a creative approach to problem solving. What
exactly does that imply? Simply said, an approach is how you approach,
advance, or get closer to something. An approach is a method of bringing
about change in the context of this book. There are at least two types of
approaches to bringing about change: creative and non-creative
techniques. A creative method means that you are striving to reach a
novel, unstructured, and open -ended result. Frequently, these situations
entail an unstructured problem with unclear solutions , popularly known as
ill defined problems . Becau se there is no ready -made solution and you
must use your knowledge and abilities to evaluate, a creative strategy
encourages you to employ your imagination as well as your brains during
your approach. It also necessitates a more comprehensive strategy that
incorporates the complete system of people, technique, content, and
context.
Taking a creative approach also entails having a brave mentality, which
includes being open to new experiences, accepting ambiguity, and
stepping into new and unexpected territor y. Because creative techniques
are about assisting you in moving from a location you are familiar to one
that is different and maybe unknown, and the outcomes of your efforts are
potentially unclear, this attitude is frequently required.
4.4.2 Divergent P roduction
Many current scholars also emphasizethat, rather than a single best
answer, creativity necessitates varied thinking.
There are moderate relationships between people's creativity test scores
and other judgments of their creativity, according to research on divergent
production tests. The quantity of diverse ideas, on the other hand, may not
be the best indicator of originality. After all, this criterion does not
consider whether the answers fit the three criteria for creativity which is
novelty, high quality, and utility.
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53 4.4.3 Creative theory of Investment
Some experts proposed that those who work in the arena of ideas purchase
low and sell high as well. That is, when no one else is interested in the
"investment," they come up with a creative ide a. They move on to a new
creative undertaking later, once the idea has gained traction. The famous
shark tank show has shown us several example of an entrepreneur coming
up with least explored idea and making it a big hit
What are the characteristics of th ese intelligent and creative investors? The
key elements of creativity, according to Sternberg and Lubart investment
theory, are intelligence, knowledge, motivation, a supportive environment,
a suitable thinking style, and an appropriate personality. You'l l need all six
of these qualities to work productively. Consider a person who meets five
of the criteria but has a low intellect level. This person is unlikely to
develop anything innovative.
It's worth noting that the investment approach to creativity als o
emphasi zes issues outside the individual's control. Individuals may have
creative qualities. However, they will not be creative in the workplace if
they do not work in a supportive environment.
The investment hypothesis of creativity is interesting in and of itself,
especially because it stresses the complex criteria for creative success.
Let's look at one of the six requirements now: motivation. As you'll see,
certain types of motivation are more likely to boost creativity than others.
4.4.4 Intrinsic Motivation -Based Creativity
People are more likely to be creative when they are working on a task that
they truly enjoy, according to research. In one study, Ruscio and his co -
authors gave college students a standardi zed test of intrinsic drive. The
partic ipants were asked to score their interest in three types of activities
:writing, painting, and problem solving.
The students returned to the laboratory a few weeks later, where they were
instructed to complete activities in these three categories.The pupi ls'
innovative projects were then judged by a panel of experts. The findings
revealed that students who scored high on the standardi zed exam for
intrinsic motivation were more likely to develop a creative project.
4.4.5 Extrinsic motivation -based creativit y
Many studies have shown that when students are working on projects for
external causes, they create less creative output. People's extrinsic
motivation is high when they see an activity as merely a way of receiving
a reward, a good grade, or a positive appraisal. Since their intrinsic
motivation is frequently diminished , as a result, their creativity is likely to
decrease.
When college students were told that their poemswould be judged by a
committee of professional poets, they generated fewer imaginativ e poetry,
according to representative research. Other studies backup these
conclusions. The same effect is frequently observed in both adults and
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54 For many years, scholars had held a straightf orward viewpoint. intrinsic
motivation is good, while external motivation is bad. You've undoubtedly
spent enough time studying psychology to reali ze that no conclusion in
our field can be that simple. According to a more extensive examination,
creativity can really be boosted if extrinsic sources supply relevant
information.
Extrinsic motivation, on the other hand, hinders creativity by controlling
and limiting your possibilities. The ramifications of these discoveries for
education and the workplace are significant: Encourage people to work on
projects that they enjoy, and use an external compensation scheme that
does not detract from their creative efforts.
To show what we mean when we say "creative methods to problem
solving," consider the following exa mples.
 Actively developing a wide range of options and identifying the most
intriguing ones to investigate further. Maintaining a cheerful mindset
while being open to many various alternatives. Solving future -oriented
issues that do not yet exist.

 Consi dering facts, impressions, feelings, and ideas from a variety of
perspectives. Willingness to delve deeper into assumptions.

 Considering the topic or situation from a variety of perspectives. Being
able to experiment with other options.

 Developing a lar ge number of diverse and uncommon ideas with the
ability to solve the problem or fulfil the challenge in a novel and
beneficial way. Being able to come up with ideas and defer judgement
as necessary. Possessing the ability to generate ideas.

 Putting time , effort, and talent into moulding, refining, and developing
a wild or highly unconventional notion into a viable solution.
Persistence is a virtue.

 Taking into account components of the situation surrounding the
solution in order for others to agree with your solutions. Being aware
of the situation and the individuals who may be affected by your
solution, as well as attempting to gain support and approval.
4.5 SUMMARY
Often, we experience a sudden discovery of a solution to a problem which
could have been a difficult task. Individuals tend to experience insights
very suddenly without any preparation or even trying; which is why
probably we also know insights as solutions coming from trial -and-error
method.
Insights are those solutions that we experience sp ontaneously, and it
occurs even more naturally as we face problems that we have also solved
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55 Insight vs non -Insight problem
As discussed above a problem that is designed in such a way that it
promotes the “A -ha” feeling whic h makes you reali ze the solution, while
you also learn to solve similar problems and the insights become more
frequent due to the past experience. The Non - insight problem on the other
hand, are those problems that are designed in such a way that one nay n ot
experience insights since, to solve the on -insight problems one must use
some well -defined methods, or look for solutions that come from some
systematic process or knowledge.
We must also take into consideration the neural activity associated with
insights, also factors such as type of problem, innovation or creativity in
solving the problem and the role of intrinsic and extrinsic motivation in
creatively solving the problem.
Experts differ from novices in problem solving in several ways such as
they h ave vast knowledge base, better memory, better meta cognition, etc.
Creative problem -solving approach involves solving problems in novel
and original way. It requires divergent thinking and is often inspired by
intrinsic motivation. Extrinsic motivation ma y help problem solver not get
discouraged or distract when solving problems.
4.6 QUESTIONS
1. What are insightful and non -insightful problems?
2. Explain the role of creativity in problem solving
3. What is creative theory of investment
4.7 REFERENCES
 Gilhooly, K.; Lyddy,F.&Pollick F. (2014). Cognitive Psychology,
McGraw Hill Education

 Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
Laboratory. (5thed.). Sage Publications (Indian reprint 2015)

 Matlin, M.W. (2013). Cognitive Psychology, 8thed., in ternational
student version, John Wiley & sons

 Solso, R.L., Maclin, O.H., & Maclin, M.K. (2013). Cognitive
Psychology. Pearson education, New Delhi, first Indian reprint 2014

 Ashcraft, M. H. &. Radvansky, G. A. (2009). Cognition. (5th ed),
Prentice Hall, Pearson education


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56 5
DECISION MAKING - I
Unit Structure
5.0 Objective
5.1 Introduction
5.1.1 What is Decision making?
5.1.2 Introduction to theoretical models of Decision Making
5.2 Expected Value Theory
5.3 Utility and Prospect Theory
5.4 Subjective Probability and Prospect Theory
5.4.1 Framing Effect
5.5 Making Probability Judgements
5.5.1 What is probability?
5.5.2 Heuristics, Mental Shortcuts!
5.5.3 Availability
5.5.4 Representativeness
5.6 The Affect Heuristic
5.7 Summary
5.8 Questions
5.9 References
5.0 OBJECTIVES
 The chapter here explains what do we mean by de cision -making and
different phases of decision -making.

 Understanding different theoretical models of decision making

 To learn one of the oldest and most famous theory of decision making -
Expected Utility Theory

 Understanding Prospect theory of Decision Making in relation to
utility and subjective probability

 Understanding how the concept of probability is related to decision
making and how it leads to judgments in decision making

 It explains the various heuristics that we use while making decisions

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57 5.1 INTRODUCTION
You are a final -year graduation student trying to find your way through
life. The next task on your academic agenda is to find a suitable college
from where you can pursue your Masters/Post Graduation. But there are so
many colleges that on e has to choose from and the decision you make will
have an impact on your life. Now what cognitive processes you might put
to use in this situation to evaluate your options? Cognitive psychologists
use the term “decision making” to refer to the mental act ivities that take
place in choosing among alternatives.
In day to day life we tend to face many situations where we have to take
certain decisions ranging from the not very important, such as which
clothes to wear today, to the moderately important, such a s where to go on
a vacation, to the very important, like what career to choose. We could say
that decisions are a type of problem in which the alternatives are set out
and the problem is to choose the best alternative available. This becomes
easy, for exam ple if the choice is between different amounts of money,
most people would readily choose the larger amount. However, if the
options are complicated and have uncertain consequences, for example
deciding what career to choose or which job offer to take, the decision
may be very difficult and have no clear correct solution. Typically,
difficult decisions require a lot of thinking to figure out the possible results
of different choices and so decision making is a complex cognitive
activity.
5.1.1 What Is Deci sion Making?
In simpler words, decision making is a cognitive process of selecting a
belief or a course of action among several possible alternative options. Out
of the number of given available alternatives to us we then make a
choice through judgment and reasoning. This choice which we make is
according to our needs and requirements and this choice process is
actually a very risky process. The reason being that once we make a
choice, once a number of options are available to us and we make a choice
there are always a chance that the choice will backfire, meaning which
that if there are five different options or five different conclusions which
can be drawn from a particular mental representation or five different
interpretations of a particular mental repr esentation.Choosing one
representation over the other basically puts us into a situation where if the
choice that we are making is wrong we could have a wrong decision and
that could harm us in some or the other way.
So, decision making is a very complex process. In decision making we
need to make choices under different situations. Another interesting thing
that we should remember is that most of the times these choices have to be
made in uncertainty or with a certain amount of risk. Considering the fact
that human beings are not calculating machines and don’t have all the
information available for making a best choice out of the number of
options which have been given to us. So, we tend to make these selections
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58 uncertainty, in a state of risk and so, we tend to minimize this risk or make
decisions which do not backfire on us.
We know that decisions are frequently made under uncertain conditions,
some do not produce the desired results, ev en when made carefully and
after thorough, unbiased consideration of the evidence. Psychologists
generally argue that the "goodness" of decision making cannot be
measured by the success of individual decisions —for example, luck
frequently plays an undue ro le. Instead, the rationality of the decision is
frequently used as a yardstick of success. This term is defined differently
by different people, but von Winterfeldt and Edwards (1986a) provide a
common definition: Rational decision making "entails selectin g ways of
thinking and acting to serve your ends, goals, or moral imperatives,
whatever they may be, as far as the environment allows." Gathering
information as methodically and fairly as feasible under the circumstances
is also part of rational decision -making. It necessitates examining both
evidence that supports and evidence that contradicts your first
inclinations.If you go out to buy a new mobile phone and choose one that
looks good in your hands but ignore other factors such as operating
system, relia bility, and software availability, you are undermining your
own decision -making.
According to Kathleen M. Galotti, decision making can be divided
into five main phases.
 Setting Goals
 Gathering information and Making Plans
 Structuring the decision
 Making a final choice
 Evaluating a decision
As shown in the following Figure 5.1, there is a particular order in which
these phases usually occur. However, this order is not necessarily be
followed in every decision -making process. Sometimes, you may have to
revisi t and redo certain phases which make this procedure cyclical as
shown by the arrows. Also, some of the phases can be skipped and
performed in a different order rather than performing in a set order

Figure 5.1 Phases of Decision Making munotes.in

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59 {Source: Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
Laboratory. (5thed.). Sage Publications (Indian reprint 2015).}
5.1.2 Theoretical Models of Decision Making:
It will be interesting to know howdo we make decisions, ranging from less
important like whic h dress to wear today to moderately important like
where to go for a vacation to a very important decision like which career
to choose. Are there any ideal ways to decide which would always lead to
the best answer? For centuries, these questions have been of great interest
to a wide range of researchers in different disciplines. Economists,
philosophers, mathematicians, and, more recently, psychologists,all have
attempted to answer the question of how to make the best decision.
Economists, philosophers, and mathematicians have focused on ideal
decision -making methods and, as we will see, have devised methods for
making the best choices in small -scale, well -defined decision tasks, such
as simple gambles. The normative approach refers to the search for good
ways to make decisions. Psychologists, on the other hand, take a
descriptive approach, attempting to understand what people actually do
rather than what they should ideally do. As we will see, normative
approaches provided ideas that were then used in descri ptive
theories. Economists are beginning to develop behavioural -economics
theories based on descriptive theories that make more realistic
assumptions about human thinking. As a result, there has been
considerable interaction between descriptive and normati ve approaches.
There is also an approach known as prescriptive approach.
 Normative approac h- It attempts to establish norms i.e.ideal ways of
deciding that will give the best decision possible. It defines ideal
performance under ideal circumstances. Econom ists tend to develop
normative models.Normative approach of decision making is also
known as the classical theory of decision making.

 Prescriptive approach - Ittell us how we “ought” to make decisions .
They take into account the fact that circumstances in which decisions
are made are rarely ideal, and they provide guidance about how to do
the best we can. Teachers try to get students to follow prescriptive
models.

 Descriptive approach - It aims to describehow decisions are taken
against how they should be made. Psychologists tend to focus on the
descriptive approach.
In this unit, we will study some theoretical models of decision making
namely - Expected Value Theory and Prospect Theory. While learning
these approaches we will understand the concepts of risk in decision
making, utility , subjective probability and prospect theory. This unit also
deals with how we make probability judgments and how people use
heuristics to take a mental shortcut while making decisions. So, before we
go ahead and study theoret ical models of decision making, let us first
understand the following concepts:
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60 Decision problems may differ from each other in several ways. One major
difference between different decision problems is that some decision
problems involve risk as against those that are risk less.
 If there is a probability that one of the options could lead to negative
outcomes for the decision -maker, we say that the decision problem
involves risk.

 Risk less decisions involve choices wher e the outcomes of the choices
are known with certainty. Thus, the negative outcomes can be
minimized by choosing the appropriate alternative.
If you decide to bet that a particular team will win a match that is a risky
decision. Because the result of the b et is unknown when you make this
decision. In such a situation the chance that you win or lose are almost
similar. On the other hand, deciding which coloured shirt to wear is risk
less. If you choose the blue shirt then that is what you will be wearing and
you won'tloose anything.
Single Attribute and Multi -Attribute alternatives -
Decision making becomes more difficult and complicated when there are
many alternatives and these alternatives differ from each other based on
different attributes ( Multi -attrib utes) as against those objects which vary
in only one way (single attribute). For e.g. when you have to choose
which tie to wear, you might have to select from different ties which may
be identical except for colors. Here there is only one attribute of the object
i.e. colour. Multi -attribute decision problem is a decision task in which the
alternatives vary in many dimensions or aspects. For e.g. when you have
to buy a new mobile phone you might have to consider different aspects in
order to select one like operating system, size, weight, colour, camera
quality etc.
5.2 EXPECTED VALUE THEORY
While looking for ways to avoid risky gambles, mathematicians Blaise
Pascal (1623 –62) and Pierre de Fermat (1601 –65) proposed that people
should act to increase the ex pected value of choices. What does this mean?
The expected value of a risky choice is the average result you would get if
you repeated the action many times . For example, if a lottery ticket had an
85% chance of winning 100 rupees, its expected value woul d be 0.85 ×
100 rupees, that is 85 rupees (an average). If you can repeatedly take the
same risk (i.e. your lottery ticket is valid every week and has the same
chance of winning), you would get nothing sometimes (15 per cent of the
time) and you would get 100 Rs the rest of the time (85% of the time). So
a long -term average over all the purchases is 85 rupees. Looking at this
example using the expected value model, you should be willing to buy the
lottery ticket for any price under 85 rupees as it would mea n you would
profit overall (even if it is only a small profit). Even buying the ticket for
84.99 rupees would be considered rational because you would make
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61 The expected value approach is one of the best ways to deal w ith risky
decisions, for example in situations where we can put a money value on
the possible outcomes and can say exactly what the probabilities of the
possible outcomes are.
Can the expected value model predict people’s real life behaviour?
Research sugg ests it does not. Kahneman and Tversky (1984) with similar
decisions to the lottery ticket case found that people’s choices showed
differences from the expected value model predictions. Many participants
made choices that made them poorer. If they had all followed the expected
value approach, most would have been richer at the end of the experiment
than when they started.
Real life shows a different scenario from what expected value model
would predict. For example, why do most of us get insurance? To stay in
business the insurance companies gives in claim payments less than they
take through charges to customers. Overall the average customer must
lose, that is paying in more than they get back. So, from the expected
value point of view people should not tak e out insurance. Overall, these
examples make it clear that the simple expected value model does not fit
actual behaviour very well. Further theories highlighting subjective
probabilities and subjective measures of value (utility) have been
developed to ov ercome the problems related to expected value theory and
provide better explanations.
5.3 UTILITY AND PROSPECT THEORY
Utility -
The concept of utility versus objective value has a lengthy history, dating
at least as far back as the eighteenth -century mathem atician Bernoulli
(1738). Utility is the subjective value of an option. The subjective value or
utility of a given additional quantity of money diminishes the more money
you already have, according to utility theory. A plot of utility vs money
should theor etically show diminishing returns. Figure 5.2 depicts our
intuition that an additional 100 Rs is more valuable to a poor individual
than it is to a billionaire.

Figure 5.2 - Plot of utility versus money. This figure shows diminishing
growth of utility of extra wealth as wealth grows.
{Source: Gilhooly, K., Lyddy, F., &Pollick, F. (2014). EBOOK: Cognitive
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62 Let’s understand utility by using this example - A poor person might cross
a busy road to pick up a 100 Rs note, whereas a rich p erson would not,
because the 100 Rs has vastly different utility for the rich and the poor.
Prospect Theory -
Prospect theory was developed by psychologists Daniel Kahneman and
Amos Tversky, originally published in 1979 in Econometrica. To
overcome issues w ith the expected value approach, Kahneman and
Tversky developed prospect theory. The theory explains how people make
decisions about which gambles (or 'prospects') to take and, more crucially,
it extends the utility plot into the realm of losses.Prospect t heory is a
theory of decision -making stressing relative gains and losses.
Loss aversion is a key idea of prospect theory that there is a greater dislike
of losing utility than liking for gaining the same degree of utility. Prospect
theory, also known as Lo ss-Aversion theory, is a theory of decision -
making under conditions of risk. The model has been imported into a
number of fields and has been used to analyse various aspects of political
decision -making, especially in international relations. The theory wa s
mainly based on human decision making while handling financial
prospects relating to betting / gambling. Prospect theory assumes that
individuals make decisions based on expectations of loss or gain from
their current position. As shown in the figure 5.3 , the S shaped curve
shows a steep fall with losses and more gradual growth with gains

Figure 5.3 - Schematic plot of gains and losses versus utility according
to prospect theory.
{Source: Gilhooly, K., Lyddy, F., &Pollick, F. (2014). EBOOK: Cognitive
Psychology . McGraw Hill.}

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63 5.4 SUBJECTIVE PROBABILITY AND PROSPECT
THEORY
Subjective and Objective Probability -
Depending on the nature of calculation or determination, there are two
types of Probabilities .
-The one that uses personal opinion is known as Subject ive Probability
-While the one that uses history and data is known as Objective
Probability.
Of course, probability estimates can differ from one person to another or
from one time to the next. For example, when someone is in a bad mood,
his/her estimate s of the likelihood of success in one of his/her ventures are
much lower than when he/she is happier. Optimistic people always seem
to find successful outcomes more probable than do pessimistic people.
Subjective probabilities are influenced by characteris tics of the probability
estimator whereas objective probabilities are not. They are based on facts.
Of course, in many real -life circumstances, there may be no objective
probabilities available.
Prospect theory addresses the issue of probability as well. B oth objective
values and known objective probabilities were assumed in the expected
value model.
As we have seen, prospect theory replaces objective values with subjective
values or utilities. It also proposes that people's perceptions of probability
devia te from objective values on a regular basis. Kahneman and Tversky
(1979), in particular, proposed that objective probabilities be transformed
into subjective probabilities known as 'decision weights.' As shown in
Figure 5.4, people tend to overestimate sma ll probabilities and
underestimate large probabilities. This figure shows that decision weights
(the solid line) are overweight low probabilities and underweight high
probabilities. The dotted line shows what would happen if the decision
weights become equ al to the objective probabilities.

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64 {Source: Gilhooly, K., Lyddy, F., &Pollick, F. (2014). EBOOK: Cognitive
Psychology . McGraw Hill.}
Phases of prospect theory -
Prospect theory contains two phases:
(1) An e diting phase - The editing phase refers to the way in which
individuals characterize options for choice. Usually, these are referred to
as framing effects.
(2) An evaluation phase -People tend to act as though they would make a
decision based on the possibl e outcomes and choose the option with the
most utility during the evaluation phase. Each prospect's outcomes are
measured and compared using statistical analysis at this phase. The value
function and the weighting function are two indices that are used to
compare prospects throughout the evaluation process.
5.5.1 Framing Effects -
Framing effects highlight the way in which someone’s choice can be
affected by the order, method, or wording in which the matter is presented
i.e. how the matter is framed. An exa mple of this effect took place in the
Asian disease paradigm in which people were asked to make a choice
among public policy plans for responding to a disease outbreak. Even
though the actual probabilities were identical, the percentage of people
supportin g a plan changed based on whether or not the outcomes were
presented in terms of the number of people who would live versus the
number of people who would die. We will see this example in detail in
following discussion.
Let’s take simple everyday life exa mple. Suppose, you are a health -
conscious person . You went to the grocery shop for buying a yogurt. On
the rack you see two different sachets of yogurt. On first sachet it is
written “contains 20% fats”. On another one it is written “80% fat free”.
What w ill the immediate choice in this situation? Most people would
choose second option. But if you really pay attention both the yogurt
contains same amount of fats. This is how framing effect, how the
problem is worded/ presented has effect on decision making .
People evaluate outcomes in terms of changes from a reference point
(which is their current state). They perceive certain outcomes as gains or
losses depending on how their current state is described. As a result, the
description is said to "frame" the d ecision, or to provide context for it. We
have seen in previous cognitive topics (such as perception, thinking, and
reasoning) that context effects can have a significant impact on cognitive
performance. In essence, framing effects are similar to context e ffects in
decision making.
Another example includes patients suffering from cancer choosing
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65 choices are greatly based on whether the outcome was presented in terms
of survival rate or mor tality rate.
In the evaluation phase people tend to show aversion of loss and there are
certain factors at play - which are as follows.
Certainty effect : People tend to value certainty over outcomes that are
merely probable. Tversky and Kahneman (1986) use d the following
examples to demonstrate the certainty effect -Consider the following
scenario:
Which option do you prefer out of the following?
A. a guaranteed profit of Rs. 300.
B. An 80% chance of winning Rs 450 and a 20% chance of winning
nothing.
Option A was chosen by 78 percent of participants, whereas option B was
chosen by only 22 percent. As the projected value of option B (Rs 450
x0.8=Rs 360) exceeds that of option A by 20%, this exhibits the classic
risk-aversion phenomenon in prospect theory and the framing effect.
Reflective effect : When it comes to positive gains, people give more
importance to a small but certain profit/gain over a larger but probable
gain. Although, when it comes to negative gains, people show risk -seeking
behaviour. For examp le, people prefer a loss that is probable over a small
loss that is certain. This seems to contradict people’s desire for safety and
insurance, but it is for moderate losses, rather than severe losses.
Related research:
Tversky and Kahneman wanted to study different impacts of framing
decisions in terms of potential losses as against potential gains. For this
research they created a scenario where people would have to decide how
to deal with an imaginary Asian disease. The options for treating the
disease c ould be framed and presented in terms of gains (lives saved) or
losses (lives lost). Prospect theory predicts that these different ways of
presenting the alternatives would have an impact on the choices made, so
that a risky option would be preferred when the choices were among
losses and a sure option would be preferred when the choices were among
gains. People were asked the following:
The first problem asks, if programme A is adopted, 200 people will be
saved. If programme B is adopted, there is a one th ird probability that 600
people will be saved and a two -thirds probability that no people will be
saved. Then the participants were asked to choose one of these two
options. Now the second problem poses these questions: if Programme C
is adopted, 400 peopl e will die. If Programme D is adopted, there is a one -
third probability that nobody will die and a two -thirds probability that 600
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66 The results show that in the first problem people preferred program A over
program B. Whereas in the second problem there was a strong preference
for program D over program C. The researchers further explained that in
Problem 1, participants are inclining towards a positive ‘gains’ frame, i.e.
in terms of lives saved. Whereas, i n the second problem the participants
were working in a ‘losses’ frame, i.e. in terms of lives lost.
5.5 MAKING PROBABILITY JUDGEMENTS
You must be aware that most difficult decisions are made under uncertain
conditions. Deciding on a major would be a very easy and simple task if
you knew in advance how each of the available alternatives would lead to
your life . In such an ideal situation, you would simply go through all the
outcomes and choose the alternative that led to the outcome that fulfils the
need of your decision -making and which you most prefer. However,
people rarely have such an ideal decision -making environment. Mostly,
real-life decisions are based on uncertainty and risks.Thus, such real -life
decisions rely on estimating the chances of differen t outcomes of different
alternatives . To understand how people do this, first, it is necessary to
understand some concepts related to probability and uncertainty.
5.5.1 What is Probability?
In the light of uncertainty and risk involved in decision making, anoth er
important concept becomes necessary to understand while we are studying
decision making and that is probability. As per Von Winterfeldt&
Edwards, Probability can generally be thought of as a measurement of a
degree of uncertainty. Probability is a mathe matical concept that is
represented by a number between 0 and 1, where 0 represents complete
certainty that an event will not happen, and 1 represents complete certainty
that it will. Intermediate values can be thought of as corresponding to
intermediate l evels of confidence that an event will occur. Someone who
says the probability of an event to be .15 is saying that the probability of
the event taking place is very low.
Application of Probability Theory in Decision Making -
Probability is the branch of m athematics concerned with the assessment
and analysis of uncertainty. The theory of probability provides the means
to rationality model,analyze and solve problems where future events
cannot be foreseen with certainty. It is difficult to ascertain the resul ts to
be obtained when choosing a course of action. In such cases, if one wants
to act rationally i.e. to maximize the chances of attaining one’s goal — it is
necessary to explicitly deal with the uncertainty created by the problem.
Thus, probability theory is crucial for rational decision -making.
In general, people who are not trained in probability theory will have little
trouble with a probability of 0 or 1. They are not very good at using the
intermediate probabilities in a coherent way. Their use of mid dle numbers
deviates significantly from probability theory, and it's not hard to see why.
What does it mean to say you are 60% sure about something, and how is munotes.in

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67 that different from being 70% sure? What these numbers “mean” in the
context of a real -life deci sion is not at all intuitive and is rather subjective.
If we think ofdefinition of probability, it says it is the number of
favourable events, divided by the total number of events. Let’s take an
example. You’ve surveyed 50 customers to know if they are sa tisfied with
your service. Out of them, 35 said they are happy.
Based on that information alone, can you predict what’s the attitude
(happy) of a random customer? You can make such predictions with
probability:
35/50 = 0.7 which means, there’s a 70% chance that an out -of-sample
customer will have a positive view of the service. That’s probability in the
simplest terms — the likelihood of something happening.
The above example has only 1 outcome — customers’ positive attitude.
However, some customers may dem onstrate other preferences such
as negative or neutral attitudes. For multiple outcomes such as those,
calculating probability is slightly different.
Probability judgments in Decision Making -
While choosing effectively between options the decision -maker h as to
reach judgments about the probability of certain scenarios. For instance, a
business traveller in Mumbai might have to decide whether to travel by
plane to Delhi or take the train. What outcomes might be considered and
the subjective probabilities gi ven to those outcomes will be critical in what
decision is made. Tversky and Kahneman argue that availability heuristic
and representativeness heuristic are most commonly used in making
probability judgments. But what does the heuristic mean? And what is t he
relevance of it in decision making? Let’s understand in detail what
heuristics exactly means and how do availability and representativeness
heuristics.
5.5.2 Heuristics -Mental Shortcuts!
A heuristic is a mental shortcut that allows people to solve problems a nd
make judgments quickly and efficiently. Heuristics are rules -of-thumb that
help to facilitate decision -making based on a limited subset of available
information. As heuristics are based on less information, they are assumed
to make faster decisions than strategies that require more
information.These mental short cuts are generalizations or rules -of-thumb,
reduce cognitive load and can be effective for making immediate
judgments/decisions.
Why and When We Use Heuristics?
Here are a few different theories from psychologists about why we rely on
heuristics.
 Attribute substitution : Simpler but related questions are substituted
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68
 Effort reduction : Heuristics are used by people as a form of cognitive
laziness to reduce t he mental effort required to make choices and
decisions.

 Fast and frugal : People use heuristics since they are quick and
accurate in certain situations. Some theories contend that heuristics are
more accurate than biased.
Here are some conditions which ex plain when we use the heuristics -
 When one is faced with too much information
 When the time to make a decision is limited
 When the decision to be made is relatively less important
 When there is access to very little information to use in making the
decisi on
 When an appropriate heuristic happens to come to mind at the time of
making decision
To deal with the enormous amount of information we encounter and to
speed up the decision -making process, the brain relies on these mental
strategies to simplify thing s so we don't have to spend endless amounts of
time analysing every detail.
Every day, you probably make hundreds, if not thousands, of decisions.
What should you eat for breakfast? What should you wear today? Should
you drive or take the bus? Fortunately, heuristics allow you to make such
decisions with relative ease and without much agonizing.
For example, when deciding whether to drive or take the bus to work, you
may suddenly recall that there is road construction along the bus route.
You are aware that this may cause the bus to stall . Heuristics allow you to
quickly think through all of the possible outcomes and arrive at a solution.
5.5.3 Availability Heuristic
This heuristic, according to Kahneman and Tversky, is a mental shortcut
for making frequency or p robability assessments based on "the ease with
which instances or occurrences can be called to memory." Making
decisions based on how easy it is to recall information (how easily the
information gets available to your memory loop) is known as the
availabil ity heuristic. When making a decision, you may recall a number
of relevant examples fast. Because these are more easily available in your
memory, you are more inclined to assess them as more common or usual.
Because we can call certain memories to mind mor e easily than others, we
apply the availability heuristic. The example Kahneman and Tversky give
for availability heuristic is that when they asked participants if there are
more words in the English language that start with the letter K or have the
third letter K, the majority of them said the former. In reality, the latter is
correct, however coming up with words with K as the third letter is far
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69 scenario, recollections of words beginning with K are more readily
recalled than memories of terms beginning with the third letter K.
Let’s look at another example. If you are planning to travel to Delhi by
flight and are reminded of a number of recent airline mishaps, you may
decide that flying i s too risky and opt to travel by vehicle instead. The
availability heuristic causes you to assume that plane crashes are more
common than they are since those examples of aviation disasters came to
mind so easily.
In one of the study, Ross and Sicoly (1979 ) polled 37 married couples
(husbands and wives separately and independently) on the estimated
extent to which they take the responsibility for various household tasks,
such as cooking, breakfast, grocery shopping, and child care etc. Husbands
and wives we re both more likely than their partners to say they had more
responsibility for 16 of the 20 activities required the presence of a spouse.
Furthermore, when asked to provide specific examples of contributions to
each activity made by themselves and their s pouses, each spouse listed
more of her or his own activities than her or his spouse's activities.
The availability heuristic was used by Ross and Sicoly (1979) to explain
these findings. We are more aware of and accessible to our own efforts
and actions th an we are aware to the efforts and behaviors of others. After
all, while we are always present when we conduct an activity, we may or
may not be there when a friend or spouse does. In general, we have greater
access to what we do, think, say, or intend tha n anyone else, and to the
thoughts, deeds, words and intentions of someone else.
Availability can be a reliable and effective heuristic. If we can be certain
that the ease with which we can build or recall examples is unbiased, it
may be the finest, if not the only, tool we have for gauging frequency or
likelihood. If you're attempting to figure out if you do more papers in
psychology or philosophy, it's generally a good idea to measure the
frequency of papers by recalling individual paper assignments for e ach
subject. There is probably no reason to suppose that psychology articles
are more remembered than philosophy papers in this circumstance. If there
is (for example, you took philosophy three years ago but psychology this
semester), the comparison is mos t likely unfair.
However, using availability to determine which happens more frequently,
hours you spend working on a group project or hours someone else spends
working on the same project, may be unjust. You were present whenever
you worked, but you may n ot have been present at all times when other
members of your group worked. Even if you had been present, you would
have been focused on your own work and planning rather than your
partners' work and preparation. As a result, examples of your own work
are m ore likely to stick with you and be more accessible than examples of
other people's work. So, using availability in such situation won't be
accurate.
The purpose of exhibiting the availability heuristic isn't to intimidate you.
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70 heuristics, the goal is to make recommendation that first consider whether
the range of examples you're considering is indeed adequately available.
5.5.4 Representativeness Heuristics
Making a decision using the representativeness heuristic involves
comparing the current situation to the most representative mental
prototype. When making probability judgments, we frequently rely on this
heuristic. We have a tendency to categorize events, which, as
demonstrated by Kahneman and Tversky, can lead to the use of this
heuristic. When we use the representativeness heuristic, we make
probability judgments about the likelihood that an object or event arises
from a given category based on how similar the object or event is t o the
prototypical example of that category. For example, if we meet someone
in one of our university lectures who look and acts like a stereotypical
medical student, we may judge the likelihood that he/she is studying
medicine, even if there is no hard e vidence to support that assumption.

The research shows that we often use the representativeness heuristic; we
judge that a sample is likely if it is similar to the population from which
this sample was selected. We believe that random -looking outcomes are
more likely than orderly outcomes. Suppose, for example, suppose the
total of your grocery bill comes out to be 374.50 rupees. This very
random -looking outcome is a representative kind of answer, and so it
looks ‘‘normal.’’ However, suppose that the total bill is 444.44 rupees.
This total does not look random, and you might even decide to check the
arithmetic. After all, addition is a process that should yield a random -
looking outcome.
Determinants of Representativeness :
Similarity : When judging the repres entativeness of a new stimulus/event,
people usually pay attention to the degree of similarity between the
stimulus/event and a standard/process.
Randomne ss: Irregularity and local representativeness affect judgments
of randomness. Things that do not appea r to have any logical sequence are
regarded as representative of randomness and thus more likely to occur.
When people rely on representativeness to make judgments, they are
likely to judge wrongly because the fact that something is more
representative doe s not actually make it more likely .
Tversky and Kahneman devised a study of the effects of
representativeness. They presented people first with information about an
imaginary person (Linda) such that the information evoked the stereotype
of a feminist. As it was made clear that Linda fits the description of a
feminist, the researchers were curious to know whether this impression
will make people guess other attributes Linda might have. The description
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71 representative of a bank employee (T). A group of 88 students then ranked
eight further statements about Linda by ‘the degree to which Linda is
similar to the typical member of that group. The description given and the
eight statements were as follo ws: Linda is 31 years old, single, outspoken
and very bright. She majored in philosophy. As a student, she was deeply
concerned with issues of discrimination and social justice, and also
participated in anti -nuclear demonstrations. Now rank the following
statements according to how likely you think they are to be true of Linda.
1) Linda is a teacher in elementary school
2) Linda works in a bookstore and takes Yoga classes
3) Linda is active in the feminist movement (F)
4) Linda is a psychiatric soci al worker
5) Linda is a member of the League of Women Voters
6) Linda is a bank teller (T)
7) Linda is an insurance salesperson
8) Linda is a bank teller and active in the feminist movement (T and F)
Most people opted for describing Linda as F, rat her than T. And to the
researchers surprise more people voted for ‘T and F’ rather than choosing
T or F individually.
Most individuals get this problem wrong, according to Tversky and
Kahneman, since they make use of representativeness: Option 2 appears to
be a more "representative" of Linda based on her description, despite the
fact that it is mathematically less likely.
Conjunction Fallacy: Error in Representativeness Heuristic -
The Conjunction fallacy (also known as the Linda Problem) is a formal
error t hat occurs when distinct/ two different situations are thought to be
more likely than a single generic one. It is an error in decision making
which may appear when one uses representativeness heuristic . When the
probability of conjunction (combined) events is judged to be more likely
than either of its constituents.
A conjunction error occurs when a person rates a combination of two
events as more likely than one of the events alone; the conjunction fallacy
refers to this tendency in general. This distinctio n is essential because a
reasoner can make these errors without having a bias toward making them
in general, just as people can make bets with good expected value in
general but yet lose money on specific bets.
5.6 THE AFFECT HEURISTIC
The Affect heuristic is a type of mental shortcut people use wherein their
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72 (affect) they are feeling. Often when people say they’ve relied on their
“gut-feeling” to make a decision - they are making use of affect heuristics.
So how exactly do our emotions influence our decisions? Well,
researchers have found that people are more likely to perceive high
benefits and low risks in an activity when they are in a positive state of
mind. Hence, they’re more likely to opt “for” the decision than go against
it. On the contrary, when people are experiencing negative emotions such
as anger or sadness - they are more likely to perceive the threats from the
given stimulus and opt to go “against” it.
Affect heuristic invo lves substituting feelings (positive or negative) for
target attributes in decision problems.

Children often instinctively make use of this heuristic while asking for
parent’s approval. Such as the vacation with friends that you had planned -
you have wai ted for your parents to be “in a good mood” till you ask for
their permission to go.
These little incidents aside, many times people often misuse the existence
of affect heuristic to manipulate masses. Such as presenting smoking or
eating junk food as app ealing and positive. In such cases the use of affect
heuristic can lead to long -term negative consequences such as poor health
decisions. Thus even though using heuristics in decision making can be a
quick short term fix - it cannot be a substitute for oth er elaborate
strategies.
How to avoid the error?
You must have heard people saying “Never take an important life decision
when you are too happy or too angry!”. It really is meaningful.
We can begin to avoid the affect heuristic by becoming more conscious of
how our emotions can influence our decisions. Simply being aware of the
fact that we tend to get influenced by our emotions might save us from
poor decision making. When faced with a major decision, we can avoid
using mental shortcuts by thinking logic ally about the decision and
examining all available possibilities.
Being conscious of one's emotional condition is also beneficial in avoiding
the affect heuristic. We can accept that our emotions have the capacity to
influence our decision -making, which c an lead to cognitive errors, if we
can recognize that we are feeling a specific way, such as happy, sad, or
furious.
Finally, if we're faced with a major decision while feeling highly
emotional, whether it's a positive or negative emotion, it's a good idea to
delay making the decision until our emotional state returns to normal. This
will ensure that our decision is not influenced by strong emotions.

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73 5.7 SUMMARY
We have reviewed some of the major concepts and research findings in the
field of decision makin g in this chapter. We got the introduction to the
concept of decision making where we understood what do we mean by
decision making and different phases of decision making.
We saw that decision problems were characterized according to whether
they were ris ky or risk less, or whether they had a single attribute or
multiple attributes.
Decision -making approaches were classified as normative, prescriptive
and descriptive. Normative models attempt to characterize the behaviour
of a rational decision maker i n an ideal world. The descriptive models
explain how decisions are actually taken as against how they should be. In
this unit we learnt about the most famous normative model of decision
making i.e. expected value theory of decision making. As per this theo ry,
people should act to maximize the expected value of a choice. This
approach is an optimal in the risky scenario where we can apply monetary
value on the outcomes of the different alternatives.
However, the expected value maximization model, the simples t normative
model, clearly does not fit individual behaviour. This is due in part to the
fact that the subjective value (utility) of money, for example, is not a
simple linear function of money amounts, and people tend to over -weight
very small probabiliti es while under -weighting high probabilities. We
learnt the concepts of utility (subjective value) and subjective probability
in relation to Prospect Theory. Prospect theory of decision making was
developed to overcome the issues with the expected value the ory. It
stresses the relative gains and losses. The prospect theory approach fits a
lot of the data, including the effects of framing, which lead to violations of
basic rationality principles in the form of certainty and reflective effect.
Since risky deci sions require that decision makers take account of
probabilities, the question of how people handle probability information
has been tackled in a number of studies. Tversky and Kahneman (1974,
1983) have provided many demonstrations of how inappropriate us age of
heuristics such as availability, representativeness and affect can lead to
misjudgements and errors in decision making and how to avoid it.
5.8 QUESTIONS
Q.1.What is Decision Making?
Q.2.What do you mean by normative and descriptive models of decis ion
making? Explain the expected value theory as a normative model.
Q.3. What do you mean by Heuristics in Decision making? Explain the
Availability and representativeness in brief.
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74 Q.5 Write Short notes on -
A) Subjective Probability and Prospect Theory
B) Framing Effect
C) Affect Heuristic
Activity -Take a real -life scenario and explain experience of Availability
and Representative Heuristic in real life.
5.9 REFERENCES

 Galotti, K.M. (2014). Cognitive Psych ology: In and Out of the
Laboratory. (5thed.). Sage Publications (Indian reprint 2015)

 Galotti, K. M. (2007). Decision structuring in important real -life
choices. Psychological science , 18(4), 320 -325.

 Gilhooly, K., Lyddy, F., &Pollick, F. (2014). EBOOK: C ognitive
Psychology . McGraw Hill.

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75 6
DECISION MAKING – II
Unit Structure
6.0 Objective
6.1 Decision Making Process for Multi -Attribute Alternatives
6.1.1 Multi Attribute Utility Theory
6.1.2 Elimination by Aspects
6.1.3 Satisficing
6.1.4 Testing Multi Attribute Decision Models
6.2 Two sy stem Approaches to Decision Making
6.3 Fast and Frugal Heuristic: The adaptive toolbox
6.4 Naturalistic Decision Making
6.4.1 Naturalistic Decision Making and important real -life choices
6.5 Neuroeconomics: Neuroscience Approaches to Decision Making
6.5.1 What is Neuroeconomics?
6.5.2 The link between neuroeconomics and decision making
6.6 Summary
6.7 Questions
6.8 References
6.0 OBJECTIVES
 To explain different decision making processes for multi -attribute
alternatives including MAUT, Satisficing, Elimina tion by aspects.

 Understanding roles of system 1 and system 2 (Two -Systems
approach) in the decision -making process

 Understanding how the heuristics can be helpful and adaptive and not
just ways of making cognitive errors

 To learn naturalistic decision making and real -life application of it

 Understanding the Neuroeconomics and decision making - neural/
brain underpinnings in relation to decision making specifically
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76 6.1 DECISION MAKING PROCESSES F OR MULTI
ATTRIBUTE ALTERNATIVES
The majority of real -life decisions involve choosing between complex
alternatives that differ in a variety of ways. For example, when purchasing
a new laptop, there are numerous functions that vary in quality and ease of
use. What is the operating system? W hat is the resolution of the screen?
What is the storage capacity? How simple would it be to view video on the
screen? Or to read lengthy documents? What is the laptop’s size? How
long does it take to charge a battery? What is your financial budget and
does it matches with the price of the laptop? How long will you be bound
by the contract? And so forth. This is an example of deciding between
alternatives that differ in a variety of ways. How does one reconcile cost
advantages with disadvantages such as bat tery life or screen resolution?
The general issue that many people face to decide between different
attributes of the alternatives.
As we saw in the previous unit, decision making becomes more difficult
and complicated when there are many alternatives and these alternatives
differ from each other based on different attributes (multi -attributes) as
against those objects which vary in only one way (single attribute). As
explained in the above example, multi -attribute decision problem is a
decision task in whi ch the alternatives vary in many dimensions or
aspects .
In the following section we will discuss about what criteria should be used
to make such decisionsAnd how do they appear to be made.
6.1.1 Multi Attribute Utility Theory -
A model that provides a mean s of integrating different dimensions and
goals of a complex decision is called Multi -Attribute Utility Theory
(MAUT). Choosing between items that differ in many ways can be
difficult, even if no risk is involved in choosing the alternatives. Multi -
Attribu te Utility Theory (MAUT) is a normative model.That is, if people
follow MAUT, they will maximize their own utility in the best way for
them to achieve all of their goals.
MAUT involves the following six steps:
(1) Identifying the relevant attributes/ dimensio ns for your decision -
For choosing a specialization for your master’s, you may find out that
difficulty level, appeal, applicability to career, reputation of college, and
past experience as the five attributes that you would like to consider while
takin g the decision.

(2) Determining the relative weights of each dimension - In this the
relative importance of the attributes would have to be considered. Is
applicability to career is more important consideration than past
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77 (3) Listing all the alternativ es- you will list down all the alternatives that
you are thinking as options to choose your majors from like psychology,
political science, economics, philosophy, etc.

(4) Ranking the alternatives along the attributes - In this step, you will
score each altern ative for five attributes that you have identified in the first
step. You have to use same scale length for all attributes (For e.g. 0 -100 or
1-10 etc)

(5) Multiplying the ranking by the weighting of each alternative to
determine its final value - Now you need to multiply weight of a particular
dimension with rank given for that dimension for that option. For
example, You have given weight of 10 to applicability dimension and
college B has received rank of 3 on that. College B’s score for that
dimension then wi ll be 20×3=60. Then you will obtain obtain a total utility
for each object by summing the weighted attribute values.

(6) Choosing the alternative with the highest value - So, you will choose
the one al ternative which scores highest.
This method of decision mak ing is most suitable for situations wherein
consideration of multiple domains or contributing factors needs to be
taken into account.
Let us understand this model in simpler way as explained by Kopp
&Slayter (1984) where they applied MAUT to decision of c hoosing a
career which was created using a computer programme called as Decision
Map.
Figures 6.1 to 6.3 show an example of MAUT applied to a major decision.
Look first at Figure 6.1, which depicts the first two steps in MAUT. It
displays the five dimensio ns listed in the previous paragraph, as well as
any weightings assigned by a specific student. (Once again, weightings
indicate the importance of a given aspect of the decision to the decision
maker.)
-

Figure 6.1 -Weightings of five dimensions in the deci sion “choosing a
major”
{Source: Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
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78 The most important goal for this student is to select a major that is relevant
to future career g oals. Take note that the goal or dimension "applicability
to career" has the highest value and thus the most weight in the graph. The
appeal of the major, its difficulty, and the student's previous record of
success in its courses are the next most importa nt goals for this student.
This student has given the goal "reputation on campus" very little weight,
indicating that it is of little importance. The fact that these weights are
subjective and would differ for different students is significant. Your own
weightings may differ greatly from those shown in this example.
Following the weighting of all dimensions, the decision maker must
consider all alternatives and evaluate them on all of the dimensions
described in the preceding paragraph. Part of this process is depicted in
Figure 6.2, which shows the ranking of various majors on the dimension
"applicability to career." Only four options are presented here: Chemistry,
Psychology, Biology, and Art. Each of these alternatives would need to be
rated on each of th e dimensions identified in the first two steps by the
student. As a result, there would be five graphs of this type, one for each
dimension identified in Figure 6.1.

Figure 6.2 -Assessment of four possible majors on one dimension in the
decision “choosing a major.”
{Source: Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
Laboratory. (5thed.). Sage Publications (Indian reprint 2015).}
The fifth step in the MAUT process is depicted in Figure 6.3: compiling
the assessments of alternatives on all dimensions, as well as the weights of
those dimensions. According to the rankings and weightings provided
earlier, psychology is the best alternative. Figure 6.4 explains why this is
the case: Psychology outranks other alternatives on the dimension
"appli cability to career" but ranks close to the bottom on the other
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79

Figure 6.3 -Final choice of a major
{Source: Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
Laboratory. (5thed.). Sage Publications (Indian reprint 2015).}

Figure 6.4-An analysis of the decision “choosing a major.”
{Source: Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
Laboratory. (5thed.). Sage Publications (Indian reprint 2015).}
To use MAUT in decision making, the dimensions listed must be
indepen dent of one another. For example, the possible dimensions "course
difficulty" and "past grades in course" are presumably related. As a result,
the decision maker must carefully select each dimension. The decision
maker must then be willing to make tradeoff s between the different
dimensions. Although the decision maker in our example is most
concerned with future career goals, MAUT assumes that if an alternative's
relative position on other dimensions was high enough to compensate, the
person would be willin g to choose it.
Though MAUT is one of the ideal way of dealing with a decision problem
when faced with different alternatives having multiple attribute,
unfortunately, little is known about whether people use MAUT on their
own when making important decisio ns, particularly when the information
relevant to the decision is extensive. We will discuss about it further in
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80 6.1.2 Elimination By Aspects
Tversky (1972) described a less demanding procedure than MAUT as a
possible strategy that individua ls could use to reduce cognitive effort or
processing load. This method is referred to as elimination by aspects
(EBA). In an EBA process, the chooser would first choose an attribute and
then eliminate all options that did not meet some level of criterion on that
attribute. In the case of a house purchase, for example, 'price' is usually a
critical factor. The chooser will frequently have established a ceiling price,
and any houses that exceed that ceiling price may be excluded from
consideration (irrespect ive of their other desirable qualities).
If the chooser continues to eliminate alternatives in this manner,
eventually only one option will be left, and the decision will be effectively
made. EBA is clearly a less demanding procedure than MAUT's proposal.
Depending on the order in which aspects are used to eliminate alternatives,
very different choices can emerge. According to Tversky, the importance
or weighting of attributes influences the order of elimination.
MAUT is a normative model, whereas Eliminati on by Aspects (EBA) can
be considered as a descriptive model. It paints a picture of what people do
in real life. It's debatable whether eliminating by aspects is the best way to
make a decision with limited time or memory. It may be entirely rational
in some cases. If an house seeker simply cannot afford a rent above a
certain amount, it makes no sense to waste time looking at houses that are
more expensive, regardless of how well they rate on other dimensions. In
other cases, it may be necessary for decis ion makers to invest the time and
effort in conducting an MAUT analysis. There are various types of
decision aids (including computer -assisted ones) that may be useful.
6.1.3 Satisficing
Simon describes 'satisficing,' a further simplifying technique that c ould be
used in decision making (1956, 1978). The basic idea is that rather than
expending time and effort to maximise utility, most people are content to
set a minimum acceptable level that will satisfy them but fall short of the
maximum. This is especial ly true in the case of sequential decisions. In the
case of buying a house, for example, houses come onto the market on a
regular basis, making it difficult to determine whether a particular house
was the best option because a better one might appear the n ext day.
As a result, buyers can set acceptable levels, either for total utility or for
key aspects of the properties, and choose the first property that meets all of
their minimum requirements. For example, one may only set criteria of
house with X price and in Y locality. The first house that is suggested
which fulfills this criteria is then selected. Should the initial minimum
requirements prove too ambitious, Simon (1978) proposes that the
satisficing level be gradually adjusted in light of market avera ge values, so
that, as a result of experience, the decision maker can become more
realistic about his or her criteria.
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81 6.1.4 Testing Multi Attribute Decision Models
As we pointed out previously, it is interesting to know how people take a
decision when fa ced with a decision problem having different alternatives
having multiple attributes. To figure out which (if any) of the major multi -
attribute choice models are reasonably descriptive of behaviour, one must
be able to deduce how humans process information during decision
making. Payne (1976) did a study relevant to this aspect of decision
making processes and contributed with unique findings which were
beneficial in the research of decision -making processes. In Payne's (1976)
study, when participants had to choose between alternatives having
different attributes, the participants used simple (non -compensatory)
techniques to reduce the number of options, then used compensatory
methods such as MAUT to analyse the remaining few options more
thoroughly. That i s, they used mixed techniques, normative as well as
descriptive to make a better decision.
Overall, research shows that when deciding between multi -attribute
options, no single decision technique is always adopted. Rather, it appears
that techniques are us ed to strike a balance between lowering cognitive
load and enhancing the utility of the desired outcome. In general,
cognitive burden of decision -making may be reduced by selecting at
random, but the resulting decisions would be bad. By evaluating all
alternatives on all important dimensions, integrating the resulting
information for all alternatives, and picking the best, the quality of
decision making would be maximized, but the information processing
necessity would be extremely demanding. Participants g enerally strike a
balance between effort and decision quality, and they may change
methods during a task.
6.2 TWO SYSTEM APPROACHES TO DECISION
MAKING
In day to day life, you'll undoubtedly notice that some of your decisions
are made almost instantly with little or no conscious effort, while others
take a long time to get at. Making decisions about less important topics,
such as choosing curtains for living room, appear to be less effortful and
more intuitive than making decisions about more complex alterna tives,
such as employment offers, say by utilising MAUT. From the
psychological perspective, Evans (2003,2008), Kahneman (2003), Sloman
(1996), Stanovich and West (2000), and others have recently highlighted
the contrasts between intuitive and more reflect ive types of decision
making in what are known as two -system approaches to thinking and
decision making.Two different cognitive systems are proposed in these
accounts - System 1 and System 2.
System 1 is thought to be automatic, implicit, quick, easy, and e motive,
and it generates intuitive, immediate reactions.
In evolutionary terms, this system is thought to be rather old, and it is
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82 such processes is visible to consciousness, which means that the individual
cannot explain why they made their decision.
System 2, on the other hand, is thought to be recent in evolution and
unique to humans. It is closely connected with general fluid intelligence
and performance on sequentially solvable issu es, operates relatively
slowly and sequentially, is unemotional, is restricted by working memory
capacity, and allows abstract reasoning and hypothetical thinking. People
can explain why such decisions were made. The two systems are thought
to interact, wi th System 2 playing a key role in inhibiting and overriding
System 1.
When System 1 is suitable, such as when the costs of errors are
substantial, a gut reaction is unlikely to be the best basis for action and
should be evaluated. Automatic System 1 proce sses, on the other hand,
have a significant impact on what information a person responds to and
concentrates on, and hencewhat information System 2 uses to make a
decision (Evans, 2008).
Two-system view is that there are two modes of thought, System 1 and
System 2.
System 1 is a hypothetical system that carries out rapid intuitive thinking.
System 2 is a hypothetical system that carries out slow deliberate thinking
Both, the intuitive System 1 and reflective System 2 paths can be taken
while making decision s. Overall, System 2 will be more involved in
meticulous analytical decision -making that aims to mix several forms of
data in a rule -based method. System 2 processing would be expected for
normative methods like those described by MAUT. System 1 will play a
bigger role in decision -making based on heuristics and biases (Tversky &
Kahneman, 1974) as well as gut feelings (Gigerenzer, 2007).
Although when dealing with formal probability problems, heuristics (such
as availability) often lead to errors (as we saw for Linda problem),
Gigerenzer has proposed that heuristics often have validity in the real
world.
We will see further in the next section at this idea that how heuristics are
adaptive and useful and not just sources of error.
6.3 FAST AND FRUGAL HEURISTIC: T HE ADAPTIVE
TOOLBOX
As the name implies, this class of heuristics is based on a small fraction of
information, and decisions using the heuristics are made rapidly. These
heuristics set a standard of rationality that considers factors including,
time, infor mation, and cognitive capacity. Furthermore, these models
consider the lack of optimum solutions and environments in which the
decision is taking place. As a result, these heuristics provide a good
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83 can form a comprehensive description of how people behave in a variety
of contexts. These behaviors vary from lunch selections to how physicians
decide whether to prescribe medication for depression, to making business
decisions. Following a re two prominent examples of fast and frugal
heuristics: theRecognition Heuristic, which exploits a lack of knowledge,
and the Take the Best heuristic, which deliberately ignores information.
Both heuristics can be applied to choice tasks and to situations in which a
decision maker has to choose which of two objects has a higher value.
Recognition Heuristic: Suppose someone asks you which of two Italian
cities have the larger population, Milan or Modena. Most students have
heard of Milan, but they may not r ecognize the name of a nearby city
called Modena. The recognition heuristic typically operates when you
must compare the relative frequency of two categories.If you recognize
one category, but not the other, so we tend to conclude that the recognized
categ ory has the higher frequency. In this case, you would correctly
respond that Milan has the greater population.
Take the Best Heuristic: It was discovered by psychologists
GerdGigerenzer and Daniel Goldstein as part of their research on human
decision maki ng. In a 2013 study, researchers found that experienced
airport customs staff used the heuristic to select travellers for body
searches. To aid in their decision, the officers used attributes such as
nationality, amount of luggage, and airport of origin. P eople often use this
type of decision making strategies while voting for political candidates.
They tend to find 1 or 2 significant attributes of the candidate which they
find to be the most important and vote accordingly. Such as someone from
the agricult ural industry voting for a candidate solely based on who
supports their views on farm laws.
 Why it is called as adaptive toolbox?
The main difference between Gigerenzer's approach and Kahneman and
Tversky's heuristics -and-biases approach is that Gigerenzer emphasizes
the validity and adaptive value of real -life heuristics, whereas Kahneman
and Tversky were more inclined to emphasize the errors that heuristics
(such as availability) can cause. Heuristics appear to be most useful when
dealing with ordinary sc enarios, but they can lead to errors when dealing
with abstract problems that require explicit computations based on logical
and mathematical standards.
Overall, the heuristics established and investigated by Gigerenzer and
colleagues function well because the decisions are based on some
underlying fact in the environment that allows for successful shortcut
solutions.
6.4 NATURALISTIC DECISION MAKING
Naturalistic Decision Making (NDM) is also known as Recognition -
Primed Decision Making. Intuitive type of d ecision making is another
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84 studying this method. Klein (1998), Lipshitz et al. (2001), and Phillips et
al. (2004) studied firefighter, nurse, police, and military decision -making
in real -life e vents. This work is a more advanced variant of laboratory
work that examines what happens in real -life situations. In actuality, the
decision maker may not be given options to choose from, but instead must
devise one or more possible actions.
It was found that recognition primed decision making was most common.
For example, during a critical incident analysis if a police officer sees a
man on the street hiding a knife, from his mannerisms the police officer
might recognize that this was a situation of possi ble danger to other
citizens and tries to isolate the man.
Findings show that in many critical situations only a single action was
mentally generated and it was then executed. The basic that initially
produced possible actions are often very appropriate wa s replicated in a
study of expert chess players (Klein et al., 1995). The players were asked
to think aloud while deciding their moves to sample positions and it was
found that the very first moves that came to mind were rated as high
quality and much bett er than chance by independent expert judges.
NDM community views intuition as an expression of experience as
people build up patterns that allow them to quickly evaluate situations and
make rapid decisions without having to compare options. Now what
count s as expertise? NDM researchers identify experts as being able to
make fine discrimination that may be invisible to novices, having
sophisticated mental models of how things work, and having resilience to
adapt to complex and dynamic situations.
6.4.1 Natu ralistic Decision Making and Important Real Life
Decisions -
Undoubtedly, we all agree that when compared to the more field -based
naturalistic decision -making model, the relevance of laboratory -based
decision -making theories like multi -attribute utility the ory (MAUT) to
actual life may appear dubious.
Is naturalistic decision -making, better suited to real -life situations where
significant decisions must be made and people must respond without being
under extreme time constraints?
 What does the research say?
Cognitive researcher Galotti compared the results of five real -life
decision -making investigations to laboratory and naturalistic decision -
making models (Galotti, 2007). Participants in these research discussed
their experiences dealing with real -life cho ice dilemmas in areas such as –
 choosing a college
 choosing a major subject,
 choosing a birth attendant/helper and
 choosing a kindergarten. munotes.in

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85 Findings of this study showed that -
 Participants consistently considered fewer options and larger
criteria/attrib utes.

 With time, number of options shrank but number of criteria did not.

 Participants subjectively rated the importance of their criteria, the
value of each option on each criterion, and the overall attractiveness of
each option.

 Fit of people's intuit ive choices to the predictions of normative models
(such as MAUT) was surprisingly good.

 People did consider a number of options in these non -expert decisions,
as opposed to the 'one -option' decisions that are frequently seen in
time-pressed expert natura listic decision making, which is generally
based on recognition (Klien, 1998).
Take the first option -
Overall, it appears that many 'decisions' made by experts in real life do not
involve conscious decision making between alternatives. Based on
interviews with Klein and colleagues, it appears that a heuristic known as
‘take-the-first-option’, identified by Gigerenzer (2007), can and is
effectively used by experts in time -critical situations.
The naturalistic decision -making approach then strongly supports the use
of fast and frugal heuristics, particularly those based on expert recognition,
in real -life situations requiring immediate responses. Again, in such time -
pressed situations, System 1 intuitive processes are heavily involved.
When decisions are impo rtant and time limitations are not
stringent, people tend to approximate the more reflective, effortful
decision processes suggested by MAUT, and these processes involve
System 2.
6.5 NEUROECONOMICS: NEUROSCIENCE
APPROACHES TO DECISION MAKING
6.5.1 What is Neuroeconomics?
Neuroeconomics is the study of neural processes underlying economic
decisions.
Researchers have recently begun to use neuroscience technologies such as
brain imaging and neuropsychological examinations of the effects of brain
lesions on fi nancial decision making to uncover neurological roots of
decision making, resulting in the formation of a new hybrid field called
neuroeconomics.
Neuroeconomics is indeed an attempt to bring neuroscience, psychology,
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86 neuroeconomics investigates brain activity before, during, and after
economic choices.
Neuroeconomics use wide variety of techniques to measure brain activity.
Some of these techniques are so invasive, for example, single neuro n
recordings, that they can only be used on animals. Other techniques, for
example, functional magnetic resonance imaging, FMRI, are less invasive
and are able to measure the hemodynamic response , that is, the changes in
blood flows to different parts of the brain. There are very recent
techniques such as transcranial magnetic stimulation , TMS as well.
6.5.2 Link between Neuroeconomics and decision making -
 Reward system and decision making -
In primates, recordings from dopamine neurons (Tobler et al., 2005), the
orbitofrontal cortex (Roesch& Olson, 2004; Tremblay & Schulz, 1999),
and the p osterior cingulate cortex (McCoy et al., 2003) have revealed
neural responses that are directly related to reward size, and similar
findings have also been reported in human studies with respect to
monetary rewards (Elliot et al., 2003). Thus activity in do pamine neurons
is linked to reward size and so such activity is linked to choices as choices
follow reward.
An interesting FMRI study by McClure et al. (2003) found that people’s
stated preferences for Pepsi versus Coke were matched by responses in the
ventromedial prefrontal cortex on tasting these drinks.
 Dual System Approaches to decision making and neuroscience -
The dual systems approach to decision making outlined above have also
been supported by neuroscientific studies. When given the option of
choosing between 100 rupees today or 200 rupees in a month, many
people choose for the 100 rupees today. If the choice is between 100
rupees in a year and 200 rupees in a year and one month, the delayed
option is frequently picked, despite the fact that the t ime difference
between the two options is still one month, just as it was when 100 Rs was
available immediately. The limbic system, which reflects System 1
activity and responds impulsively to immediate rewards, is thought to be
the source of short -term im patience. The lateral prefrontal cortex, which
reflects System 2 activity, controls the delayed reward choices. In an fMRI
study, McClure et al. (2004) discovered that when participants chose
delayed options, there was relatively more fronto -parietal activ ity (related
with deliberative processing) and limbic system activation (associated
with emotional processing) than when they chose immediate options.
 Study of Emotions and neuroeconomics -
By linking economic decision making to brain function, the emergi ng field
of neuroeconomics has highlighted the overlap in the neural systems that
mediate choice and other behaviors, including emotion. Like cognitive
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87 emotion in economic decision m aking is blurred when attempting to
understand the neural circuitry mediating these classes of behaviors
(Phelps, 2006)
In an fMRI study, Sanfey et al. (2003) discovered that accepting unfair
offers was associated with relatively greater activation in the dorsolateral
prefrontal cortex (related to controlled cognitive processing) and rejecting
unfair offers was associated with relatively greater activation in the right
anterior insula (related to negative emotions such as disgust).
6.5.3 Ageing brain and f inancial decision making?
Have you ever noticed that many investment companies target the retired
people for their investment seminars where they explain that they will get
high returns if they invest in their plan by showing them tempting offers?
You must have. If you try to understand the reason behind that you will
realize it is because older people ,despite having more experience, often
make poor financial choices. Recent neuroscientific study are exploring
on this area.
Older people, despite their ye ars of knowledge, are more likely to make
financial mistakes by exaggerating prospective benefits and downplaying
potential risks. Older adults appear to be less concerned about potential
financial losses than younger people.
Cognitive decline with ageing -
Understanding how financial systems work and having the mental acuity
to locate and choose the best option are both required for making good
financial decisions. 'Experience brings progress, but after a point, the
accumulation of experience starts to be overshadowed by decrease in
cognitive function,' according to Sumit et al. (2009).
This is consistent with our understanding of cognitive ageing, which
shows that as we become older, we lose a number of cognitive abilities
such as memory, analytical reason ing, and processing speed. Crystallized
intelligence, a person's acquired knowledge about the world, is the only
thing that remains constant or even increases.
Affective factor -
Affective processes have a role in decision -making, and it has been
discovered that older people are generally more optimistic than younger
ones, and are more prone to focus on the situation's possible benefits. This
proclivity to emphasize on the good outcomes affects the decisions in
elderly people.
If older individuals are aware that they are prone to focusing on the
benefits or 'upsides' of their financial decisions, taking the time to consider
the potential losses could help them avoid making costly judgments .
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88 6.6 SUMMARY
To summarize, this unit enumerated further theories of decision making
with respect to multi -attribute alternatives. In the case of multi -attribute
decision making, the burden of integrating multiple attributes into a single
overall value measure leads to suboptimal but easy procedures like
elimination -by-aspects and satisficing. Payne demonstrated the use of
elimination -by-aspects, at least as a first stage in multi -attribute choice
problems.
Gigerenzer (1993, 2007) emphasised the broad benefits of real -life
heuristics like the recognition heuristic, which all ow for good decision
making with minimal effort (fast -and-frugal heuristics).
The popularity of fast -and-frugal heuristics, such as taking the first
alternative thought of in a given situation, is further supported by studies
of real -life decision making u tilising the naturalistic decision making
approach.
According to research into the brain underpinnings of decision -making
and specifically in the area of financial decision making shows that how
different neural processes are involved in the study of finan cial decision
making
6.7 QUESTIONS
Q.1. What are the models of decision making to deal with multi -attribute
alternatives? Explain normative as well as descriptive approaches.
Q.2.What are the relative roles of System 1 and System 2 processes in
decision making?
Q.3. Explain the Gigerenzer’s perspective towards heuristic and how it
serve as adaptive toolbox? Explain how it is different from
Kahneman’s approach.
Q.4. Do neuroscience approaches increase our understanding of decision
making? How?
Q.5 Write S hort notes on -
A) Multi -Attribute Utility theory
B) Satisficing and Elimination by Aspects
C) Naturalistic decision making
D) Decision making in elderly people
E) Neuroeconomics

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89 6.8 REFERENCES
 Galotti, K. M. (2017). Cognitive ps ychology in and out of the
laboratory . Sage Publications.

 Galotti, K. M. (2007). Decision structuring in important real -life
choices. Psychological science , 18(4), 320 -325.

 Gilhooly, K., Lyddy, F., &Pollick, F. (2014). EBOOK: Cognitive
Psychology . McGraw Hill.

 Phelps, E. A. (2009). The study of emotion in neuroeconomics.
In Neuroeconomics (pp. 233 -250). Academic Press.

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90 7
REASONING - I
Unit Structure
7.0 Objectives
7.1 Introduction
7.2 Deductive Reasoning
7.2.1 Propositional Reasoning
7.2.2 Syllogistic Reasoning
7.3 Henle on ‘rationality’
7.4 Summary
7.5 Questions

7.6 References

7.0 OBJECTIVES
After reading this unit you w ill be able to understand –

1. What is reasoning?
2. What is Deductive Reasoning?
3. What is propositional reasoning?
4. What is meant by mental logic approaches?
5. What is syllogistic reasoning?
6. What are the various biases that influence logical reasoning?
7.1 INTROD UCTION
The great philosopher Aristotle said that all human beings are rational.
They have the ability to reason. We are continuously using reasoning
while making even routine daily decisions. We are evolutionary wired to
use reasoning. It is crucial for ou r survival. Any error in our reasoning can
adversely affect our relationships, our careers, and even our safety. People
may use reasoning deliberately to solve certain puzzles or problems or
they may use it at unconscious level, even while not being aware of it. For
example, in the Silver Blaze story written by A.C. Doyle in 1892,
Sherlock Holmes solves the mystery of disappearance of racehorse, named
Silver Blaze, by using reasoning at conscious level. He argues that the
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91 the stable, the dog would have barked, but the dog did not bark. That
indicates that the person who entered the stable and took away the horse
was not a stranger. Using the reasoning like this, he came to the
conclu sion that that the theft of the racehorse was done by somebody from
the known people and not by a stranger. The mystery was solved. On the
other hand, when a mother observes the child lowering his face and
stammering while answering her questions, she conc ludes that the child is
guilty of doing something that he was not supposed to do. She has used
reasoning at an unconscious level. When we hear the sound of water
dripping, we conclude that the tap must be open and we go to check which
tap is open. We use r easoning at unconscious level. Thus we are
genetically programmed to use reasoning in almost all of our decisions.
Now, let us see what is reasoning.
“Reasoning is a stepwise thinking with a purpose or goal in mind” —
Garrett.
2. “Reasoning is the term appl ied to highly purposeful, controlled and
selective thinking” —Gates.
Reasoning is the cognitive process of deriving new information from old
information.
“Reasoning is the word used to describe the mental recognition of cause
and effect relationships, it m ay be the prediction of an event from an
observed cause or the inference of a cause from an observed event” —
Skinner.
Thus, reasoning is a highly specialized thinking which helps an individual
to explore mentally the cause and effect relationship of an even t or
solution of a problem by adopting some well -organized systematic steps
based on previous experience combined with present observation. There
are two types of reasoning – Deductive and Inductive reasoning. In this
unit, we will discuss deductive reason ing.
7.2 DEDUCTIVE REASONING
It is the ability to draw some logical conclusions from known information,
that is known to be true. For example, All branches of psychology are
interesting, Cognitive psychology is a branch of psychology. Therefore,
cognitive psychology must be interesting.
Here one starts with already known or established generalized statement or
principle and applies it to specific cases. For example,All human beings
live in wateryou are a human being, therefore, you must be living in water.
Though the conclusion in above example seems to be logical but we know
that it is not true. What does it show? It means that the truthfulness of the
conclusion depends upon the first assumptions being true. The initial
assumption is called premises, e.g., in above example, the statement ‘all
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92 truthfulness of conclusion depends upon this premises. The conclusion
may be logical and yet not true if the premises is not true.
Deductive reasoning is further divided into two types –
1. Propositional reasoning
2. Syllogistic reasoning
7.2.1 Propositional Reasoning:
Propositional reasoning refers to a set of rules based on logic. These rules
help in developing the arguments. These arguments consist of simp le
statements bound by simple logical relations. These logical relations are
also called conditional rules, for example, these simple statements are
connected to each other with words like and, or, not and if…then. Let us
take an example, suppose the given statement is that ‘if it is 7 p.m. Tina
goes out to play’… it is 7 p.m. then it is very natural for people to
conclude that Tina is out to play. On the other hand, if the statement is
‘Tina is not out to play’ it is very difficult for people to conclude t hat it is
not 7 p.m.
Certain inference rules are developed by logicians to derive correct
conclusions from patterns of propositions. For example,
a.) Modus ponens – The word modus ponens is derived from Latin
language and it means ‘mode of affirming’. Accordi ng to this rule ‘ If p
then q’ and given p is true, it follows that q is true too. For example, if the
premise is “I f it is Monday (p), Tina goes to college (q)”.Then given the
information “I t is Monday today(p is true) we can conclude therefore
Tinahas gon e to college (q is also true) .
b.) Modus tollens – This Latin word means ‘mode of denying’.
According to this rule, ‘if p then q’ and given ‘not q’ therefore not -p
follows. For example, if it is Monday, I go to college. I am not going to
college today, therefor e today is not Monday.
c.) Double negation – According to this rule not (not p ) therefore p. For
example, ‘it is not not Monday, therefore , it is Monday”.
The first two inference patterns ,viz., modus ponens and modus tollens
have been often used in conditi onal (if..then) rules, but there are two
significant fallacies or mistakes that people often make when engaging in
such reasoning .
1. Affirming the consequent : Affirming the consequent is the first
fallacy of conditional rule that uses the inference pattern of modus ponens
in incorrect way . As a ccording to modus ponens pattern, ‘if p then q’ ,
thereby assuming ‘q’ means ‘p’ is true , is an error . For example, if it is
Monday then Tina goes to college ;Tina is going to college, therefore it is
Monday. This infe rence is not correct, because the rule does not mean that
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93 2. Denying the antecedent: This is another fallacy of conditional rule
that uses the inference pattern of modus tollens incorrectly . This involves
using modus toll ens pattern of, ‘if p then q’ to conclude ‘not p’ means ‘not
q’ is true. For example, If it is Monday, Tina goes to college’. It is not
Monday, there fore Tina is not going to college today. This argument is
also not valid inference as the rule does not mea n that Tina goes to college
only on Mondays.
Above examples of ‘if…then’ conditional rules are called ‘ material
implication . There is another type of conditional rule that is known as rule
of equivalence or biconditional . Biconditional rule states the rule as ‘ if
and only if’. In other words, ‘q if and only if p’, that means p is true only
if q is true ( affirming the consequent) and ‘not q’ happens when ‘not p’(
denying the antecedent). For example, only and only if a closed figure is a
triangle, then it has three sides and if it does not have three sides then it is
not a triangle. Affirming the consequent and denying the antecedent are
valid arguments if we follow the rule of equivalence but not valid if we
follow the rule of material implication. There i s a possibility of an error
taking place if we misinterpret material implication as equivalence in
conditional reasoning.
Psychologists carried out many research studies to find out how people
perform on these four arguments, viz., modus ponens, modus tol lens,
affirming the consequent and denying the antecedent. These experiments
were conducted by using both abstract material ( e.g. , if there is an A then
there is 1) as well as concrete materials (e.g., if it is Sunday then Tina eats
non veg in lunch). Meta analysis of these studies indicated that people
perform with almost 100 percent accuracy in the case of modus ponens,
with 60 percent accuracy in case of modus tollens and about quarter of a
time people correctly reject affirming the consequent and de nying the
antecedent fallacies.
Conditional inferences can be put in table form as shown in table 1 for
better clarity
Table 1
Modus Ponens:
If p, then q
P is true
_______
Conclusion : q is true
Modus Tollens:
If p, then q
Not q
_________
Conclusion: N ot p Affirmation of the consequent
If p, then q
q
_______
Conclusion : p
Denial of the antecedents
If p, then q
Not p
_________
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94 Suppression effects
Some psychologists believe that fallacies in conditional reasoning can be
due to misi nterpretation of the premises. For example, suppose a student is
given the information that “ if there is heavy rain, then water logging takes
place in Mumbai”. Student is now given the information that there has
been water logging in Mumbai, he might draw the inference that “ it must
have rained heavily”. But suppose if he is given additional information
that water logging can take place due to high tide in the sea or due to
clogged gutters, then he will not be so sure that water logging has taken
place du e to heavy rain. Rumain, Connell, and Braine (1983) in their study
showed that when a possible alternative was explicitly presented to
participants, the affirmation of consequent (e.g. if p then q , q therefore p)
and denial of the antecedent (e.g. if p then q, not p , therefore not q) are
less likely to take place. They suggested that giving additional antecedents
such as “if gutters are not clogged, there is no water logging” makes it
clear that heavy rain is not the only thing required for water logging to
take place and therefore equivalence interpretation is blocked and fallacies
of affirming the consequent and denying the antecedent is suppressed.
Later on, Byrne (1989) found a similar effect on modus ponens and modus
tollens when a possible disabler was mentioned. In other words,
knowledge of additional background conditions suppresses inferences
such as modus ponens and modus tollens. For a pair of conditionals that
contain an additional condition (sometimes called an enabler), such as “ if
it rains hea vily, water logging takes place in Mumbai ; also if gutters are
clogged, water logging takes place in Mumbai”, the frequency of student
making an inference drops drastically because now he is not sure why the
water logging took place in Mumbai. For example, when a student is told
“ there is waterlogging in Mumbai” he no longer makes the modus ponens
inference “ therefore it rained heavily”, and when a student is told that “
there was no waterlogging in Mumbai” he does not make the modus
tollens inference “ t herefore it did not rain heavily in Mumbai”. These
findings have come to be known as the suppression effect. The additional
information or extra premises seems to form a conjunctive condition with
the first premise or first information. For example, “ If i t rains heavily and
if the gutters are clogged then water logging takes place in Mumbai”. This
indicates that surrounding context can affect interpretations and so
influence reasoning.
Mental logic approaches
David Braine believed that people apply mental logic rules that they can
apply
to solving reasoning problems. Braine et.al. (1991) explained three aspects
of conditional reasoning –
1. A set of mental inference rules or schemas – it permits inferences
when the schema conditions are met. These schemas mat ch some rules
of logic such as modus ponens but not modus tollens. These mental rules
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95 So the schemas may or may not match the formal inference rules. The
schemas ta ke the form of ‘Prem ises Conclusion, an example of a
disjunctive syllogism schema is: e.g. premises : p or q ; not p then
conclusion: therefore q
Any two given statements represent p and q, if either p or q or both are
true and not p is also true, it means q must be true. Fo r example, “it is
raining heavily or Rosy is shopping”; “it is not raining heavily”; therefore
Rosy is shopping.
Brain et. al.(1984) suggested that there are 16 simple inference schemas
on which people make less errors. If a given problem directly evokes a
particular individual schema, it is considered as non - problematic by a
person, but if it does not invoke individual schema, the person finds it
problematic. For example, suppose the given information is “Either Mr. X
will go to a restaurant or Mr. X wil l go for a movie” and the other piece of
information given is “Mr. X will not go to a restaurant”. This will evoke
the disjunctive syllogism schema and by using that one can conclude that
Mr. X will go for a movie. This is simple and the person making a
decision will find it nonproblematic.
2. A reasoning programme that implements the schemas to construct
the lines of reasoning
3. A pragmatic architecture in which reasoning is imbedded.
Braine et. al. (1984) proposed in their mental logic theory that people
reason by applying mental rules in the form of schemas. An experiment
was conducted to test Braine et.al.’ s this proposal. In this experiment,
participants were presented one line at a time (i.e. premises) at a
predetermined speed on a computer monitor. A fter presenting all the
premises, they were presented with a possible conclusion which the
participants had to judge whether it is true or false. For example: The
premises presented were -
7. There is an L or a W.
2. If there is an L then there’s not an E .
3. If there is a W then there’s not an E.
4. There is an E or an O.
Then they were asked in conclusion -
Is there is an O?
(Answer is ‘Yes, there is an O’.)
This kind of task needs more than one kind of schemas to make a
judgement. For example, the firs t three lines or premises in the above
example indicated that there is not an E. Using this information, in the
fourth line, one can infer from line four that there is an O. So one can say
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96 the basis of the length of the problem and the number of schemas required
to solve that particular problem. In general, it has been found that Braine
et al.’s mental logic theory proposing that people reason using a limited
number of schemas, was w ell supported by their experiments.
Mental models
“Mental models are deeply held internal images of how the world works,
images that limit us to familiar ways of thinking and acting. Very often,
we are not consciously aware of our mental models or the effe cts they
have on our behaviour.” – Peter Senge
Mental models are made up of meaning, values, ideas, beliefs, concepts,
premises, images, representations, previous experiences, symbols,
language, assumptions, etc. mental models represent the meaning of
conn ectives and quantifiers such as and, or, if , and also include such
quantifiers as “most” and “few,” and a variety of other sorts of
constructions, such as spatial, temporal, and causal relations, and
counterfactual conditionals. They are the thinking tools that you use to
understand life, make decisions, and solve problems.
Mental model approach proposes that people tackle logical reasoning
problems by forming mental representations of possible states of the world
and draw inferences from those representati ons. Mental models, it is
argued, offer economical forms of representation that appear
psychologically plausible. In other words, e ach mental model represents a
possibility. If the premises are true, there is a possibility of drawing a
conclusion.
So, we can say that mental representations of possible states of the world
is known as mental models. Mental models are also known as the model
theory. A fundamental assumption of the theory is the principle of truth.
Johnson -Laird (1999) stated that mental model s help people reduce burden
on working memory by only representing only what is true and not what is
false. If mental models are not complete, they may lead to ‘illusory
inferences’ that may be compelling, but invalid inferences. For example,
Johnson -Laird (2006) conducted an experiment as follows –
Either Jane is kneeling by the fire and she is looking at the TV or
elseMark is standing at the window and he is peering into the garden.Jane
is kneeling by the fire.
Does it follow that Jane is looking at the TV?
Most people do say ‘yes’ to this question, but the inference is not valid; it
is an example of an illusory inference.
Just because Jane is kneeling by the fire, it does not follow that she is
looking at the TV; she may be or may not be. Johnson -Laird argued that
the principle of truth leads people to form models in which the possibility
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97 Johnson -Laird et al. (1992) also found that the number of mental models
needed per problem depends upon the level of problem difficulty, i.e.,
difficult tasks require more models. They further reported that that modus
ponens is easier than modus tollens for conditionals because modus
ponens requires only one model while modus tollens requires three
models. It was observed that exclusive disjunctions (i.e. ‘ p or q , but not
both’) were harder than conditionals and that modus tollens was easier with
biconditionals (or equivalences) than with conditionals.
Obsessive -compulsive disorder, an xiety and depression are three examples
of neuroses which are disorders of behaviour and feeling. Beck (1976,
1991) believed that they are due to faulty reasoning either from invalid
inferences or from false beliefs and this is the basis of his cognitive -
behavioural therapy. For example, a person with depression might make a
conclusion without realizing that it is invalid,: ‘If you’re worthless then
you fail at everything’; ‘I failed my exam’; ‘So, I am worthless’. On the
other hand, Johnson -Laird et al. (2 006) proposed that neuroses originated
in overemotional reactions to situations (the hyper -emotion theory) and
that reasoning errors were not a key factor in such mental illnesses. They
argued that if anything, neurotic patients should reason better about
material related to their disorder than controls, because the patients tended
to be very preoccupied with their condition and mulled over material
related to their condition very often.
Evaluation of mental models versus mental logic
One of the advantages of mental model theory is that at least in principle it
can be refuted. But the disadvantage of mental model theory is that if a
deduction depends upon many models then it violates its principal
prediction. In case of mental logic theory, O’Brien et.al. (1 994) showed
that participants handled well even those tasks that required many models.
For example, O’Brien et.al. (1994) conducted an experiment, where they
propositioned that
If O or K or R or C then X
If E or F or G or H then Y
K F.
What follows?
100 per cent of participants answered correctly, ‘X and Y’, although the
problem involves 58 mental models.
On response to this, Johnson -Laird et al. (1994) said that participants
would not blindly generate models unnecessarily. They would realize that
only a small part of the premises needs to be represented and that can be
done with a manageable number of models. But the criticism against this
argument is that one needs to add procedures to the model to enable
participants to know when models are unnecessary and this makes the
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98 Both mental logic and mental models approaches successfully deal with
propositional reasoning but only mental model approach easily applies to
syllogistic reasoning.
7.2.2 S yllogistic Reasoning
So far, we have discussed propositional reasoning. Another type of
deductive reasoning is categorical syllogisms. According to Merriam -
Webster Dictionary syllogism is a kind of logical argument in which one
proposition (the conclusion) is inferred from two or more others (the
premises) of a certain form.
Timbreza (1992) defined syllogism as “an argumentation in which, from
two known propositions that contain a common idea, and one at least of
which is universal, a third proposition, di fferent from the two propositions,
follow with necessity ”.
CATEGORICAL SYLLOGISM is a deductive inference which consists of
three categorical propositions, the first two which are premises and the
third is the conclusion . It contains exactly three terms.
For example - All animals are mortals.
Every dog is an animal.
Therefore, every dog is mortal.
In this example, there are two assumptions about the category of things,
“animals” and “dogs” and properties li ke being mortals. First two
statements are the premises or assumptions and third statement is
conclusion. As the third statement
(the conclusion) definitely follows necessarily from the first two (the
premises) it is a valid syllogistic argument that lead s to a true conclusion.
On the other hand, consider another example –
All Chimpanzees are mammals (premises)
All cows are mammals (premises)
Therefore all cows are Chimpanzees. (conclusion)
As you can see, in this example, the conclusion does not follow from the
two true premises and has a invalid form of argument, so it is a invalid
syllogistic argument. It is not possible for a valid syllogistic argument to
have true premises and false conclusion. That is, the conclusion
necessarily follows from the pre mises. An invalid deductive syllogism is
one where if the premises are assumed true, it is possible for the
conclusion to be false. That is, the conclusion does not necessarily follow
from the premises.
Apart from validity, syllogism can be varied in vario us other forms such
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99 Quantity wise – The quantifiers can be expressed in terms of all, none,
some.
Quality wise - The premises and conclusion - may be negative or
affirmative.
The terms – The terms used may be abstract or concrete. Example of
abstract terms can be “ All S are P” .Example of concrete terms can be
“All dogs are mammals”
The premises and conclusion - may be negative or affirmative.
The propositions in the argument -may be empirically true or false.
Even the required responses can vary. For example, participants can be
asked to generate valid inferences from given premises; to judge a
possible conclusion as valid or not; or to select a valid conclusion from a
list of alternatives.
Basic findings from syllogistic reasoning studies:
Wilkins ( 1928) showed that one of the problems with syllogism is that
compared to concrete premises, inferences from abstract premises can lead
to wrong conclusions. For example, in abstract form we may say
All Cs are Ms
All Ds are Ms
Therefore, all Ds are Cs
This argument in this abstract may be considered as valid argument
though it is not valid. If we substitute abstract terms with concrete terms
such as Cs as Chimpanzees and Ds as cows, then the conclusion as shown
above will be all cows are chimpanzees, which is an invalid argument.
However, Wilkins (1928) suggested that sometimes even in case of
concrete syllogisms, people accept invalid conclusions due to atmosphere
effect.
The atmosphere effects:
Atmosphere effect is a tendency to draw conclusions in syllo gisms that are
over influenced by the form of the premises rather than the logic of the
argument. The atmosphere effect is also known as ‘global effect’ created
by the premises, and accounts for common errors in syllogistic reasoning.
Woodworth and Sells ( 1935; Sells, 1936) defined atmosphere effect as the
influence which the context, or tone, of a situation has upon the
completion of a task. It was assumed that, if one does not understand, or
does not usethe given logical relationships, the conclusion will be based
upon the structural features of the syllogism, i.e., the quantifiers and
qualifiers.
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100 Quantitative (all or some) and qualitative (affirmative or negative)
attributes of premises can together produce an "atmosphere" that induces a
participant to ei ther accept or reject a certain conclusion consistent with
it.Woodworth and Sells (1935) proposed that if both premises involve
‘all’, people are disposed to accepting an ‘all’ conclusion. If
anyo nepremise involves ‘some’, people will be disposed to a ‘som e’
conclusion. If any o nepremise involves ‘not’, people are disposed to
accept a ‘not’ conclusion. An invalid argument is often accepted by the
participants, if the argument is presented in abstract form.
Atmosphere versus conversion errors (illicit conve rsion) and
probabilistic inference:
Chapman and Chapman (1959) presented an alternative to atmosphere
effect hypothesis. According to thempeople apply heuristicsknown as
‘conversion’ and ‘probabilistic inference’which are not appropriate. In
their experim ent, they gave participants problems such as
Some Ls are Ks
Some Ks are Ms
Therefore, (1) No Ms are Ls, (2) Some Ms are Ls, (3) Some Ms are not Ls,
(4) None of these, (5) All Ms are Ls.
The correct conclusion to above problem is (4) ‘None of these’.
Participants tended to be wrong on such items and the kind of error that
they made depended on the form of syllogism. When they presented
different types of syllogism, the type of error that participants committed
was determined by the atmosphere effect. Howev er, participants failed on
the following type of syllogism –
(A)
Some X are Y
No Y are Z
and
(B)
Some X are not Y
No Y are Z.
The right response for both A and B (on atmosphere) was ‘Some Z are not
X’; but most of the participants chose ‘No Z are X’, esp ecially on (A)
problem. In case of B problem, they were evenly split between the
universal and the particular conclusions on (B). Chapmans said that results
of their experiment can be explained further by two reasoning errors called
‘conversion’ and ‘probab ilistic inference’.
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101 Conversion errors:
There are two types of conversion errors that will be described first in
abstract terms and then in concrete terms. First is the assumption in
abstract form that –
(1) from ‘All X are Y’ that ‘All Y are X’ and
(2) that ‘Some As are not Bs’ implies
‘Some Bs are not As’.
In concrete form, it will be -
‘All women are human’, but it does not imply that ‘All humans are
women’. Similarly, saying ‘Some humans are not politicians’ does not
imply that ‘Some politicians are not humans’.
Chapmans said that people tend to make conversions unless they have
information to the contrary (which they do not have with abstract
material).
Probabilistic inference:
Probabilistic inference refers to ‘plausible reasoning’ that is not valid in
deductive logic. For example , suppose it is said
‘Some cloudy days are wet’, (premises)
‘Some wet days are unpleasant’, (premises)
‘Some cloudy days are unpleasant’(conclusion)
In this example, the conclusion may be true or may not be true. It does not
necessarily follow from the premises, even if the premises are true. The
Chapmans said that these two types of errors explained their results much
better than the atmosphere effect.
Later on ,Begg and Denny(1969), Sells (1936) and Chapman & Chapman
(1959) re -examined the atmosphere effect vs. conversion error and found
that atmosphere predictions were more frequently found to be true than the
conversion and probabilistic inference predictions.
However, Wason and Johnson -Laird (1972) concluded from the ir study
that atmosphere hypothesis cannot completely explain syllogistic
reasoning. Even Wilkins’s (1928) too reported that atmosphere effect is
not so strong with familiar or concrete material as it is with abstract or
unfamiliar material.
7.3 HENLE ON ‘RATIONALITY’
Henle(1962) was of the opinion that the atmosphere hypothesis,
probabilistic inference and illicit conversion do not fully explain our
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102 the examples of illogical thinkin g actually tacitly ignore some of the given
premises, ignore the misrepresentation of some the other premises, and
include additional premises. In general, people make rational inferences
depending upon how they interpret the premises. To support her point of
view, For example , Henle gave the following problem participants of
experiment and asked them to assess the validity of this argument, as well
as give reasons for their answers.
It’s important to talk about things that are in our minds.We spend so muc h
of our time in the kitchen that household problems are in our minds.
Therefore, it’s important to talk about household problems.
She found that some of her students did not look at the task as an exercise
in pure logic. They did not differentiate betwee n logical validity and
factualtruth. They said that “it is not important to talk about things in our
minds unless they worry us.” Many of them interpreted the premises or
conclusion and this led to change in intended meaning. Some of them
completely ignore d the premises and said “ I don’t think of household
problems so it is not important for me to talk about them”.
At times, participants added a totally new premises that was not given to
them originally. For example, “ It is only important to talk about th e things
that really worry us a lot and household problems don’t; so it is not
important to talk about them”.
On the basis of such observations Henle (1962) said that the reason for
subjects seemingly giving invalid conclusions or failing to see the falla cy,
can be that they have worked with material which is different from the
intended material or they may have performed the task in a different
manner than what was intended. So, if we take into account the way an
individual actually understood the materia l and the task to be performed,
then his conclusion may not appear to be invalid and his reasoning may
not appear to be a faulty reasoning. This shows that laws of logic can be
detected from their thinking process itself.
Henle emphasized that different participants can interpret tasks, materials
and goals in different ways. If we pay attention to different possible
interpretations of an argument, then it is easy to understand the behaviour
of people and conclude that they have followed the logical reason ing but
that reasoning is different from what experimenter had originally intended
it to be.
Ceraso and Provitera (1971) compared the interpretations of traditional
syllogism statements and syllogism premises having very clear
interpretations. For example , one group of participants were given the
statement ‘Some of the As (but not all) are Bs, but all of the Bs are As’
(syllogism statements with clear interpretation).
Another group were given the traditional syllogism statements such as
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103 the clarified premises performed much better than those who were given
the traditional syllogism premises.
Culture and logic:
In 1971, Luria conducted a study on non -literate peasants in Soviet Central
Asia and in the same year Cole conducted a study on non -literate Kpelle
adults in rural Liberia to understand their logical thinking to a reasoning
problem. In Luria’s study, participants did not consider the given task as
merely an exercise having no cont extualized logic. They considered the
given exercise as task that requires strong contextualized real world
information. For example, the exercise given to them was
In the far North all bears are white.
Novaya Zembla is in the far North.
What colour are t he bears there?
Participants typically responded to this by saying, “ But I don’t know what
kind of bears are there. I have not been there and I don’t know.”
Cole (1971) gave the exercise such as “At one time Spider went to a feast.
He was told to answer th is question before he could eat any of the food.
The question is: Spider and black deer always eat together. Spider is
eating. Is black deer eating?”
Initially, participant responded by saying that “ I was not there, how can I
answer that question?” Later on, he said the black deer was eating grass
but he gave a non -logical reason for it.
Both these studies indicated that when someone gives a correct answer to
a reasoning problem, it does not mean the answer was deduced by
applying rules of logic.
Greenfi eld (2005) said that such non logical reasoning is influenced by the
cultural mindset of a person. Broadly, cultures can be categorized as
collectivist culture and individualistic culture. The collectivist mindset
represents the typical rural preindustrial societies where majority of the
people did not have exposure to formal education. This type of mindset
emphasizes the use of practical and contextualized knowledge in real
social settings. On the other hand, the individualistic mindset is
predominant in industrialized, urban and formally educated populations.
People having individualistic mindset are the ones who were exposed to
formal education. They can recognize and apply abstract knowledge of
rules and principles. For example, they can use the abstrac t principles and
rules of science and mathematics.They in fact value such rules.
Triandis (1989) believed that approximately 70percent of world’s
population belongs to collectivist mindset but Greenfield (2005) believed
that many people have both the tendencies but in varying proportion.
Some people have more of collectivist mindset and less of individualistic
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104 less of collectivist mindset. But they have both types of mindsets. Whic h
type of mindset will be more or less depends upon the opportunities that a
person gets in life. It also depends upon how religious a person is, as all
religions emphasize on collectivistic values. Garner et.al. (2005) proposed
that by using suitable prim ing methods, less dominant mindset can be
brought forth in dominance. For example, individualism can be brought
forth in Asians and collectivism can be brought forth in Americans.
Mental -model approaches to syllogisms:
Johnson et.al. (1975) showed how fi gure of the syllogism can lead to bias
in conclusion. They called it as ‘figure bias’. Figure bias can be defined as
the effect of figure on preferred conclusions. Now the question in your
mind will be what is figure in syllogism.
Syllogism has three ter ms – A,B, and C. The way these three terms are
laid out or paired is called figure. There are four possible syllogistic
figures - A-B, B -C; B -A, B -C; A -B, C -B; B -A, C -B. These layouts or
pairs determine which valid conclusions are preferred by a person. Le t us
put it in concrete example. Suppose the premises are –
‘Some of the parents are scientists;
all of the scientists are drivers;
therefore ……?’
In this syllogism, topicterm is not specified. We do not know whether this
syllogism is about parents or abo ut drivers. So, we may draw a conclusion
that ‘some of the parents are drivers. An equally valid but alternative
conclusion can be - ‘some of the drivers are parents’. Now suppose, the
premises are
‘Some of the scientists are parents;
all of the drivers a re scientists’
The conclusion would be “Some of the drivers are parents” and there can
be another alternative but valid conclusion that ‘Some of the parents are
drivers.
Johnson et.al.found that using premises of the form ‘A -B;’B -C’ always
produced a biase d conclusion in the form of ‘A -C’ even when ‘C -A’
conclusions were valid. This tendency to draw conclusions in the form of
A-C is called figure bias effect . As mentioned before, figure bias effect
refers to the effect of figure on preferred conclusions.
Johnson – Laird (1982,1983) believed that people first construct mental
models to interpret the premises. If the person draws a conclusion from the
given premises but makes an alternative model that is consistent with the
given premises but does not have sa me conclusion as the previous one that
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105 It was noticed by them that the atmosphere hypothesis, conversion and
probabilistic inference hypothesis do not predict the figure bias ef fect. So,
Johnson -Laird and Steedman(1978), came up with the mental models
theory. This theory has 4 stages. They are -
(1) interpretation of premises;
(2) initial heuristic combination of the representations of the two premises;
(3) formulation of a con clusion corresponding to the combination of
premises;
(4) a logical test (or series of tests) of the initial heuristic combination,
which may lead to the conclusion being modified or abandoned.
This theory differs from the previous mentioned theories in terms of the
last testing stage. This last stage can lead to a changed combination of
information in the premises which in turn may be tested again.
Johnson -Laird and Steedman explained the theory in the form of a
computer program and its performance vs. human performance. In their
experiment they used 64 problems. It was expected that some syllogisms
would not lead to any modifications while others would lead to modified
conclusions after testing. Where it was expected that premises will not
lead to any m odifications after testing did meet the expectation and were
found to be 80.4 percent correct, while in other syllogisms 46.5 percent
were found to be correct.
It was believed that figure bias takes place when the information is
processed in short term m emory. Johnson -Laird and Bara (1984)
conducted an experiment to test this belief and found that figure bias did
take place even when the participants were exposed to syllogism for a
brief period of just 10 seconds. With such short exposure to syllogism,
participants found it difficult to make combinations of premises in certain
figures (such as B -A, B -C, in which required reordering of terms in one of
the premises in order to integrate premises) and this led to high rate of
(incorrect) conclusions that ‘no conclusion can be drawn’. Johnson -Laird
(1983, p. 104) reports data from studies which found that the rate of
drawing correct conclusions declined sharply as the number of possible
combined models increased from one to three because load on working
memory increased. Gilhooly (2005) also supported this view that difficult
syllogisms put heavy load on working memory, impairing its performance.
Evaluation of Mental Models Theory:
1. First term as the topic of the argument - Wetherick and Gilhooly
(1990) and Ford (1995) have indicated other possible explanations of
figure bias. Wetherick and Gilhooly believed that figure bias takes place
because people have a tendency to pick the first term as the topic of the
argument. For example, if the premises presented is ‘A ll the scientists are
drivers’ and ‘All the drivers are golfers’ it is natural to take ‘Scientists’ to
be the topic here and to draw a conclusion about scientists, ‘All the munotes.in

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106 scientists are golfers’. If the premises were ‘Some drivers are golfers’ and
‘All t he scientists are golfers’ a conclusion in which ‘drivers’ was the
topic (‘Some drivers are scientists’) would be more natural.
2. The mental models theory assumes that all participants approach the
task in the same way and the theory does not provide any exp licit
mechanisms of change or improvement. But in reality, in any large sample
of participants, there are bound to be individual differences. Some may get
most syllogisms correct, some others may be at guessing level and the
remainder may show the typical variations in item difficulty.
Galotti et al. (1986) found in their experiment that participants who had no
training in formal logic but were bunched up as ‘good resoners’ either
used or quickly developed short -cut rules that helped them in avoiding
labor ious explorations of multiple models. For example, better reasoners
used the rules that two ‘some’ premises could only yield no valid
conclusion and similarly that two negative premises must give no valid
conclusion.
Belief bias and dual system theory
Studies have indicated that arguments can differ in validity or truthfulness
of the conclusions. The problem of truthfulness or believability generally
does not arise when argument has abstract material. Basically, no
participant has prior belief about whether or not ‘All As are Cs’. But when
concrete material is picked up from real life materials, prior beliefs do
influence the judgement about the validity of the presented argument. For
example, Kahneman(2011) demonstrated an example of belief bias . He
present ed a syllogism to his subjects –
All roses are flowers
Some flowers fade quickly
Therefore, some roses fade quickly.
Majority of the people will consider it to be valid argument because
conclusion is true in real life. But if we ignore the context and jus t look at
the premises presented to us, the conclusion does not follow logically from
the premises. There is an alternative possibility that may be there are no
roses in the set of flowers that fade quickly.
7.4 SUMMARY
In this unit, we started with what i s reasoning and why it is important.
Reasoning was defined as stepwise thinking with a purpose or goal in
mind. It is a cognitive process of deriving new information from old
information. Next it was emphasized that there are two types of reasoning
– dedu ctive and inductive. In this unit we have discussed deductive
reasoning and in next unit we will discuss inductive reasoning. It was
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107 to given information to draw valid conclusions. De ductive reasoning is
also of two types – propositional reasoning and syllogistic reasoning.
Propositional reasoning helps us to develop arguments based on certain
rules of logic. Drawing conclusions from given statements or premises is
known as drawing in ferences. Some of the inference rules are modus
ponens, modus tollens, and double negation. Modus ponens and modus
tollens are used mainly in conditional problems where the premises are in
the form of if…then. However, these rules suffer from two fallacies –
affirming the consequent and denying the antecedent. Another fallacy of
conditional reasoning is suppression effect. This takes place due to
misrepresentation of the premises. It is found that knowledge of additional
background conditions suppresses inf erences such as modus ponens and
modus tollens.
Braine et. al. (1984) proposed in their mental logic theory , in which they
emphasized that people use a set of mental inference or schemas, a
reasoning programme that implements the schemas to construct the l ines
of reasoning and a pragmatic architecture in which reasoning is imbedded .
Mental models are the thinking tools that you use to understand life, make
decisions, and solve problems. They are made up of meaning, values,
ideas, beliefs, concepts, premises, images, representations, previous
experiences, symbols, language, assumptions, etc. mental models
represent the meaning of connectives and quantifiers such as and, or, if ,
and also include such quantifiers as “most” and “few,” and a variety of
other sorts of constructions. Mental models are also known as the model
theory. If mental models are not complete, they may lead to ‘illusory
inferences’ that may be compelling, but invalid inferences .
Another type of deductive reasoning is categorical syllogisms . Syllogism
is an argumentation in which, from two known propositions that contain a
common idea, and one at least of which is universal, a third proposition,
different from the two propositions, follow with necessity.
Atmosphere effect is a tendency to draw conclusions in syllogisms that are
over influenced by the form of the premises rather than the logic of the
argument . Quantitative (all or some) and qualitative (affirmative or
negative) attributes of premises can combinedly produce an "atmosphere"
that in duces a participant to either accept or reject a certain conclusion
consistent with it.
When people apply heuristics which are not appropriate , it is known as
‘conversion’ and ‘probabilistic inference’.Chapmans said that people tend
to make conversions un less they have information to the contrary
.Probabilistic inference refers to ‘plausible reasoning’ that is not valid in
deductive logic . the conclusion may be true or may not be true. It does not
necessarily follow from the premises, even if the premises are true. While
talking about rationality, Henle (1962) said that the reason for subjects
seemingly giving invalid conclusions or failing to see the fallacy, can be
that they have worked with material which is different from the intended
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108 what was intended. Studies indicated that when someone gives a correct
answer to a reasoning problem, it does not mean the answer was deduced
by applying rules of logic.
Mental Model approach to syllog ism has 4 stages - (1) interpretation of
premises;
(2) initial heuristic combination of the representations of the two premises;
(3) formulation of a conclusion corresponding to the combination of
premises;
(4) a logical test (or series of tests) of the initial heuristic combination,
which may lead to the conclusion being modified or abandoned.
Belief bias generally does not take place when the premises are in abstract
form but takes place when premises are in concrete form.
7.5 QUESTIONS:
1. Define prop ositional reasoning. Discuss in detail various inference
rules developed by logicians to derive proper conclusions from
patterns of propositions?

2. Elaborate on mental logic approach and evaluate mental models.

3. Explain the concept of syllogistic reasoning and elaborate on basic
findings from syllogistic reasoning studies.

4. Write a short note on –
a) suppression effect,
b) the atmosphere effect,
c) conversion errors,
d) probabilistic inference,
e) rationality,
f) culture and logic.
7.6 REFERENCE
 Ashcraft, M. H. &. Radva nsky, G. A. (2009). Cognition. (5th ed),
Prentice Hall, Pearson education
 Francis, G., Neath, I., &VanHorn, D. (2008). Coglab 2.0 on a CD.
Wadsworth Cengage Learning, international student edition
 Galotti, K.M. (2014). Cognitive Psychology: In and Out o f the
Laboratory . (5thed.). Sage Publications (Indian reprint 2015)
 Goldstein, E. B. (2007). Psychology of sensation and perception. New
Delhi: Cengage learning India, Indian reprint 2008 munotes.in

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109  Matlin, M.W. (2013). Cognitive Psychology, 8thed., international
student version, John Wiley & sons
 Reed, S. K. (2004). Cognition: Theory and Applications. (6th ed.),
Wadsworth/ Thomson Learning
 Robinson -Riegler, B., & Robinson -Riegler, G. L. (2008). Cognitive
Psychology – Applying the science of the Mind. (2nded.). Pearso n
Education. New Delhi: Indian edition by Dorling Kindersley India pvt
ltd.
 Srinivasan, N., Gupta, A.K., & Pandey, J. (Eds). (2008). Advances in
Cognitive Science. Volume 1, New Delhi, Sage publications
 Sternberg, R.J. (2009). Applied Cognitive Psychology: Perceivnig,
Learning, and Remembering. New Delhi: Cengage learning India,
Indian reprint 2009



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110 8
REASONING - II
Unit Structure
8.0 Objectives
8.1 Introduction
8.2 Inductive reasoning: Testing Hypotheses - The Four -Card Selection
Task
8.2.1 Basic Results
8.2.2 Procedural variations
8.2.3 Interpretation factors
8.2.4 Matching bias
8.2.5 Memory -cueing (availability) accounts
8.2.6 Pragmatic reasoning schemas
8.2.7 Social contract theory
8.2.8 The selection task as optimal data selection
8.3 Generating and Testing Hypotheses
8.4 Wason’s reversed 20 questions task
8.5 Simulated research environmen ts
8.6 Summary
8.7 Questions
8.8 References
8.0 OBJECTIVES
After studying this chapter, students will be able to:

 Learn is inductive reasoning and where it is used
 Understand different researches conducted to explore inductive
reasoning
8.1 INTRODUCTION

In previous unit we learnt about deductive reasoning, in which inferences
or conclusions are drawn on the basis of logical rules and the conclusions
are true if the premises are true. In inductive reasoning, the conclusion
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111 “inductive reasoning is a form of reasoning in which inferences and
general principles are drawn from specific observations and cases.”

It is the base of developing hypotheses (expected relationships between
variables) and ver ifying their truthfulness.

Inductivereasoning has two tasks –

a.) Hypothesis testing - assessing hypotheses for truth/falsity against
data. The hypotheses cannot be conclusively proved but they can be
disproved. Inductive reasoning is inherently uncertain. In this form of
reasoning we cannot conclusively prove the hypothesis true, we can only
say in degrees to which, given the premises, the conclusion is credible
according to some theory of evidence. Instead of being valid or invalid,
inductive arguments ar e either strong or weak, which describes how
probable it is that the conclusion is true.
Hypothetico -Deductive Method: This is one of the most prevalent
method of testing the hypothesis. In this method, first conclusions are
drawn from the premises and th en the truthfulness of those conclusions
are verified against the data.
b.) Hypothesis generation – This refers to deriving possible
hypotheses from data for later testing, that is, a person can get data from
obtained observations and aim to make a generaliz ation supported by the
evidence. Such hypotheses need to be tested and may not lead to
absolutely true conclusions.
8.2 INDUCTIVE REASONING: TESTING HYPOTHESES
- THE FOUR -CARD SELECTION TASK

Wason (1966,1968) tested hypothetico -deductive reasoning by usi ng the
four card selection task also known as Wason card task.This task is used to
test the truthfulness or falsification of hypotheses. To use this method,
four cards are shown to a participant. Each card has a letter on one side
and a number on the other side. The participant is asked to identify the
card which need to be turned over to test the statement, “ If a card has a
vowel on one side, then it has an even number on the other side.”. It is
based on conditional reasoning and to complete this task, th ere can be four
possibilities –
a.) Abstract version : Each card has the letter A or B on one side and the
number 1 or 2 on the other side. Rule: If a card has a ‘1’ on one side it has
an ‘A’ on the other side.
b.) Concrete version : Each card represents a journey and has a
destination on one side and a means of transport on the other side. Rule: If
a card has a ‘Churchgate’ written on one side it has a ‘Train’ written on
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112 c.) Drinking rule : Each card has a person’s age on one side and what he
is drinking in a bar on the other side. Rule: If someone is drinking alcohol
they must be of age 18 or over.
d.) Negative abstract version : Each card has the letter A or B on one
side and the number 1 or 2 on the other side. Rule: If a card has a ‘1’ on
one side it does not have a ‘B’ on the other side.
These proposed rules or hypotheses can never be absolutely verified, but
can be falsified. Since you can verify only a limited number of instances,
there is always a possibility of encountering an instance that does not
follow the rule. For example, if we hypothesize that “Indians like
panipuri”, we may verify this with several Indians ; however there is
always a possibility of encountering an Indian who does not like panipuri.
So, it is not possible to absolutely verify t he rule, but if a person does not
like panipuri, it is easy to falsify the rule. This is the general characteristic
of universal hypotheses. Philosopher Karl Popper (1959) has emphasized
on the logic of falsifying hypotheses.
In vase of above task, for e xamplethe rule, ‘If vowel on one side, then
even number on the other side’, can be tested by using the cards showing
‘E’ and ‘7’ because they could falsify the rule (if E does not have an even
number on the other side and if 7 has a vowel on the other side) . The ‘4’
and the ‘K’ cards may be left unturned since whatever is on their other
sides would be consistent with given rule.
8.2.1Basic results
While testing the conditional rule ‘if p then q’ there can be four
possibilities as shown in following boxes -





In these four possibilities only the second one ‘p and not q’ is not in
consonance with the rule, rest of them are in accordance to the rule. It has
been observed in studies that when participants are asked to test the given
conditional rule ‘if p then q’ on a four card task, they very frequently
choose the first option, i.e., ‘p, q’ instead of ‘p, not q’. The reason is that
people tend to be biased towards verification or confirmation, so they tend
to choose potentially confirming card (p, q) and ignore the potentially
falsifying card (p, not q). In other words, a card having ‘p’ on face may
have ‘q’ on the reverse side (potentially confirming) or it may have ‘not q’
which is potentially falsifying. They understand that if a card having ‘not
q’ on face has ‘p’ on the reverse side will falsify the rule and yet they tend
to choose a card with ‘p’ on face side.

8.2.2 Procedural variations
Wason et.al. (1969) tried to find out the procedural variables that may be
making the task difficult for the part icipants. In one study , they presented P, q P, not q not p, q not p, not q
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113 strictly ‘Vowel – even number’ cards as shown below. These cards had
lots of possible combinations but that did not create any confusion in the
minds of the participants.



“If a card has a vowel on one side, t hen it must have an even number on
the other side ”Which cards must be turned over to test this rule?
Later, Wason and Johnson -Laird (1970) thought that may be participants
were confused with the expression ‘ the other side of the card’ in the
instructions and might have interpreted it to mean ‘the side face
downward’. So, they conducted another experiment, in which they
presented cards that had all the information on one side and used masks to
hide the appropriate part of the card. The results were no diffe rent from
the previous experiment. So they conducted another experiment in which
the instructions were changed and participants were asked to pick up a
card which ‘could break the rule’. Still there was no change in the
performance of the participants.
However, when Wason and Shapiro (1971) conducted another research by
using concrete material on the cards instead of abstract material, they
found the results were different from the previous experiments. In the
experiment with concrete material, participant s were given information
about four journeys. Cards carried the names of the destination towns, on
one side, and the mode of the transport on the other side. For example –


In this experiment they specified the rule that ‘Every time I go to
Mancheste r, I go by train’. To get right answer, the participants needed to
turn over Manchester and Car card, and most of them got it right. Later
onGilhooly and Falconer (1974) also got similar results when they
replicated the study.
Encouraged by these results, Johnson -Laird et al. (1972) conducted
another experiment. In this experiment, they made the task not only
concrete but also life like. They asked the participants to imagine that they
work in the Post Office and their job is to sort the letters. They have to
find out whether the following rule had been broken.
‘If a letter is sealed, then it has a 5 penny stamp on it.’
They were familiar with this rule as it was in practice in real life too in
UK. Four different envelops were given to them . The four types of
envelopes were either sealed or not sealed and had either 4 penny stamp or
5 penny stamp.
A E 4 7
Manchester Car Train Leeds
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114



Apart from this concrete condition, they simultaneously presented an
abstract condition task too. In abstract condition, the rule was ‘If an
envelope ha s a D on one side, then it has a 5 on the other side.’






Surprisingly, participants performed better on selection tasks where
concrete and realistic material was used rather than on abstract material
selection tasks.

8.2.3 Interpretation factors

Many psychologists argued that Wason’s four card task has lot of
ambiguity. It is possible that participants make interpretations that are
different from what was intended but while giving reasons for their
interpretation they give correct reasons.

For example, Smalley(1974) gave three different sources of ambiguity –
1. Is the rule ‘reversible’ or not? i.e. does p – q also mean q – p or not?
2. Does the rule refer to both sides of the card or just to the showing side?
3. Is the task one of verification , falsification or both?

These ambiguities can lead to 12 different possible interpretations. In
Smalley’s study, such different interpretations did occur and the
participants’ choices were consistent with their interpretations.

In another study , Bracew ell (1974) gave a ‘clarified’ statement of the task
and the rule given was
‘If either the showing face or the underside face of the card has a J on it
then 2 is on the remaining face. This hypothesis should not be interpreted
to mean that 2 only occurs wi th J.Please indicate the card or cards it is
necessary to examine in order to see if the above hypothesis is false.’
The results of this study showed that success rate with clarified
instructions was much higher than the other studies where standard
instru ctions were used. It was further argued that realistic concrete
material too gives better results than abstract material because participants
can see the illogical aspect of interpreting a reverse rule. For example, if
the rule says, ‘If I go to London, I go by car’, it is very unlikely that
people will interpret it in reverse order, ‘If I go by car, I go to London”. In Sealed Not sealed 4 penny stamp 5 penny
stamp
A D 5 7
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115 case of abstract rule, when participants are thinking of the right answer to
a task, they will not use such plausible checks while reversin g the abstract
rule. In abstract rule p and q appears to be logical if it is reversed to q and
p. For the same reason, participants were found to perform better on
drinking rule task mentioned above.
8.2.4 Matching bias
Matching bias in the four -card task refers to choosing the card mentioned
in the rule. Evans(1984) pointed out that when people have to select a card
in abstract version, they tend to show ‘matching bias’. That means they
simply select the cards that show the symbols mentioned in the rule or we
can say that responses match the input and no ‘deeper’ processing takes
place. For example, when a negative form of the rule was used such as “ If
B on one side, there will not be 3 on the other’ side, the success rate was
very high. Most of the partic ipants chose card ‘B’ and ‘3” which was the
correct potentially falsifying choice pattern. This occurred due to fact the
participants simply matched the information and did not show any specific
insight into the logic of the task. The same participants mad e errors
consistent with matching hypothesis when tested with positive version of
the rule.
8.2.5 Memory -Cueing (Availability) Accounts
Griggs, R.A., & Cox, J.R. (1982) proposed that performance on the
selection task is facilitated when the presentation of the task allows the
participant to recall past experience with the content of the problem, the
relationship expressed and the counter example to the rule monitoring the
relationship. They conducted an experiment having a rule determining the
legal drinkin g age in Florida. Participants were asked to consider
themselves as police officers and their task was to enforce the rule, ‘If a
person is drinking beer, then the person must be over 19”. The experiment
followed the four card task with age on one side and drink on the other.



The task was to indicate the card that definitely needed to be turned over
to determine whether the rule was being violated. The results showed that
75 percent participants made right choices. This supported the memory -
cueing p roposition.
8.2.6 Pragmatic Reasoning Schemas

So far we have discussed how memory cueing influences the performance
on conditional rule testing tasks, especially on tasks that involve abstract
conditions . Cheng &Holyoak (1985) proposed another possible factor that
may influence the performance and that is pragmatic reasoning schemas.
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116 They argued that people solve the real -world versions of the task using
pragmatic reasoning schemas which are not so abstract. They suggested
that though there are many ty pes of schema s, the crux of four card
problem is the ‘permission schema’. Permission schema basically means
‘If a person satisfies condition A, they have permission to carry out action
B’. They believed that if permission schema is activated, it will impr ove
performance in a four -card task.

For example, in the abstract problem, participants were not encouraged to
activate permission schemas and therefore their performance was
comparatively poorer than in the drinking problem, where people were
encouraged to activate permission schemas. In drinking problem, the
participants could think about whether people drinking beer had
permission to do so or not.
Cheng and Holyoak (1985) conducted a study to examine the effect of
permission schema. The participantswere instructed to imagine that they
are an immigration officer at the International Airport and they have to
check the documents of the passengers. In those documents, they were
asked to check a sheet called Form H. One side of the form indicates the
whether the passenger is entering the country or in transit and the other
side of the form was a list of tropical diseases. They were instructed that
‘If the form says “ENTERING” on one side, then the other side includes
Cholera among the list of diseases’. Which of the following forms would
you have to turn over to check? Indicate only those that you need to check
to be sure. There were 4 possibilities based on p, q, not -p, and not -q.
Half of the participants were given the explanation for ‘cholera’ rule by
saying that one side of the form indicates whether the passenger is
entering the country and the other side of the form lists inoculations the
travelers had received in the past 6 months. You have to make sure that if
the form says “entering” on one side, that t he other side includes cholera
among the list of diseases. This is to ensure that the entering passengers
are protected against the disease. It was expected that this explanation
would invoke the ‘permission schema’ and participants will show a
remarkable improvement in their performance when the rationale for the
rule is given. Results supported this assumption. In abstract version only
56 percent gave correct answers while in permission schema condition and
rationale given for that 91 percent participants gave correct answer.
This result was not consistent with the memory -cueing explanation since
participants did not have relevant memories; nor was it consistent with the
syntactic rule view, since the logical structure of the task is not affected by
the ra tionale. The result was consistent with the pragmatic reasoning
schema approach.
8.2.7 Social Contract Theory
Cosmides (1989) has been conducting research to understand reasoning
from evolutionary perspective. She proposed that people have many innate
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117 problems that are very important for the survival of many generations. She
concluded from her research that social contract cannot evolve or sustain
in a social group unless the cognitive machinery of the participants allows
a potential cooperator to detect individuals who cheat, so that they can be
excluded from future interactions in which they would exploit cooperators.
A cheater can be defined as an individual who accepts a benefit with out
satisfying the requirements that provision of that benefit was made
contingent upon. For example, suppose a person who has agreed to
contribute equally to a group assignment is found to enjoy the credits of
doing the group project without working for i t at all will be called a cheat.
Cosmides proposed that humans have evolved so that they possess a
‘cheat detecting algorithm’ to detect such possible cases of cheating.
Now the question arises, how this evolutionary cognitive concept fit in our
selection theory. Cosmides believed that when four card task with
thematic material fits the social contract pattern, it produces high rate of
correct(falsifying) answers. Griggs and Cox (1982) also demonstrated the
support for social contract theory. They showed t hat a cost has to be paid
in terms of waiting to beold enough or in terms of money before a benefit
can be taken, i.e., drinking beer.
Later on, in 1989, Cosmides compared availability and pragmatic schema
approach with social contract theory.

Social con tract and Availability Approach –
Availability theory assumes that participants are influenced by their
familiarity with the content of a rule. The more exposures a subject has
had, say for example, to P and Q, the stronger that association will be and
the more easily P and Q will come to his mind and will be “available” as a
response

Availability predicts a low percentage of logically falsifying, P & not -Q' ,
responses for all unfamiliar rules, whether they are social contracts or not,
and does not predic t the response 'not -P & Q’ under any circumstance.
Social contract theory predicts a high percentage of 'P & not -Qt responses
to "standard" social contracts, and a high percentage of 'not -P & Q'
responses to "switched" social contracts -- no matter how unf amiliar the
social contracts are.
Cosmides made problems which had unfamiliar social contracts,
unfamiliar descriptive rules, familiar descriptive rules and abstract rules.
For example, in case of unfamiliar social contract, she made the following
Cassava rule –
Cassava rule:
If a man eats cassava root, then he must have a tattoo on his face.
The cassava rule was explained in a context story as a social contract in a
tribe called the Kaluame. The cassava root is a powerful aphrodisiac that
is given only to married men and only married men are tattooed. The
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118 of sexual relations between unmarried people. Many unmarried men,
however, are tempted to cheat. Participants were asked to ensu re this rule.
They were presented with four cardsindicating information about four
young Kaluame men. Each card represents one man. One side of the card
tells which food a man is eating, and the other side of the card tells
whether or not the man has a tat too on his face. Indicate only the card(s)
you definitely need to turn over to see if any of these Kaluame men violate
the rule.



P Not -Q Not -P Q

Since cheating means taking the benefit P and not meeting the requirement
Q, subjects should select P & not -Q. In fact, about 70% of Cosmides’
subjects selected P & not -Q in this social contract problem.

In case of familiar description, she made a “transportation rule”.
Tran sportation rule:“If a person goes into Boston, then he takes the
subway.” The places and means of transport were familiar to the
participants. The four cards had information about where a person went
and how the person got there.
The abstract version was similar to Wason’s original problem. In all cases
a ‘detective’ type of set was induced to encourage participants to look for
violations of the rules. The transportation rule was not a social contract.
There were no two people who engaged in a contract, no r was P a benefit
for one person and a cost for the other, nor did this hold for Q. Therefore,
social contract theory was not applicablei n this problem.
Overall results of these experiments indicated that there was a high rate of
falsifying(p and not q) in the unfamiliar social contract condition (70%),a
low rate of falsifying with unfamiliar descriptive problems (23%)a
medium rate of falsifying with familiar descriptive problems (42%).
Cosmides (1989) further tested social contract theory with ‘switched’
social Contracts.The switched version of Cassava root rule states ‘If a man
has a tattoo on his face then he eats cassava root’
Participants were tested with the switched social contract, unfamiliar
descriptive rules, familiar descriptive rules and abstrac t descriptive rules.
The results indicated a high rate of the not -p and q choices for the
switched social contract (70 per cent) with a near zero rate of such
responses in the other conditions. This further supported the social
contract theory.
Eats
Cassava
Root No Tattoo Eats Molo
Nuts Tattoo
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119 Social con tract and Pragmatic schema approach
Cheng &Holyoak (1985) proposed that people reason using pragmatic
reasoning schemas which are abstract knowledge structures induced from
ordinary life experiences such as “permission,” “causation,” etc.
Cosmides said th at though all social contracts are ‘permissions’ not all
permissions are social contracts, since social contracts always involve
costs and benefits while permissions as a class do not always do so.
Permission rules are beneficial only when they include cos ts and benefits.
In other words, permission rules need to be in the form of social contract
to be effective. To prove her point of view, Cosmides conducted an
experiment in which the same rules were framed by means of background
stories as either social c ontracts (in which the actions were taking benefits
and the preconditions were costs to be met) or as permissions where the
same actions and preconditions were without costs or benefits to the
individuals. The results showed that falsifying choices (p and not-q) were
more frequent for the social contract version than for the permission
version (80 per cent vs. 45 per cent). Thus, it has been noted that
Cosmides’s evolutionary approach identifies rules that reliably produce
response patterns that match falsi fication choices (p and not -q) or if
switched will produce choices unlikely to occur in the standard abstract
version (not -p and q).
Studies conducted on social contract theory and pragmatic reasoning
schema approach have shown the effectiveness of deontic rules on four
card tasks. Deontic rules refer to obligations involving terms like should,
ought, must, etc. The very fact that people’s selection task choices are as
per deontic rules included in the social contract theory indicates that either
human brai ns got hardwired to special purpose mechanisms due to
evolutionary pressure or they acquire such practical knowledge in the
normal development process through general purpose learning
mechanisms.
8.2.8 The selection task as optimal data selection
In the be ginning, Wason’s studies were inspired by Popper’s (1959)
notion that seeking falsification was the rational way to test scientific
hypotheses or any causal or indicative hypothesis. However, studies have
shown that very few people on their own instantly a dopt a falsifying
approach to the standard abstract selection task. Researchers have been
attributing people’s disinclination to falsification as a sign of imperfect
rationality.
Oaksford and Chater (1994) rejected logicism and considered falsification
philosophy of science as an outdated model and gave an alternative
normative approach. This approach used a statistical rule called Bayes’s
theorem. Bayesian model gives a rational analysis of the selection task that
fits well with people's performance on bo th abstract and thematic versions
of the task.
The model suggests that reasoning in these tasks may be rational rather
than subject to systematic bias. Oaksford and Chater (1994) said that munotes.in

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120 applying Bayesian model to the selection task involves specif ying the
alternative hypotheses that participants must choose between and
define them in terms of their probability. So, for the selection task, you
require 2 hypothesis –
Hypothesis 1 - ‘if a card has p on one side then it has q on the other side ’.
The implied rule is ‘if p then q’. That means p and q are dependent.
Hypothesis 2 – There is no relationship between p and q. That means they
are independent. This is also called null hypothesis. In which case the
implied rule ‘if p then q’ will be fals e. In this the probability of not -q when
p can be more than zero and the probability of q could be less than the
probability of p.
By using Bayes’s theorem the researcher can revise the probabilities of
hypotheses in the light of data which are more or l ess likely if the
hypotheses are true. Oaksford and Chater proclaimed and found that if we
believe right from the beginning that there is an equal possibility of
proposed rule and null hypothesis being true and there are very low
chances of p’s and q’s, th en predicted preference order of card choices
then is p > q > not -q > not -p order.
Several studies have been conducted thereafter to test this claim. Oberauer
et al. (2004), compared the optimal data selection model with direct test.
They observed that in the optimal data selection model, people believe
even the most rare event as most informative and make predictions on the
basis of that information. On the other hand, in case of direct test,
participants were given lot of experience of stimuli having a c ombination
of rate and common features and then given a four card task having rare
and common features. The results did not show that four card tasks were
related to the experienced frequencies. Thus, these results did not support
the optimal data selecti on model and its supporting studies. Another
criticism against optimal data selection model was that it was not specified
how the selections were made.
8.3 GENERATING AND TESTING HYPOTHESES
Generally, in studies of conditional rule testing, people aregiv en a rule and
possible evidence which may or may not support or disconfirm the rule.
Usually, in real -life situations we are not given rules to test but must
generate possible rules (hypotheses) first which can then be tested.
Basically, two main approache s have been used to test the process of
generating and testing self -produced hypotheses. These are (a.) Wason’s
reversed 20 questions task, and (b.) performance in simulated research
environments. Let us look at each one of them.
8.4 WASON’S REVERSED 20 QU ESTIONS TASK
In 1960, Wason published a paper in which he outlined his experiment
about testing the inductive reasoning. He devised a task called 2-4-6 task.
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121 In this experiment, pa rticipants were told that experimenter has a rule in
his mind that applies to only set of threes. Participants were given these
three numbers 2 -4-6 and asked to discover the rule that experimenter had
in his mind. To do this, they had to generate a differe nt three number
series that might or might not match with the rule. The participants were
expected to announce their rule when they were very sure that they have
got the right answer. The “2 -4-6” rule the experimenter had in mind was
“any ascending sequenc e”. That means the correct rule was numbers in
increasing order of magnitude. In most cases, participants not only formed
hypotheses that were more restrictive, e.g., they formed hypotheses having
an intervals of 2 between increasing numbers or they formed ‘ arithmetic
series’. Not only that but participants kept on generating hypotheses that
were consistent with their previously set hypothesis. Very few participants
either tried out series that went against their own hypotheses or
spontaneously varied their hypotheses. This further supported Wason’s
hypothesis of confirmation bias. Wason varied the experiment a bit and
imposed a fine of 12.5 pence(money) for every incorrect rule
announcement. This made participants cautious but did not change their
confirmati on bias(a tendency to seek out and attend only to information
consistent with the hypothesis while ignoring falsifying information).
Tukey (1986) differed from Wason’s conclusion that people do not behave
rationally, rather he believed that participants d o behave rationally in
terms of various alternative philosophies of science. His study highlighted
that participants were either not always testing particular hypotheses on
each trial, but would quite often be examining instances ‘at random’ or
they were ‘ different’ in gathering information that could lead to useful
hypotheses. In conclusion, he said that people appear to be irrational if
Popperian philosophy of science is applied to the task, but if alternative
approach to science testing is used then part icipants behaviour appears to
be rational and intelligible.
8.5 SIMULATED RESEARCH ENVIRONMENTS

Though theoretically it is emphasized that more than one hypothesis
should be considered at a time and falsification is important to verify the
hypotheses, Was on et.al. showed through their experiments that most of
the participants do not pay attention to alternative hypothesis and do not
try to get potentially falsifying data. In other words, confirmation bias
takes place.

Mynatt et.al. (1977,1978) conducted two experiments to verify this claim.
In their 1977 study, they presented participants with a set of various
shapes (such as triangles, circles and squares) displayed on computer in
varying degrees of brightness (dim to bright) and moving particles whose
motion was influenced by the objects. After observing the particle’s
movements in this universe, they were asked to produce a hypothesis that
can explain the behaviour of the particle. They were allowed to make a
hypothesis on the basis of the particle’s b ehaviour with one particular
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122 such a way that it favoured forming of wrong hypothesis in terms of object
shape. After that they were asked to test the hypothesis in various
environments . They were presented with two environments – one in which
their observations could confirm the wrong hypothesis and second in
which they could test alternative hypotheses. The idea was to see which
environment they would choose. The results showed that th ey did not
choose the second environment where they could test the alternative
hypotheses. They showed confirmation bias by choosing the first
environment where they could merely confirm their wrong hypothesis. But
if they got explicit information that cou ld falsify the initial hypothesis,
they used it to reject the incorrect or wrong hypothesis. Instructions given
to either emphasizing confirmation or disconfirmation had no effect on the
participants’ behaviour.

In their 1978 study, Mynatt et al. allowed participants to explore a very
complex environment of 27 objects that differed in shape, size and
brightness. The particles were deflected on approaching the objects. The
angles of deflection were governed by a formula. In this experiment too,
confirmatio n bias took place and participants did not make any attempt to
falsify the hypotheses. Dunbar(1993) also got similar result in his study.

8.6 SUMMARY

In this unit we discussed how hypotheses are tested and how hypotheses
are generated. Hypothesis testing refers to assessing hypotheses for
truth/falsity against data. Hypothesis generation refers to deriving possible
hypotheses from data for later testing. Wason’s four card selection task
was discussed in detail to describe hypothetico reasoning. Hypothetic o
reasoning refers to both deductive and inductive reasoning but Wason’s
emphasis was on checking out the falsification process while assessing the
hypotheses. He used card selection tasks in various ways. Four of the main
variants were abstract version, c oncrete version, drinking rule, and
negative abstract version. His basic results showed that participants had
confirmation bias and ignored the falsifying data.
Along with his team, Wason further checked the variables that might lead
participants’ difficu lty in making correct decisions. They suspected that
when information was presented on both sides of the cards and
instructions included the phrase ‘the other side of the card’, participants
may get confused and make mistakes. So, the information was prese nted
on only the front side of the card masked to hide the appropriate part of
the card. Yet they found no difference in the results in both conditions.
They further investigated what happens when the instructions emphasize
on falsification. That also did not change the performance of the
participants. However, Wason and Shapiro (1971) found significant
improvement in the performance when four card selection task was
presented in concrete version. Bracewell (1974) found that results
improved significantly w hen absolutely clear instructions were given,
especially in concrete version. Evans(1984) held that in abstract version,
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123 the symbols mentioned in the rule. Johnson -Laird et al. (19 72) found that
memory cueing or availability of the information from past experience
helps in making correct choices. Cheng and Holyoak’s (1985) showed the
role of permission schema and rationale given for the rule resulted in
dramatic improvement in corre ct answers. Cosmides(1989) believed that
human beings are evolutionary wired to have ‘cheat detecting algorithm’
and social contract pattern produce high rates of correct (falsifying)
answers. Oaksford and Chater (1994) proposed comparing null and
alternat ive hypotheses by using Bayes’s theorem.
Even for hypotheses generation, Wason (1960) devised a special task in
which people had to generate over restrictive hypothesis. The results
showed that people had an overwhelming tendency to keep generating
new hy potheses consistent with their initial hypothesis. Very few
participants tried out developing hypotheses that were contradictory to
their initial hypotheses or spontaneously varied their hypotheses. Even in
simulated research environment, participants show ed confirmation bias.

8.7 QUESTIONS :

1. Discuss in detail hypothesis testing with the help of four card
selection tasks.
2. Elaborate on Cosmides’ study on social contract theory and its
implications for reasoning.
3. Discuss in detail Wason’s work on generati on of hypothesis.
4. Write a note on -
a) Basic results of Wason’s four cards study
b) Pragmatic reasoning schemas
c) confirmation bias
d) Matching bias
e) Memory cueing

8.8 REFERENCES:
 Ashcraft, M. H. &. Radvansky, G. A. (2009). Cognition. (5th ed),
Prentice Hall, Pea rson education
 Francis, G., Neath, I., &VanHorn, D. (2008). Coglab 2.0 on a CD.
Wadsworth Cengage Learning, international student edition
 Galotti, K.M. (2014). Cognitive Psychology: In and Out of the
Laboratory . (5thed.). Sage Publications (Indian repri nt 2015)
 Goldstein, E. B. (2007). Psychology of sensation and perception. New
Delhi: Cengage learning India, Indian reprint 2008
 Matlin, M.W. (2013). Cognitive Psychology, 8thed., international
student version, John Wiley & sons munotes.in

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124  Reed, S. K. (2004). Cogniti on: Theory and Applications. (6th ed.),
Wadsworth/ Thomson Learning
 Robinson -Riegler, B., & Robinson -Riegler, G. L. (2008). Cognitive
Psychology – Applying the science of the Mind. (2nded.). Pearson
Education. New Delhi: Indian edition by Dorling Kindersle y India pvt
ltd.
 Srinivasan, N., Gupta, A.K., & Pandey, J. (Eds). (2008). Advances in
Cognitive Science. Volume 1, New Delhi, Sage publications
 Sternberg, R.J. (2009). Applied Cognitive Psychology: Perceivnig,
Learning, and Remembering. New Delhi: Cengage learning India,
Indian reprint 2009
 Solso, R.L., Maclin, O.H., & Maclin, M.K. (2013). Cognitive
Psychology. Pearson education, New Delhi, first Indian reprint 2014
 Surprenant, A.M., Francis, G., & Neath, I. (2005). Coglab Reader.
Thomson Wadsworth

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