Combined Lecture Slides: Weeks 08 – 10

Combined Lecture Slides: Weeks 08 – 10
Psychology 355: Cognitive Psychology Instructor: John Miyamoto Spring 2016 Note: This Powerpoint presentation may contain macros that I wrote to help me create the slides. The macros aren’t needed to view the slides. You can disable or delete the macros without any change to the presentation.

Lecture 08 – 1 This file does not exist because 08-1 would be Monday May 16, and we had Midterm 2 on this date. Therefore there are no lecture slides for this date. Psych 355, Miyamoto, Spr '15

Next: Lecture Psych 355, Miyamoto, Spr '15

Introduction to Categorization Theory (Goldstein Ch 9: Knowledge)
Psychology 355: Cognitive Psychology Instructor: John Miyamoto 05/17/2016: Lecture 08-2 Note: This Powerpoint presentation may contain macros that I wrote to help me create the slides. The macros aren’t needed to view the slides. You can disable or delete the macros without any change to the presentation.

Lecture probably ends here
Outline Concepts, categorization, and knowledge Categorization – what is it? How are objects placed into categories? The definitional theory of categorization Problems with the definitional theory of categorization Prototype theory of categorization Exemplar theory of categorization Lecture probably ends here Categorization – Examples Psych 355, Miyamoto, Spr '16

Categorization – Examples
Categorize objects in the physical world. E.g., tables, chairs, cars, dogs, cats, trees, water, etc. Categorize types of people. E.g., policemen, teachers, students, politician, etc. E.g., friend, enemy, helpful, inconsiderate, smart, talkative, etc. E.g., white, black, asian, ...., catholic, muslim, hindu, ...., Abstract categories. "___ is a crime", e.g., theft is a crime; complaining is not a crime. "___ is a relative of mine." (Kinship is an abstraction.) "credit" in the financial sense is an abstraction. Inferences with Categorizations Psych 355, Miyamoto, Spr '16

Inferences with Categories
Example of Reasoning with Categories Type of Inference I see a dog and say, "That's a mammal/dog/collie." Categorize an object (put it into a category) A friend tells me, "I have a collie," and I think to myself, "I hope she doesn't mind dog hair on her clothes." Draw an inference from a categorization to other properties of the object. A friend tells me that fluorescent light can wreck a cd that contains data. I think to myself, I wonder if the same thing is true of dvd's? Draw an inference from a property of one category to a possible property of another category. Categories Imply a Lot of Other Information – Cat Example Psych 355, Miyamoto, Spr '16

Knowing the Category Provides a Lot of Information
Definition of Categorization Psych 355, Miyamoto, Spr '16

Categorization – What is it?
Goldstein book: Categorization "is the process by which things are placed into groups called categories." This is an incomplete definition. Below is a better definition. The psychology of categorization has to do with: how we assign objects or events to categories; the structure of knowledge that we use to organize our knowledge of categories (category structure); the inferences that we draw when we learn what categories an object or event belongs to; how we learn new categories (Children learn new categories frequently; adults learn new categories from time to time) These are all examples of semantic knowledge. Distinction Btwn Concepts & Categories Psych 355, Miyamoto, Spr '16

Categories and Concepts
The word "concept" emphasizes knowledge of relationships between concepts. E.g., the concept of a cat includes knowledge of its biology, appearance, behavior, its relation to human life styles, etc. mokita (Kivila language): “truth we all know but agree not to talk about” (Wikipedia) Concept of "time" includes knowledge of all sorts of temporal relationships, e.g., relations of "before" and "after." Related relations and concepts s of "duration," "instant", "concurrency", "distant past," "recent past." The word "category" emphasizes the set of all things that are joined together under a common label. E.g., every cat is a member of the category "cat"; every dog and every cat is a member of the category "carnivore." Relationship between Knowledge and Categories Psych 355, Miyamoto, Spr '16

Categorization and Knowledge
Goldstein calls the topic “Knowledge” – why? This is a hypothesis – not a fact. IMO: The structure of categories and the structure of concepts are related to the structure of knowledge, but none is identical to any of the others. ? Definitional Approach to Category Membership Psych 355, Miyamoto, Spr '16

Definitional Approach to Category Membership
Originated with Aristotle. According to the definitional approach, category membership is determined by checking a list of necessary and sufficient features. Example: Definition of a tea cup. Concrete object Concave Can hold liquids Has a handle Can be used to drink hot liquids Properties 4 and 5 are debatable. Chinese tea cups. Lacquer cups. If you drop 4 and 5, then there are many objects (bowls) that satisfy Problems with the Definitional Approach Psych 355, Miyamoto, Spr '16

What Do These "Chairs" All Have in Common?
Maybe there is nothing that ALL chairs have in common, ..... but they all share a family resemblance. Problems with the Definitional Approach to Categorization Psych 355, Miyamoto, Spr '16

Problems with the Definitional Approach
How do we discover the definitions? For many categories, it is doubtful that necessary and sufficient features exist. Example: If we call someone "friendly," what are necessary and sufficient features for calling someone friendly? The definitional approach does not explain important aspects of human categorization. E.g., it does not explain typicality effects in categorization response time. See the Rosch color priming study to be described later. E.g., it does not explain typicality effects in semantic memory experiments (Goldstein calls this the "sentence verification technique"). Prototype Theory of Categorization Psych 355, Miyamoto, Spr '16

Prototype Theory of Categorization
Cognitive psychologists were dissatisfied with the definitional approach to representing cognitive categories. Eleanor Rosch: Categories are organized around a category prototype. Category prototype. Family resemblances. Next: Digression regarding family resemblances Family Resemblance Idea - Background Psych 355, Miyamoto, Spr '16

Family Resemblance Idea - Background
Composite A "family resemblance" photograph – popular in late 19th & early 20th century. Take multiple exposure photo of all family members. Only the common features are retained. The photo looks like everyone in the family, but is not any one person. Images downloaded from: Continuation of Present Slide with Composite Image Psych 355, Miyamoto, Spr '16

Family Resemblance Idea - Background
Composite A "family resemblance" photograph – popular in late 19th & early 20th century. Take multiple exposure photo of all family members. Only the common features are retained. The photo looks like everyone in the family, but is not any one person. Ludwig Wittgenstein – early 20th century philosopher. Proposed that the structure of a concept is like a family resemblance photo. Images downloaded from: Paul Wittgenstein Concert pianist Lost right arm in WW I Family Resemblance in Concept Theory Psych 355, Miyamoto, Spr '16

Family Resemblance & Concepts
Famous example: Wittgenstein says that there are no necessary and sufficient conditions for something to be a "game", but there is a family resemblance among games. Playing checkers or bridge; playing football; skipping rope; children play acting as if, e.g., having a tea party; trash talking Similarly, there are no necessary & sufficient conditions for something to be a chair, but there is a family resemblance among chairs. Return to Slide Showing the Prototype Theory of Categorization Psych 355, Miyamoto, Spr '16

Prototype Theory of Categorization
Eleanor Rosch proposed: cognitive representations of categories are like family resemblances. categories have prototypes – category structure is created by the relationship between category members and the category prototype. Prototypes (mental representation of a concept) are like a family resemblance photographs – they retain the typical characteristics of many particular examples. Examples of Birds & the Prototypical Bird Image Psych 355, Miyamoto, Spr '16

A Prototypical "Bird" Image (for an Urban American)
The prototypical "bird" image is simililar to the average of many bird images. Not Prototypical "Bird" Image Basic Claim of Prototype Theory Psych 355, Miyamoto, Spr '16

Basic Claim of Prototype Theory
Categorization decisions are based on the similarity of a specific instance to the prototype of a category. Is it a bird? compare Two Basic Questions (Not Answered in Psych 355) Psych 355, Miyamoto, Spr '16

Two Basic Questions (not answered in Psych 355)
"Similarity to the Prototype" – What does this mean? What does it mean to "mentally compute" the similarity between two things? How do we (psychologists) figure out the properties of a category prototype? There is no single best method. Here are two ways. Typicality Ratings: Subjects rate how typical various objects are of a category, e.g., rate the degree to which robins (or penguins) are typical birds. Prototypical objects are high in rated typicality. Property listing: Ask subjects to list the properties of category members, e.g., list the properties of "robin", "penguin", "eagle", "ostrich" "hawk", "sparrow", etc. Assumption: The commonly listed properties are the properties of the prototype. Rosch’s Color Priming Experiment Psych 355, Miyamoto, Spr '16

Evidence for a Prototype Theory of Categorization
Semantic memory experiment Goldstein calls this the sentence verification technique Subjects are faster to verify that prototypical objects are in category than non-prototypical objects. “A sparrow is a bird" – FAST; "A penguin is a bird" – SLOW Prototypical objects have high family resemblance (share many properties) with other category members. List features of sparrows – share many features with other birds List features of penguins – share few features with other birds Prototypical objects are named first if subjects are asked to list examples of a category. List types of birds: Robin, sparrow, hawk, eagle, ....., penguin, ostrich, ... Category names prime prototypical objects more than non-prototypical objects. (See next slides.) Rosch's Color Priming Experiment Psych 355, Miyamoto, Spr '16

Rosch – Priming with Color Matching Task
Task: Say "yes" if I present two identical colors Say "no" if I present two non-identical colors. Unprimed Condition Prime Stimulus Response "yes" "no" Primed Condition in the Color Matching Task Psych 355, Miyamoto, Spr '16

Rosch – Priming with Color Matching Task (cont.)
Task: Say "yes" if I present two identical colors Say "no" if I present two non-identical colors. Primed Condition (the prime is logically irrelevant) Prime Stimulus Response "green" spoken aloud "yes" "pink" spoken aloud "no" Results for Priming with Color Matching Psych 355, Miyamoto, Spr '16

Results: Priming with Color Matching Task
Effect of Priming is measured as the difference in RT between the unprimed and the primed stimulus Finding: The word "green" primes the "yes" response for matching greens, but it does this more for prototypical greens than for less typical greens. "green" spoken aloud primes "yes for more than: "green" spoken aloud primes "yes" for priming helps a lot priming helps, but not as much Theoretical Interpretation of Results for Priming & Color Matching Psych 355, Miyamoto, Spr '16

Theoretical Interpretation of Priming in Color Matching
Step 1. Subject hears the prime word "green" Theory: Subject automatically thinks of a prototypical version of the color, e.g., Step 2. Subject sees the target pair: If the target pair is close in color to the prototype, e.g., is close to , then the subject is primed to see this color so the subject is very fast to see that the colors match. If it is not close in color to the prototype, e.g., is not close to , then the subject is not as primed to see this color so the subject is slower to see that the colors match. Conclusion: The spoken category name, "green", produces a mental representation of the prototype of "green." or Summary of Prototype Theory Psych 355, Miyamoto, Spr '16

Summary of the Prototype Theory of Categorization
Basic claim: Categorization judgments are based on the similarity of an object to a prototype. EVIDENCE Semantic memory experiment (Goldstein calls this the sentence verification technique): Prototypical objects have high family resemblance (share many properties) with other category members. Prototypical objects are named first if subjects are asked to list examples of a category. Category names prime prototypical objects more than non-prototypical objects. Exemplar Theory of Categorization Psych 355, Miyamoto, Spr '16

Exemplar Theories of Categorization – Basic Idea
Competitor to prototype theories. Main claims of exemplar (instance) theories: Category judgments are not based on a process of prototype abstraction and similarity of new objects to a prototype. Category judgments are based on the similarity of new objects to a sample of previously encountered exemplars. Example of Categorization by Exemplar Model – Am I Looking at a Dog? Psych 355, Miyamoto, Spr '16

How Exemplars Lead to Categorization
Retrieve 3 Bear Exemplars Retrieve 4 Dog Exemplars What is it? Same Idea Written Out Verbally Psych 355, Miyamoto, Spr '16

Exemplar Theory of Categorization - Example
Suppose we are trying to decide whether object A is a member of category X. E.g., A is a shaggy, four-legged creature. Is A a dog? Exemplar theory says we do the following: Retrieve memories of specific dogs (exemplars) that we have encountered. Retrieve memories of relevant non-dogs, e.g., memory of a cat; memory of a stuffed animal; memory of a raccoon; etc. Mentally compute the total similarity of the current instance to memories of positive and negative exemplars (exemplars of dogs and non-dogs). Decide that A is a dog if A is more similar to the memories of dogs than to memories of relevant non-dogs. Summary of Exemplar Theory Psych 355, Miyamoto, Spr '16

Summary – Exemplar Theory also Predicts the Main Categorization Findings
Exemplar theory can predict the main findings that support prototype theory. Semantic memory experiment (sentence verification) Prototypical objects have high family resemblance (share many properties) with other category members. Prototypical objects are named first if subjects are asked to list examples of a category. Category names prime prototypical objects more than non-prototypical objects. Comparison of Prototype Theory to Exemplar Theory Psych 355, Miyamoto, Spr '16

Contrast Between Prototype Theory and Exemplar Theory
Both theories claim that categorization judgments are based on a judgment of similarity. Prototype theory – categorization based on similarity of object to the prototype of a category. Exemplar theory – categorization based on total similarity of object to exemplars of the category versus total similarity. Prototype theory – Category structure base on prototypes Exemplar theory assumes only that we can retrieve memories of specific instances of a category. Categorization based on similarity to these remembered instances. Exemplar theory – no abstraction of prototypes Maybe both theories are true but for different situations. Some evidence suggests that exemplar theory is better for small categories (U.S. presidents) than for large categories (birds). Also exemplar theory may be better for categories with lots of variation (games). Some evidence suggests that prototype theories are better as descriptions of early learning of categories, but later use of category begins to accumulate special case exceptions (exemplars). Case of Amadou Diallo Psych 355, Miyamoto, Spr '16

Case of Amadou Diallo Source: Events took place 2/4/1999. Amadou Diallo – 23 year old immigrant from Guinea. Diallo matched description of a dangerous serial rapist in NY city. 4 plain-clothes NY police officers shout at Diallo to stop & show hands Diallo ignored officers; ran up steps to porch in front of apartment; lighting on porch was bad One police officer thought Diallo had pulled out a gun, so he yelled “gun”. Officers shot at Diallo. During the shooting, one officer slipped and fell backwards, causing other officers to think he had been shot. Later it was found that Diallo was not holding a gun. He was holding his brown wallet. Officers fired 41 shot; Diallo was hit 19 times and died. Payne’s Study of Race Priming of Weapons Identification Psych 355, Miyamoto, Spr '16

Race Priming of Weapons Categorization
Priming Stimulus Test Stimuli * Payne (2001): Weapon versus non-weapon identification. Dependent variable = response time. Subjects: 24 women, 7 men (all white) What do you predict if their are automatic associations of black faces with weapons? Results for Race Priming Psych 355, Miyamoto, Spr '16

Race Priming of Weapons Categorization
Priming Stimulus Test Stimuli Payne Exp Prime is white. Prime is black. Test stimulus is a weapon > 423 Test stimulus is a tool < 454 Weapon identification is faster when the prime is black. Non-weapon identification is faster when the prime is white. Diagram that Summarizes the Theoretical Interpretation of This Finding Psych 355, Miyamoto, Spr '16

Theoretical Model for the Race Priming Experiment
Access the Prototype for the Race & Gender of Individual Prime Other Judgments About the Individual Perceiving the Race of an Individual Main Point: Categorizations can affect important decisions. END Psych 355, Miyamoto, Spr '16

Representational Hypotheses in Cognitive Psychology

Outline Representational hypotheses - what are they? The centrality of human language capacity in the theory of cognition Semantic network models - a very brief discussion Is there a basic level in human conceptual representations? Neuropsychological representations of categories and concepts Lecture probably ends here Cognitive Theory = { Representational Hypotheses + Processing Hypotheses } Psych 355, Miyamoto, Spr ‘16

Representational Hypotheses in Cog Psych
Assumption: Human perception, memory, language, and reasoning are based on the ability to create, store, retrieve and manipulate a variety of mental representations. Representational Hypotheses Cognitive Theory = Plus Processing Hypotheses Mental Representations & Their Uses Psych 355, Miyamoto, Spr '15

Levels of Representation in Language
Theories of human language capacity propose representational hypotheses at many levels. Articulatory and acoustical phonetics - the study of the muscular coordination in speech and acoustical patterns in the speech signal. Phonology - the study of the sound structures of languages. Syntax - theories of sentence structure and its relation to speech context and meaning. Semantics - theories of the relation between language structure and meaning. Language-Like Representations in Cognition Psych 355,, Miyamoto, Spr '16

Symbolic Representations in Cognition
The motor theory of speech comprehension: A link between speech production and speech comprehension. Models of language syntax Cognitive, computational and/or neuropsych models of language processing with emphasis on sentence structure. Inference in language use - is it a logic? is it a computer program? The modeling of human language capacity is a major concern of cognitive science. cog psych is part of cognitive science; other parts include philosophy, linguistics, computer science, neuroscience, anthropology Semantic Networks & Connectionist Models Psych 355,, Miyamoto, Spr '16

Semantic Networks & Connectionist Models
SKIM Semantic networks & connectionist models – two different approaches to modeling concept structure. Semantic networks & connectionist models use different formalisms. Spreading activation – activation of some concepts causes activation in associated concepts. Connectionist models focus on learning concept structure. [This topic will be omitted from Psych 355 this quarter.] Semantic networks focus on modeling human concept structure without modeling how we learn this structure. Psych 355 will briefly discuss semantic network models and ignore connectionist models. Semantic Network Models Psych 355, Miyamoto, Spr '16

Semantic Network Models
Concepts are arranged in networks that represent the way concepts are organized in the mind. Any specific network model must explain: what is the relationship between the structure of the network and human thinking or behavior; what are the rules by which representations are constructed within the model; how to derive predictions from the model to different aspects of human behavior. Example of a Semantic Network Psych 355, Miyamoto, Spr '16

Example of a Semantic Network
SKIM Goldstein Figure 9.12 Collins and Quillian (1969) Model for how concepts and properties are associated in the mind Node = category/concept Concepts are linked Cognitive economy: shared properties are only stored at higher-level nodes Exceptions are stored at lower nodes Inheritance Lower-level items share properties of higher-level items Psych 355, Miyamoto, Spr '16

Using a Semantic Network to Predict Sentence Verification
Goldstein Figure 9.13 (top) & 9.14 (bottom) Collins & Quillian (1969) Measure response time to verify whether sentence is TRUE or FALSE ("sentence verification" or "semantic memory") Spreading Activation Theory: Activation is the arousal level of a node When a node is activated, activity spreads out along all connected links Concepts that receive activation are primed and more easily accessed from memory SKIM Summary re Semantic Networks Psych 355, Miyamoto, Spr '16

Summary re Semantic Network Models
Concepts are represented as nodes in a semantic network. What do semantic network models (SNM's) do? SNM's explain the relationship between the network structure and human reasoning and behavior. SNM's yield predictions for cognitive experiments like semantic memory experiments, categorization judgments and causal inferences. SNM's can be part of a learning model that predict how a learner learns new concepts and conceptual relationships. There are many different SNM's that are devised for different research problems. They tend to be complicated. Reminder: Cat Picture - Transition to Basic Levels in Categorization Psych 355,, Miyamoto, Spr '16

Knowing the Category Provides a Lot of Information
Is There a Basic Level of Categorization? Psych 355, Miyamoto, Spr '16

Is There a Basic Level in Categorization?
Categories can often be arranged from higher-level superordinate categories (more inclusive) to lower-level subordinate categories (less inclusive; more specific). Eleanor Rosch asked: Is there a basic level of categorization at which we habitually categorize the objects in our experience? Basic Level Versus Superordinate Level: Amount of Feature Sharing Psych 355, Miyamoto, Spr '16

What Defines the Basic Level of Categorization?
Hypothesis: The basic level is the highest level at which category members share many features within the category while also being very different from objects in other categories at the same level. Two Factors that Determine the Basic Level: Within a basic level category, objects share many features with each other. Between different categories at the basic level, objects are very different from each other. Basic Level Versus Subordinate Level: Amount of Feature Sharing Psych 355, Miyamoto, Spr '16

Hypothesis: The basic level is the highest level at which category members share many features within the category while also being very different from objects in other categories at the same level. Two Factors that Determine the Basic Level: Within a basic level category, objects share many features with each other. Between different categories at the basic level, objects are very different from each other. Same Slide Without Emphasis Rectangles Psych 355, Miyamoto, Spr '16

Hypothesis: The basic level is the highest level at which category members share many features within the category while also being very different from objects in other categories at the same level. Two Factors that Determine the Basic Level: Within a basic level category, objects share many features with each other. Between different categories at the basic level, objects are very different from each other. Information Gain at Different Levels of Categorization Psych 355, Miyamoto, Spr '16

Information Provided by Categorizations at Different Levels
If you categorize something at the superordinate level, e.g., "furniture," instead of at the basic level, e.g., "table," you lose a lot of information about the object. If you categorize something at a subordinate level, e.g., "kitchen table," instead of at the basic level, e.g., "table," you don't gain a lot of information about the object. Concrete Example with Images of a Bull Do and a Pickup Truck Psych 355, Miyamoto, Spr '16

What Is It? Possible Answers Possible Answers An animal A vehicle
Superordinate level A vehicle Superordinate level A dog Basic level A truck Basic level A bulldog Subordinate level A pickup truck Subordinate level Return to Diagram Showing Superordinate, Basic & Subordinate Levels Psych 355, Miyamoto, Spr '16

Information Provided by Categorizations at Different Levels
The basic level is the level at which we typically categorize everyday objects. We are capable of categorizing objects at superordinate or subordinate levels, if required to do so by a situation or task. Criteria for Basic Level: Feature Listing Psych 355, Miyamoto, Spr '16

Behavioral Criteria for Basic Level Categories
Skim Criterion 1 (Feature-Listing): Ask subjects to list features of objects in a category Category Name = (fill in the blank, e.g., “furniture” or “chair”) Instruction: “List as many characteristics or attributes that you can think of which describe the object.” Examples Categorization: FURNITURE “List as many characteristics or attributes that you can think of which describe furniture.” Categorization: CHAIR “List as many characteristics or attributes that you can think of which describe chairs.” Categorization: ARMCHAIR Superordinate Level Basic Level Subordinate Level The instructions for the feature listing task were taken from Exp 1 of Tanaka & Taylor (1991), cited in the notes to the next slide. Behavioral Criteria: Basic Level has Greatest Gain in Features Psych 355, Miyamoto, Spr '16

Behavioral Criteria for Basic Level Categories (cont.)
Skim Criterion 1 (Feature-Listing): Ask subjects to list features of objects in a category Category Name = (fill in the blank, e.g., “furniture” or “chair”) Instruction: “List as many characteristics or attributes that you can think of which describe the object.” Characteristic of the basic level: Superordinate level – relatively few features are listed (not many features of “furniture”) Basic level – many features are listed (Many features associated with “chairs”) Subordinate level – may have more features than the basic level but the increase is relatively small (Features listed for “armchair” are not many more than features listed for “chair”) The instructions for the feature listing task were taken from Exp 1 of Tanaka & Taylor (1991), cited in the notes to the next slide. Behavioral Criteria: Free Naming Psych 355, Miyamoto, Spr '16

Behavioral Criteria for Basic Level Categories (cont.)
Skim Criterion 2 (Free Naming): Ask subjects to name a picture of an object Instruction: “What would you call this?” [show image of an object] Characteristics of the Basic Level: The basic level is the most commonly used category label. Example Show subject a picture of an armchair. The response “chair” is more likely than the response “furniture”. The response “chair” is more likely than the response “armchair”. The instructions for the feature listing task were taken from Exp 1 of Tanaka & Taylor (1991), cited in the notes to the next slide. Behavioral Criteria: Category Verification Time Psych 355, Miyamoto, Spr '16

Skim Criterion 3 (Category Verification): Measure response time for deciding whether an image is an example of a given category Examples 1st subject hears “FURNITURE” Next subject sees picture of an armchair. Correct response = TRUE 1st subject hears “CHAIR” 1st subject hears “ARMCHAIR” Superordinate Level Basic Level Subordinate Level The instructions for the feature listing task were taken from Exp 1 of Tanaka & Taylor (1991), cited in the notes to the next slide. Behavioral Criteria: Category Verification Time – Basic level Fastest Psych 355, Miyamoto, Spr '16

Skim Criterion 3 (Category Verification): Measure response time for deciding whether an image is an example of a given category Characteristic of the Basic Level: Categorization decisions are fastest at the basic level. 1st subject hears “FURNITURE” Next subject sees picture of an armchair. Correct response = TRUE 1st subject hears “CHAIR” 1st subject hears “ARMCHAIR” Superordinate Level Basic Level Subordinate Level The instructions for the feature listing task were taken from Exp 1 of Tanaka & Taylor (1991), cited in the notes to the next slide. Slower Categorization Response Fastest Categorization Response Fastest Categorization Response Slower Categorization Response Behavioral Criteria: Summary Psych 355, Miyamoto, Spr '16

Summary: Behavioral Criteria for Basic Level Categories .
Skim Criterion 1 (Feature-Listing) Criterion 2 (Free Naming) Criterion 3 (Category Verification) The instructions for the feature listing task were taken from Exp 1 of Tanaka & Taylor (1991), cited in the notes to the next slide. Expert Versus Non-Experts Psych 355, Miyamoto, Spr '16

The Basic Level May Not Be the Same for Experts & Non-Experts
Tanaka and Taylor (1991): Bird experts use specific bird species, e.g., "eagle", "hawk", etc., as if they are the basic level. For the average person, "bird" is basic level, but not for bird experts. Experts Non-Experts Tanaka, J. W., & Taylor, M. (1991). Object categories and expertise: Is the basic level in the eye of the beholder? Cognitive Psychology, 23, basic specific Same Slide Without Emphasis Rectangles Psych 355, Miyamoto, Spr '16

The Basic Level May Not Be the Same for Experts & Non-Experts
Tanaka and Taylor (1991): Bird experts use specific bird species, e.g., "eagle", "hawk", etc., as if they are the basic level. For the average person, "bird" is basic level, but not for bird experts. basic specific There May Be Cultural Differences in Categorization Psych 355, Miyamoto, Spr '16

There can be systematic cultural differences in category knowledge
Medin et al. compared Native American (Menominee Indian) and Euro- American fish experts (fisherman). Euro-American experts tended to sort fish into goal-related categories, e.g., game fish and non-game fish. "Native American fish experts ... tended to sort ecologically and were more likely to see positive and reciprocal ecological relations," e.g., fish that live together or live in predator-prey relations. Itza Maya in Guatemala categorize birds at a lower level than do typical Americans. Basic level for Itza Maya is the subordinate level for Americans. E.g., Itza Maya would say "red hawk" where an American would say "bird." Summary re Category Structure Psych 355, Miyamoto, Spr '16

Summary re Category Structure
The "basic level" of categorization is the level of category structure that is usually most useful for members of a given culture. The objects that are grouped together in a basic level category .... share many properties with other objects in the category, i.e., different chairs share many important properties with each other, and .... differ in important ways from objects in other categories at the same level, e.g., chairs differ in important ways from tables, lamps, cars, etc. The basic level can differ between experts and non-experts in a domain, or between members of different cultures. What Is the Neural Representation of Category Knowledge? Psych 355, Miyamoto, Spr '16

Neuropsychological Evidence for Category Structure Then: The Functional Role of Mental Imagery

Outline Neuropsychological evidence for category representations. Mental Imagery Imagery Debate – Do mental images play a functional role in human cognition? Evidence in favor of the functional role of imagery. Are perception and imagery similar processes? Behavioral evidence Neuropsychological evidence Lecture probably ends here What Is the Neural Representation of Category Knolwedge? Psych 355, Miyamoto, Spr ‘16

What Is the Neural Representation of Category Knowledge?
Are there object-specific or person-specific neural representations? Are there specific neurons that are sensitive to specific objects or persons? Are there grandmother cells? How is knowledge of categories of objects represented at the neural level? Category Specific Neurons Psych 355, Miyamoto, Spr '16

Evidence for Category-Specific Neurons
Kreiman, G., Koch, C., & Fried, I. (2000). Single-cell recordings from 11 epilepsy patients awaiting surgery. Neurons found in the temporal lobe that respond best to specific classes of objects. Category-specific neurons for: faces; famous faces; animals; cars; buildings; spatial layouts; abstract patterns Multimodal Category Representations Psych 355, Miyamoto, Spr '16

Multimodal Category Representations
Category knowledge includes knowledge of .... an object's visual appearance, e.g., what a dog looks like; typical sounds, e.g., what kinds of sounds you would expect from a dog; function, e.g., what you would use a hammer for; smell, e.g., what a dog smells like; what a flower smells like; what a fire smells like; taste, especially true of foods typical body actions that relate to it, e.g., we have a representation of the body actions with respect to a hammer, a chair, a chest of drawers Multimodal representations involve multiple sensory modes, vision, audition, touch, kinesthesis, and so forth. Example: The Grasping Circuit Psych 355, Miyamoto, Spr '16

Example of a Multimodal Category Representation: The Grasping Circuit for Manipulable Objects
The Grasping Circuit – a neural circuit associated with use of manipulable objects, like hammers, screwdrivers, tennis rackets, etc. Involves pathways in parietal cortex Question: Are the neural circuits involved in handling a hammer part of the category representation of "hammer"? fMRI Images While Viewing Hammers, Buildings, Animals & People, Psych 355, Miyamoto, Spr '16

Neural Evidence for Multimodal Representations of Category Knowledge
Hammer use activates the left ventral premotor cortex & left posterior parietal cortex (grasping circuit). Subjects are immobile in the scanner so they could not grasp anything. Nevertheless, viewing a hammer activated the grasping circuit. Supports the hypothesis that we activate object-appropriate motor association areas when we access category knowledge. Supports the view that category knowledge is multimodal. Psych 355, Miyamoto, Spr '16 Summary: Neural Representations of Category Knowledge – END

Summary – Neural Representations of Category Knowledge
Some neurons may be specific to particular people or objects. (Controversial Issue) Some neural mechanisms are specific to particular kinds of objects. Not necessarily single neurons – the mechanism may involve distributed processing. Neural category representations are multimodal. As yet, we only have a preliminary understanding of neural representations of category knowledge. This is a good field for someone with a scientific pioneering spirit. Visual Images - What Are They? Psych 355, Miyamoto, Spr '16

Visual Images – What Are They?
Mental Imagery: Experiencing a sensory impression in the absence of sensory input Visual imagery: “seeing” in the absence of a visual stimulus Table Showing the Pro and Con View of the Functional Importance of Imagery Psych 355, Miyamoto, Spr '16

The Debate Over Mental Imagery
Pro-Mental Imagery Anti-Mental Imagery Mental images play a functional role in human cognition – they play an important role in memory, problem solving and reasoning. Advocated by Stephen Kosslyn Mental images are epiphenomenal; Mental images accompany real mechanism but plays no functional role. Cognitive theory does not need a theory of mental images. Advocated by Zenon Pylyshyn Human cognition makes use of mental images and propositional representations. All cognitive representations are propositional representation. Same Slide with Emphasis Rectangles Psych 355, Miyamoto, Spr '16

The Debate Over Mental Imagery
Pro-Mental Imagery Anti-Mental Imagery Mental images play a functional role in human cognition – they play an important role in memory, problem solving and reasoning. Advocated by Stephen Kosslyn Mental images are epiphenomenal; Mental images accompany real mechanism but plays no functional role. Cognitive theory does not need a theory of mental images. Advocated by Zenon Pylyshyn Human cognition makes use of mental images and propositional representations. All cognitive representations are propositional representation. Review: Basic Pattern of Argument in the Imagery Debate Psych 355, Miyamoto, Spr '16

Basic Approach to Arguing for the Functional Role of Mental Imagery
Find behavioral or neuropsychological evidence that is ... easy to explain if we assume that humans possess and use perception- based representations, but ... hard to explain if we assume that we have only propositional representations. In general, these arguments are not air tight, but they can be strong and convincing. Example of Ptolmaic circles in astronomy Related Hypothesis: Humans use analog representations. I.e., Humans use representations that are analogous to working with real physical objects. Review: Shepard's Mental Rotation Study Psych 355, Miyamoto, Spr '16

Shepard's Mental Rotation Experiments
TASK: As quickly as possible, decide whether the two figures have the same shape or different shapes. Mental rotation experiments: Influential argument for the importance of mental imagery in cognitive processes. Review: Mental Rotation Experiment - Results Psych 355, Miyamoto, Spr '16

Mental Rotation - Results
2-D Rotation 3-D Rotation Angle of Rotation (Degrees) Mean Reaction Time (sec.) Response time for "identical" figures is a linear (straight-line) function of the angle of rotation between the figures. Result is easy to explain if subjects are rotating a mental image. Result is hard to explain if mental representation is exclusively propositional. Image Scanning – Kosslyn's Experiment with Island Map Psych 355, Miyamoto, Spr '16

Mental Scanning (Image Scanning)
Distance (cm) Response Time (sec) Figure 10.4: Image scanning is discussed in Goldstein, pp Subjects study the map of the island. Then take it away. Using only the mental image of the island, imagine a dot moving from one point to another, e.g., from the mountain to the tree. Push a button when you are done. Zooming In or Zooming Out on a Mental Image Psych 355, Miyamoto, Spr '16

Zooming In and Zooming Out on a Mental Image
Top: Imagine a rabbit next to an elephant. Yes or No: Does the rabbit have whiskers? Bottom: Imagine a rabbit next to a fly. Same Slide with Results Added Psych 355, Miyamoto, Spr '16

Zooming In and Zooming Out on a Mental Image
Top: Imagine a rabbit next to an elephant. Yes or No: Does the rabbit have whiskers? Bottom: Imagine a rabbit next to a fly. RT result is hard to explain if you assume that information is retrieved from a semantic network without imagery. RT = 2,020 ms slower RT = 1,870 ms faster Can Imagery Prime Perception Psych 355, Miyamoto, Spr '16

Can Imagery Prime Perception?
Priming – Stimulus A can prime Stimulus B if A is similar to B. Why? Theoretical reason is that A and B activate similar brain areas or processes, so the recent activation by A facilitates the processing of Stimulus B. Question: Can imagining a visual representation prime the perception of a real visual stimulus? Farah, M. J. (1985). Psychophysical evidence for a shared representational medium for mental images and percepts. Journal of Experimental Psychology: General, 114, Farah's Demonstration that Imagery Can Prime Perception Psych 355, Miyamoto, Spr '16

Images Can Act as Primes for Real Visual Displays
H Step (a): Create mental image of H or T while staring at a blank screen. Step (b): After forming a good image, subject presses a button that causes 2 screens to be displayed one after the other. One screen has an H or a T, the other screen is blank. Task: Say whether the letter was on the 1st or the 2nd screen. "1" "2" "1" "2" Will Priming Occur? Psych 355, Miyamoto, Spr '16

Images Can Act as Primes for Real Visual Displays
H Will priming occur? Will these responses be slower? Will these responses be faster? Results of Farah's Experiment Psych 355, Miyamoto, Spr '16

Results: Images Act as Primes for Real Visual Displays
% Correct Detection Results: When mental image is same as target, percent correct is higher. Interpretation: Forming the mental image requires similar brain activity to actual perception. Therefore forming the mental image primes the perception of the target. Conclusion: Imagery Plays a Functional Role in Cognition Psych 355, Miyamoto, Spr '16

Yes – Mental Imagery Plays a Functional Role in Cognition
Lots of evidence – Image scanning (RT pattern consistent with mental image) Zooming in or out with image (RT pattern consistent with mental image) Image can prime detection task Evidence is easy to explain if we postulate that people use mental imagery; Evidence is hard to explain if we claim that people only use propositional representations. Next Question: How similar are perception and imagery? Neuropsych Evidence for Similarity Between Perception & Mental Imagery Psych 355, Miyamoto, Spr '16

Neuropsychological Evidence for Similarity Between Perception & Mental Imagery
Neural response is similar when perceiving an object or imagining the object. Single cell studies - neurons that respond to perceiving or imagining an object. fMRI studies - similar brain activity when perceiving an object or imagining the object. Transcranial magnetic stimulation (TMS) has similar effect (slower response) on tasks based on perception and on imagery. Single-Cell Evidence for Similarity Between Perception & Imagery Psych 355, Miyamoto, Spr '16

Single-Cell Studies of Perception & Imagery
Imagine the Baseball Perceive the Baseball Kreiman, Koch & Fried (2000) Single-cell recording in medial temporal lobe as precursor to surgery for epilepsy. Cell is sensitive to perception and mental image of baseballs, but not to faces. Same Slide with Emphasis on Face Stimuli Psych 355, Miyamoto, Spr '16

Imagine the Face Perceive the Face Kreiman, Koch & Fried (2000) Single-cell recording in medial temporal lobe as precursor to surgery for epilepsy. Cell is sensitive to perception and mental image of baseballs, but not to faces. Same Slide with Final Comment re Selectivity of Other Individual Cells Psych 355, Miyamoto, Spr '16

Kreiman, Koch & Fried (2000) Single-cell recording in medial temporal lobe as precursor to surgery for epilepsy. Cell is sensitive to perception and mental image of baseballs, but not to faces. Also found other cells that were selective for perception & imagery of animals, famous people, and food. Similarity in fMRI Activation During Perception & Imagery Psych 355, Miyamoto, Spr '16

fMRI Studies of Perception and Imagery
Perception of objects and imagining an object produces similar activation in visual cortex. Goldstein Figure 10.13 Results are from: Le Bihan, D., Turner, R., Zeffiro, T. A., Cuenod, A., Jezzard, P., & Bonnerdot, V. (1993). Activation of human primary visual cortex during visual recall: A magnetic resonance imaging study. Proceedings of the National Academy of Sciences, USA, 90, 11802–11805. O'Craven & Kanwisher Study - Better Evidence of the Same Type Psych 355, Miyamoto, Spr '16

Location of the fusiform face area and parahippocampal gyrus
Parahippocampal place area (PPA) and Fusiform face area (FFA) Diagram of Brain From the Side Facing Left Graphic from the article: Haynes, J-D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7, Next: fMRI Results for Face & Place Recognition P 355, Miyamoto, Winter '09

fMRI Study of Face and Place Perception/Imagery
% Signal Change FFA: Fusiform face area. Specialized for faces. PPA: Parahippocampal place area. Specialized for representing location info. % Signal Change Perception Imagine Subjects either view or imagine a face or place Next: Look at Just the Upper Left Quadrant of this Graph P 355, Miyamoto, Winter '09

% Signal Change FFA: Fusiform face area. Specialized for faces. PPA: Parahippocampal place area. Specialized for representing location info. % Signal Change Perception Imagine Subjects view a face or place Face stimulus activates FFA; place stimulus does not. Next: Look at Just the Lower Left Quadrant of this Graph P 355, Miyamoto, Winter '09

% Signal Change FFA: Fusiform face area. Specialized for faces. PPA: Parahippocampal place area. Specialized for representing location info. % Signal Change Perception Imagine Subjects view a face or place Place stimulus activates PPA; face stimulus does not. Next: Discuss Results for the Left Half of this Graph P 355, Miyamoto, Winter '09

% Signal Change FFA: Fusiform face area. Specialized for faces. PPA: Parahippocampal place area. Specialized for representing location info. % Signal Change Perception Imagine Subjects view a face or place Face stimulus activates FFA; place stimulus does not. Next: Look at Just the Upper Right Quadrant of this Graph P 355, Miyamoto, Winter '09

% Signal Change FFA: Fusiform face area. Specialized for faces. PPA: Parahippocampal place area. Specialized for representing location info. % Signal Change Perception Imagine Subjects view a face or place Place stimulus activates PPA; face stimulus does not. Next: Discuss Results for the Left Half of this Graph P 355, Miyamoto, Winter '09

% Signal Change FFA: Fusiform face area. Specialized for faces. PPA: Parahippocampal place area. Specialized for representing location info. % Signal Change Perception Imagine Face and place stimuli have opposite effects on FFA and PPA. Double dissociation when perceiving faces or places. Next: Discuss the Right Half of the Graph P 355, Miyamoto, Winter '09

% Signal Change FFA: Fusiform face area. Specialized for faces. PPA: Parahippocampal place area. Specialized for representing location info. % Signal Change Perception Imagine Face and place stimuli have opposite effects on FFA and PPA. Double dissociation when imagining faces or places. Do Neurological Impairments Have Equivalent Effects on Perception & Imagination? P 355, Miyamoto, Winter '09

Do Neurological Impairments Have Similar Effects on Perception and Imagery?
Kosslyn's TMS study supports this hypothesis. (See Goldstein, p. 287 and Figure on p. 288) What about permanent neurological impairments due to lesions or strokes? Reminder re Hemispatial Neglect – Transition to HemiSpatial Neglect in Imagery Psych 355, Miyamoto, Spr '16

Hemispatial Neglect (Goldstein calls this "unilateral neglect")
Hemispatial Neglect (Unilateral Neglect): A deficit of attention in which one entire half of a visual scene is simply ignored. The cause of unilateral neglect is often a stroke that has interrupted the flow of blood to the right parietal lobe. Figure to the right: Patient’s copy of an image (model) shows systematic deficits. This slide is based on instructional material that was downloaded from the Pearson Publishers website ( for Smith & Kosslyn (2006; ISBN ). The patient’s copy in the right column neglects the left side of the visual field (opposite to the side of brain damage). Unilateral Neglect in Perception & Images Psych 355, Miyamoto, Spr '16

Left Unilateral Neglect in Perception & Images
Bisiach, E., & Luzzatti, C. (1978). Unilateral neglect of representational space. Cortex, 14, Patient with left unilateral neglect was asked to imagine himself standing at one end of the Piazza del Duomo in Milan. Patient neglected left side of the visual image (in his description) just as he neglected the left side in actual perception. “Duomo” is pronounced do-mo. Transition to Question: Are Perception & Imagery Always Similar? Psych 355, Miyamoto, Spr '16

Are Perception & Imagery Always Similar?
Due to brain injury, Patient CK has visual agnosia (inability to recognize objects) Figure (a) – incorrect identifications Dart labeled “feather duster” Tennis racquet labeled “fencer’s mask” Asparagus labeled “rose twig with thorns” Figure (b) – drawings from memory Outline of England Guitar Figure (b) – If you show CK his drawings at a later time, he cannot recognize (label) what they are. Figure (p. 284). (a) Pictures incorrectly labeled by CK who had visual agnosia. (b) Drawings from memory by CK. From study by Behrmann, Moscovitch, & Winocur (1994). (a) (b) Dissociations Between Perception & Imagery Psych 355, Miyamoto, Spr '16

Dissociations Between Imagery & Perception
Case Perception Imagery Guariglia (1993) OK Unilateral neglect Farah et al. (1993): Patient RM OK: Recognizes objects & can draw pictures of objects POOR: Can't draw objects from memory or answer questions that require mental imagery Behrmann et al. (1994): Patient CK POOR: Visual agnosia (can't recognize objects) OK: Can draw objects from memory Same Slide without Emphasis Rectangles Psych 355, Miyamoto, Spr '16

Dissociations Between Imagery & Perception
Case Perception Imagery Guariglia (1993) OK Unilateral neglect Farah et al. (1993): Patient RM OK: Recognizes objects & can draw pictures of objects POOR: Can't draw objects from memory or answer questions that require mental imagery Behrmann et al. (1994): Patient CK POOR: Visual agnosia (can't recognize objects) OK: Can draw objects from memory Diagram Showing Bottom Up & Top Down Processing of Images Psych 355, Miyamoto, Spr '16

When Are Perception & Imagery Similar? When Are They Different?
Behrmann et al. (1994) point out that perception is more bottom up; Imagery is more top down. Same Slide with Explanation of Behrmann’s Hypothesis Psych 355, Miyamoto, Spr '16

When Are Perception & Imagery Similar? When Are They Different?
Hypothesis: CK's injury blocks the bottom up input for object perception. RM injury blocks the top down construction of a mental image. Conclusion - END Psych 355, Miyamoto, Spr '16

Conclusion Mental manipulation of images is similar to perception of scenes as they undergo the analogous physical alterations. Perception and imagery engage similar cognitive processes, but they are not perfectly equivalent. Perception has more bottom-up influence than imagery. Imagery has more top-down influence than perception. END Psych 355, Miyamoto, Spr '16

Lecture 09 – 1 There is no Lecture 09-1 because Monday was Memorial Day (no lecture on that day.) Psych 355, Miyamoto, Spr '15

Introduction to Problem Solving

Outline Definition of “problem” Information processing versus Gestalt approach to problem solving. Algorithmic problems & insight problems Tower of Hanoi – an example of an algorithmic problem Insight problems Problem representation Problem restructuring Problem isomorphs Lecture probably ends here Definition of Problem Solving Psych 355, Miyamoto, Spr '16

Definition of Problem Solving
A problem exists when the present state differs from a goal state. The problem is to change the present state into the goal state. Initial state Goal state Permissible "moves" – ways to change the problem state from the initial state towards the goal state. Interesting problems are situations where it is not obvious how to change the initial state into the goal state. Cognitive psychology of problem solving – how do people solve problems. Examples of Problem Solving Situations Psych 355, Miyamoto, Spr '16

Problem Solving - Examples
Math problems, physics problems, science problems generally. Initial state: The given information in the problem. Goal state: The “answer” or solution to the problem. Practical problems, e.g., arranging furniture, building a mechanical device. Winning strategies in games, business, public health, law & war. Key Ideas in Theory of Problem Solving Psych 355, Miyamoto, Spr '16

Key Ideas in the Psychology of Problem Solving
Problem representation – The mental representation of the problem that the problem solver manipulates while trying to solve the problem. Initial state Goal state Moves or transformations. Constraints and rules. Insight problems & algorithmic problems Restructuring a problem representation Set Functional fixedness Algorithmic vs Insight Problems Psych 355, Miyamoto, Spr '16

Algorithmic Problems versus Insight Problems
Algorithmic problems: The initial problem state can be transformed to the goal state by a systematic procedure. Example: The Tower of Hanoi Example: Solving a long division problem Insight problems require mental restructuring of the problem representation to get a solution. Circle problem Mutilated checkerboard problem Algorithmic and insight problems require somewhat different psychological processes to solve them. Tower of Hanoi – Example of an Algorithmic Problem Psych 355, Miyamoto, Spr '16

The Tower of Hanoi (A Problem with an Algorithmic Solution)
We will discuss algorithmic problems tomorrow. Long division is an example of an algorithmic problem Multiplying two numbers is an algorithmic problem. Finding the square root of a positive number is an algorithmic problem. Tower of Hanoi is an algorithmic problem – there is a logically adequate strategy that will always solve this problem. General Idea of an Insight Problem Psych 355, Miyamoto, Spr '16

General Idea of an Insight Problem
The solution of insight problems usually depends on finding a new way to represent the problem. Ideas from Gestalt Psychology The mind searches for structure in perception The mind searches for structure in problem solving Mental Representation of a Problem The Problem Representation = Finding a New Way to Represent a Problem Restructuring the Problem Representation = Solving the Circle Problem by Restructuring the Problem Representation Psych 355, Miyamoto, Spr '16

The Circle Problem: An Example of an Insight Problem
Given: radius r = 1 length of a = 0.9 line b is perpendicular to line a Question: What is the length of x? Hint: Change the problem representation. #Section: plot.jm(x=c(-100, 100), y = c(-100, 120), axes=F) ellipse(c(0,0), width=160, ht = 160, lwd=2) lines(c(0,0), c(-80, 80), lwd=2) lines(c(-80, 80), c(0,0), lwd=2) tt <- pi/6 rr <- 80 aa <- -cos(tt)*rr bb <- sin(tt)*rr #lines(c(0, aa), c(0, bb), lwd=2) lines(c(aa,aa), c(0, bb), lwd=2) lines(c(aa, 0), c(bb,bb), lwd=2) lines(c(aa, 0), c(0,bb), lwd=2) text(x = aa + 5, y = bb/1.95, "a", cex=1.5) text(x = -2 + aa/2, y = bb/2 - 7, "x", cex=1.5) text(x = 40, y = -7, "r", cex=1.5) text(x = c(-80), y = (95), c(paste("r = 1.0, a = ", round(aa/80 , dig=1))), cex=2, adj=0) text(x = c(-80), y = (120), "What is the length of x?", cex=2, adj=0) #lines(c(0, aa), c(0, bb), lwd=2, lty=2) #EndSection: Initial Representation Restructuring the Representation of the Circle Problem Psych 355, Miyamoto, Spr '16

Restructuring the Representation of the Circle Problem
If r = 1, a = 0.9, and a and b are perpendicular, what is the length of x? Solution: Add dashed line that connects the opposite corners. Alternative representation: The answer is obvious: x = r = 1. Alternative problem representation makes the solution obvious. Solutions to insight problems often depend on a “trick”. Here the trick is to change the problem representation. #Section: plot.jm(x=c(-100, 100), y = c(-100, 120), axes=F) ellipse(c(0,0), width=160, ht = 160, lwd=2) lines(c(0,0), c(-80, 80), lwd=2) lines(c(-80, 80), c(0,0), lwd=2) tt <- pi/6 rr <- 80 aa <- -cos(tt)*rr bb <- sin(tt)*rr #lines(c(0, aa), c(0, bb), lwd=2) lines(c(aa,aa), c(0, bb), lwd=2) lines(c(aa, 0), c(bb,bb), lwd=2) lines(c(aa, 0), c(0,bb), lwd=2) text(x = aa + 5, y = bb/1.95, "a", cex=1.5) text(x = -2 + aa/2, y = bb/2 - 7, "x", cex=1.5) text(x = 40, y = -7, "r", cex=1.5) text(x = c(-80), y = (95), c(paste("r = 1.0, a = ", round(aa/80 , dig=1))), cex=2, adj=0) text(x = c(-80), y = (120), "What is the length of x?", cex=2, adj=0) #lines(c(0, aa), c(0, bb), lwd=2, lty=2) #EndSection: Alternate Representation for the Circle Problem Another Insight Problem – the Mutilated Checkerboard Problem Psych 355, Miyamoto, Spr '16

Another Insight Problem – Mutilated Checkerboard Problem
Problem: Cover the mutilated checkerboard with domino pieces so that every domino covers two squares OR if this is impossible, explain why it is impossible. The domino pieces must always be perpendicular or parallel to the sides of the board - they cannot be placed in a diagonal position. See ‘e:\p355\hnd10-1a.doc’ and ‘e:\p355\hnd10-1b.doc’ for code for making mutilated checkerboards. #Section: plot.jm(c(-1, 9), c(-1, 10), axes=F) j.dark <- colors()[82] j.light <- 8 for (i in 1:4) for (j in 1:8) { II <- (i - 1)*2 + .5 JJ <- j - .5 if (j %in% c(1,3,5,7)) j.col <- j.light else j.col <- j.dark if (i != 1 | j != 8) rectan(c(JJ,II), width=1, ht=1, col=j.col) } II <- (i - 1)* if (j %in% c(2,4,6,8)) j.col <- j.light else j.col <- j.dark if (i != 4 | j != 1) rectan(c(JJ,II), width=1, ht=1, col=j.col) lines(c(0,0), c(0, 7), lwd=3) lines(c(0,1), c(7, 7), lwd=3) lines(c(1,1), c(7, 8), lwd=3) lines(c(1,8), c(8, 8), lwd=3) lines(c(8,8), c(8, 1), lwd=3) lines(c(8,7), c(1, 1), lwd=3) lines(c(7,7), c(1, 0), lwd=3) lines(c(7,0), c(0, 0), lwd=3) rectan(c(1,9), width = 1.6, ht=.6, col = colors()[chip.col]) text(2, 9, "= domino piece", cex=2,adj=0) #EndSection: Failed Attempt to Solve the Mutilated Checkerboard Problem Psych 355, Miyamoto, Spr '16

Failed Attempt at Solving the Mutilated Checkerboard Problem
Problem: Cover the mutilated checkerboard with domino pieces so that every domino covers two squares OR if this is impossible, explain why it is impossible. Failure! This is not a solution! FACT: It is impossible to cover the mutilated checkerboard with dominoes. Why is it impossible? #Section: plot.jm(c(-1, 9), c(-1, 10), axes=F) j.dark <- colors()[82] j.light <- 8 for (i in 1:4) for (j in 1:8) { II <- (i - 1)*2 + .5 JJ <- j - .5 if (j %in% c(1,3,5,7)) j.col <- j.light else j.col <- j.dark if (i != 1 | j != 8) rectan(c(JJ,II), width=1, ht=1, col=j.col) } II <- (i - 1)* if (j %in% c(2,4,6,8)) j.col <- j.light else j.col <- j.dark if (i != 4 | j != 1) rectan(c(JJ,II), width=1, ht=1, col=j.col) lines(c(0,0), c(0, 7), lwd=3) lines(c(0,1), c(7, 7), lwd=3) lines(c(1,1), c(7, 8), lwd=3) lines(c(1,8), c(8, 8), lwd=3) lines(c(8,8), c(8, 1), lwd=3) lines(c(8,7), c(1, 1), lwd=3) lines(c(7,7), c(1, 0), lwd=3) lines(c(7,0), c(0, 0), lwd=3) rectan(c(1,9), width = 1.6, ht=.6, col = colors()[chip.col]) text(2, 9, "= domino piece", cex=2,adj=0) #EndSection: Solution to the Mutilated Checkerboard Problem Psych 355, Miyamoto, Spr '16

Solution to the Mutilated Checkerboard Problem
Problem: Cover the checkerboard with domino pieces so that every domino covers two squares OR if this is impossible, explain why it is impossible. A Solution is Impossible! A domino piece always covers one dark square and one light square. Therefore any solution covers an equal number of dark and light squares. The mutilated checkerboard has 30 dark squares and 32 light squares so it is impossible to cover an equal number of dark and light squares. Easy Version of the Mutilated Checkerboard Problem – The Matchmaker Problem Psych 355, Miyamoto, Spr '16

Easy Version of the Mutilate Checkerboard Problem The Russian Marriage Problem (a.k.a. the Matchmaker Problem) Hayes, 1978: [wording slightly altered below] In a small Russian village, there were 32 bachelors and 32 unmarried women. A matchmaker arranges 32 highly satisfactory marriages. The village was happy and proud. One night, two bachelors got drunk and killed each other. Can the matchmaker come up with heterosexual marriages (one man, one woman) among the 62 survivors? #Section: plot.jm(c(-1, 9), c(-1, 10), axes=F) j.dark <- 8 #colors()[82] j.light <- 8 for (i in 1:4) for (j in 1:8) { II <- (i - 1)*2 + .5 JJ <- j - .5 if (j %in% c(1,3,5,7)) j.col <- j.light else j.col <- j.dark if (j %in% c(1,3,5,7)) j.tx <- "Woman" else j.tx <- "Man" rectan(c(JJ,II), width=1, ht=1, col=j.col) text(JJ, II, j.tx) } II <- (i - 1)* if (j %in% c(2,4,6,8)) j.col <- j.light else j.col <- j.dark if (j %in% c(2,4,6,8)) j.tx <- "Woman" else j.tx <- "Man" rectan(center=c(4,4), width=8, ht=8, lwd=3) rectan(c(1* , ), width=1, ht=1, col="darkblue", density = 25, angle = 0) rectan(c(1* , ), width=1, ht=1, col="darkblue", density = 25, angle = 0) #EndSection: There are 30 men and 32 women. Obviously there is no way to match them into a complete set of heterosexual couples. Mutilated Checkerboard Problem & Russian Marriage Problem Are Isomorphs Psych 355, Miyamoto, Spr '16

Mutilated Checkerboard Problem & Russian Marriage Problem
#Section: plot.jm(c(-1, 9), c(-1, 10), axes=F) j.dark <- 8 #colors()[82] j.light <- 8 for (i in 1:4) for (j in 1:8) { II <- (i - 1)*2 + .5 JJ <- j - .5 if (j %in% c(1,3,5,7)) j.col <- j.light else j.col <- j.dark if (j %in% c(1,3,5,7)) j.tx <- "Woman" else j.tx <- "Man" rectan(c(JJ,II), width=1, ht=1, col=j.col) text(JJ, II, j.tx) } II <- (i - 1)* if (j %in% c(2,4,6,8)) j.col <- j.light else j.col <- j.dark if (j %in% c(2,4,6,8)) j.tx <- "Woman" else j.tx <- "Man" rectan(center=c(4,4), width=8, ht=8, lwd=3) rectan(c(1* , ), width=1, ht=1, col="darkblue", density = 25, angle = 0) rectan(c(1* , ), width=1, ht=1, col="darkblue", density = 25, angle = 0) #EndSection: The multilated checkerboard problem and the Russian marriage problem are problem isomorphs. Problem Isomorphs: Problems that differ superficially but have identical logical structure. Concept of Problem Isomorphs Psych 355, Miyamoto, Spr '16

Concept of Problem Isomorphs
Problem isomorphs – structurally identical versions of a problem. Basic fact about problem isomorphs: Some versions of a problem are harder to solve than other versions of the problem. What is the psychological difference between the mutilated checkerboard problem and the matchmaker problem? Kaplan and Simon: It is easier to solve the Russian marriage problem than the mutilated checkerboard problem, presumably because the Russian marriage version makes the importance of pairing men with women obvious. (See next slide) Basic meaning of “morph” is “form” or “shape”. Four Isomorphic Versions of the Mutilated Checkerboard Problem Psych 355, Miyamoto, Spr '16

Kaplan & Simon: Four Isomorphic Versions of the Mutilated Checkerboard Problem
Blank board is hardest problem. “Bread”/“Butter” word labels are easiest problem. Colored & “Pink”/“Black” word labels are intermediate difficulty. The salience of the pairing affects difficulty. Blank (hardest) Colored (intermediate) See ‘e:\p355\rcode\mutilated checkerboard.doc’ for the R-code. “Pink” & “Black” Word Labels (intermediate) “Bread” & “Butter” (easiest) Conclusions re Problem Representation Psych 355, Miyamoto, Spr '16

Conclusion re Problem Representation
Some problem representations make problem solving easier than other problem representations. Solving an insight problem often depends on finding a problem representation that make it obvious how to find the solution. Examples that support these claims: Mutilated checkerboard problem; Russian marriage problem; other isomorphic versions. Circle problem. . Cheap Necklace Problem – An Example of a False Constraint Psych 355, Miyamoto, Spr '16

Cheap Necklace Problem (Chain Problem)
Cheap Necklace Problem: Convert these 4 strands of chains into a single loop by opening and closing only 3 links. (Insight problem) This is an example of a problem that is difficult because people apply a false constraint to the problem representation. Stop Here? Psych 355, Miyamoto, Spr '16

Problem Definition for the Chain Problem
Initial state: 4 strands of chains, initially separated. Goal state: One unbroken loop. Moves: Open and close links. Constraint: Only 3 links can be opened and closed. Initial State Goal State What series of permissible moves will transform the initial state into the goal state? Solution to the Chain Problem Psych 355, Miyamoto, Spr '16

Solution to the Chain Problem
Open all three links of one strand. Use these open links to link together the other three strands. (Next – see how this would work) Show How to Visualize the Solution Psych 355, Miyamoto, Spr '16

Open all three links of one strand. Use these open links to link together the other three strands. Show how to visualize the solution Psych 355, Miyamoto, Spr '16

Open all three links of one strand. Use these open links to link together the other three strands. Summary re Solution to the Cheap Necklace Problem Psych 355, Miyamoto, Spr '16

Summary re Solution to the Chain Problem
Open all three links of one strand. Use these open links to link together the other three strands. Why is this solution hard to discover? False constraint: People assume that they can only open the links at the ends of existing chains. Often we have difficulty solving a problem because we add a requirement to the solution that is not a true requirement (false constraint). Nine Dot Problem Psych 355, Miyamoto, Spr '16

Nine-Dot Problem Make a diagram that has 9 dots as shown below. Draw 4 straight lines that connect all of the dots without lifting the pencil or pen from the paper. The Nine-Dot Problem is difficult because people tend to assume a false constraint. (Same difficulty as with the Cheap Necklace Problem.) Failed Attempt at a Solution to the Nine-Dot Problem Psych 355, Miyamoto, Spr '16

Nine-Dot Problem (cont.)
Dead-end thinking. This is NOT a solution (5 lines are used). Solution to the Nine-Dot Problem Psych 355, Miyamoto, Spr '16

Solution to the Nine-Dot Problem
"Thinking inside the box" – People impose constraints on the problem that aren't there. To solve this problem, you have to “think outside the box.” False constraint: In a failed solution, people assume that they must stay within the boundaries of the square. It can be useful to "think outside the box" – discard false constraints on the problem solution. So Far: Two Obstacles to Problem Solving Psych 355, Miyamoto, Spr '16

Common Obstacles to Successful Problem Solving
Obstacle #1: A poor initial problem representation makes it difficult to solve a problem. Remedy: Try changing the problem representation Obstacle #2: People sometimes place a false constraint on the possible ways to solve the problem. Remedy: Examine the constraints – are you imposing a false constraint? Circle Problem Mutilated Checkerboard Problem Cheap Necklace Problem 9 Dot Problem # Psych 355, Miyamoto, Spr '16

The Information Processing Approach to Problem Solving;
Then: Set, Insight and Incubation Psychology 355: Cognitive Psychology Instructor: John Miyamoto 5/24/2016: Lecture 09-2 Note: This Powerpoint presentation may contain macros that I wrote to help me create the slides. The macros aren’t needed to view the slides. You can disable or delete the macros without any change to the presentation.

Outline Obstacles to successful problem solving
Information processing approach to problem solving. Example: The Tower of Hanoi Set & Functional Fixedness Insight Problems – Are they different cognitively from algorithmic problems? Incubation Effects Common Obstacles to Successful Problem Solving Psych 355, Miyamoto, Spr '16

Common Obstacles to Successful Problem Solving
Obstacle #1: A poor initial problem representation makes it difficult to solve a problem. Remedy: Try changing the problem representation Obstacle #2: People sometimes place a false constraint on the permissible ways to solve the problem. Remedy: Examine the constraints – are you imposing a false constraint? Circle Problem Mutilated Checkerboard Problem Cheap Necklace Problem 9 Dot Problem Time Permitting – Tell a Fishing Story Psych 355, Miyamoto, Spr '16

Time Permitting: An Example of a Real False Constraint – A Fishing Story
Mention this book: Hammond, J. S., Keeney, R. L., & Raiffa, H. (1998). Smart choices: A practical guide to making better decisions. Time permitting, give practical example of a false constraint. JM was stuck on a rock in the middle of a deep rapid river (Middle Fork of the Snoqualmie River). Problem: How to get from the rock to the shore (alive)? False Constraint: JM only considered routes through the rapids that would get him to the shore dry and alive. These routes were all very dangerous. Solution: Choose a route that would get JM to the shore wet but alive. Information Processing Approach to Algorithmic Problem Solving Psych 355, Miyamoto, Spr '16

"Information Processing Approach to Problem Solving"
Textbook calls this the "information processing approach to problem solving" – Is this a good name for it? PRO: That's what Newell & Simon called this approach. Alan Newell & Herbert Simon pioneered this approach to the study of problem solving. They were also pioneers of the information processing approach to studying cognitive processes. Pioneering research in: Artificial intelligence Psychology of problem solving. Simon: Psychology of decision making and economic behavior Nobel Prize in Economics. Continue this Slide with CON Argument Psych 355, Miyamoto, Spr '16

"Information Processing Approach to Problem Solving"
Textbook calls this the "information processing approach to problem solving" – Is this a good name for it? PRO: That's what Newell & Simon called this approach. CON: The name is too general - all modern cognitive theory follows an information processing approach. Even the Gestalt approach to problem solving is pursued within the information processing framework. Better name: “Analysis of problem solving strategies” (abbreviate as “Strategy Analysis”) Tower of Hanoi Problem Psych 355, Miyamoto, Spr '16

Tower of Hanoi Problem Same Slide with Emphasis Rectangles
Psych 355, Miyamoto, Spr '16

Tower of Hanoi Problem Rules and Contraints
The Tower of Hanoi Is a Problem with an Algorithmic Solution Psych 355, Miyamoto, Spr '16

The Tower of Hanoi Tower of Hanoi is a problem with an algorithmic solution. There is a logically adequate strategy that will always solve this problem. The algorithm applies no matter how many disks are used in the problem. Next: Illustrate subgoal analysis (working backwards strategy) Long division is an example of an algorithmic problem Multiplying two numbers is an algorithmic problem. Finding the square root of a positive number is an algorithmic problem. Alternative Representation of the Tower of Hanoi Psych 355, Miyamoto, Spr '16

Alternative Representation of the Tower of Hanoi
|-ABC |- |- |- |- |-ABC Initial State Goal State Goal: Move the letters one at a time from the top pin to the bottom pin. (A is "bigger" than B and B is "bigger" than C.) Constraint: Later letters never precede earlier letters. Show Diagrammatic Analysis – Working Backwards Psych 355, Miyamoto, Spr '16

Solving the Tower of Hanoi by Working Backwards
|-ABC |- |- |- |- |-ABC Initial State Goal State . . . Continue with this Diagram Psych 355, Miyamoto, Spr '16

|-ABC |- |-C |- |- |- |- |-ABC |-AB Initial State Goal State Subgoal Continue with this Diagram Psych 355, Miyamoto, Spr '16

|-ABC |- |-C |-C |- |- |- |-B |- |-ABC |-AB |-A Initial State Goal State Subgoal 1 Subgoal Continue with this Diagram Psych 355, Miyamoto, Spr '16

|-ABC |- |-C |-C |- |- |- |-B |- |-ABC |-AB |-A Initial State Goal State Subgoal 1 Subgoal 2 |- |-BC |-A Subgoal Continue with this Diagram Psych 355, Miyamoto, Spr '16

|-ABC |- |-C |-C |- |- |- |-B |- |-ABC |-AB |-A Initial State Goal State Subgoal 1 Subgoal 2 |- |-A |-BC |-BC |-A |- Subgoal 3 Subgoal Continue with this Diagram Psych 355, Miyamoto, Spr '16

|-ABC |- |-C |-C |- |- |- |-B |- |-ABC |-AB |-A Initial State Goal State Subgoal 1 Subgoal 2 |- |-A |-A |-BC |-BC |-B |-A |- |-C Subgoal 3 Subgoal 4 Subgoal Continue with this Diagram Psych 355, Miyamoto, Spr '16

|-ABC |- |-C |-C |- |- |- |-B |- |-ABC |-AB |-A Initial State Goal State Subgoal 1 Subgoal 2 |- |-A |-A |-AB |-BC |-BC |-B |- |-A |- |-C |-C Subgoal 3 Subgoal 4 Subgoal 5 Subgoal Depict the Solution to Tower of Hanoi By Using These Diagrams Psych 355, Miyamoto, Spr '16

|-ABC |- |-C |-C |- |- |- |-B |- |-ABC |-AB |-A Initial State Goal State Subgoal 1 Subgoal 2 |- |-A |-A |-AB |-BC |-BC |-B |- |-A |- |-C |-C Subgoal 3 Subgoal 4 Subgoal 5 Subgoal 6 Solution: Initial State  Subgoal 6  Subgoal 5  Subgoal 4  Subgoal 3  Subgoal 2  Subgoal 1  Goal State Above illustrates working backwards strategy. Summary of Problem Solving Strategies Psych 355, Miyamoto, Spr '16

Strategies for Solving Algorithmic Problems
Working backwards: Start from the goal state. Look for earlier states that transition to the goal state, but are closer to the initial state. Working forwards: Start from the initial state. Work towards the goal state. Means-ends analysis: A strategy that focuses on generating subgoals that bring the initial state and goal state closer together. Why the Information Processing Approach is Important for Cog Psych Psych 355, Miyamoto, Spr '16

Why the (So-Called) Information Processing Approach Is Important in Cognitive Psychology?
It has lead to useful insights about problem solving. Physics experts tend to use a working forwards strategy when solving problems in introductory physics. Physics novices tend to use a working backwards strategy when solving problems in introductory physics. Information processing analysis has lead to much better understanding of the cognitive processes during problem solving. E.g., the role of the central executive in the Wisconsin Card Sorting Problem. But the Gestalt approach is more applicable to everyday problems. So-called information processing approach focuses on problems that can be solved by a systematic procedure. E.g., the Tower of Hanoi E.g., the Missionaries & Cannibals problem (famous, but not covered in Psych 355). E.g., solving simultaneous equation problems in algebra E.g., making change in a cash transaction (actually pretty interesting work) Set in Problem Solving Psych 355, Miyamoto, Spr '16

Set in Problem Solving Set refers to a person's implicit assumptions about how to solve a problem. Goldstein textbook calls it "mental set" (p. 340). Set can be helpful or harmful to one's ability to solve a problem. Example: On the Cheap Necklace Problem, if you wrongly assume that you have to open the links at the ends of the chains, this is a harmful set. Functional Fixedness - Definition Psych 355, Miyamoto, Spr '16

Functional Fixedness Functional fixedness: Tendency to assume that objects can only serve their typical functions. Functional fixedness is a particular kind of set. Maier's Two String Problem Psych 355, Miyamoto, Spr '16

Example – Functional Fixedness
Maier's Two String Problem: Goal = To tie two strings together that hang from the ceiling. The strings are too far apart to reach both at once. Some subjects receive a hint (described on next slide). Class: Any proposed solutions? Hint #1: The chair is a red herring - thinking about the chair leads down a dead end path. Hint #2: The pliers can be useful. Solution to Maier's Two String Problem Psych 355, Miyamoto, Spr '16

Example – Functional Fixedness
Solution: Tie the pliers to one string. Swing it like a pendulum so that you can reach it while holding the other string. 40% solve problem with no hint. 62% solve problem when experimenter "accidentally" brushes against a string, setting it in motion. Functional fixedness: In the "no hint" condition, subjects see only the typical function of the pliers, not its potential function as a weight on a pendulum. Duncker's Candle Problem Psych 355, Miyamoto, Spr '16

Duncker's Candle Problem: Another Example of Functional Fixedness
You have the materials shown above (subjects receive the actual materials). You are in a room with a corkboard for a wall. Your task is to mount a candle on the wall in such a fashion that the wax does not drip on the floor. How do you do it? Solution to the Candle Problem Psych 355, Miyamoto, Spr '16

Solution to the Candle Problem
Solution: Use the tacks to attach the matchbox to the wall. Mount the candle on the matchbox. Functional fixedness: Matchbox is seen only as a container of matches, not as a potential support for the candle. Adamson (1952): 80% of subjects solve the problem if the matchbox is empty. 40% of subjects solve the problem if matches are in the matchbox. Results support the functional fixedness hypothesis. When the matchbox is empty, it is easier to see that it can serve a different function from its standard use. Conclusions re Functional Fixedness Psych 355, Miyamoto, Spr '16

Conclusions: Functional Fixedness
Set refers to assumptions that guide problem solving. They are often unconscious assumptions. Set is helpful when it leads the problem solver towards an efficient solution. Having the right set makes problem solving faster and with fewer errors. Set is unhelpful when it leads the problem solver towards an inefficient solution or to a dead end (no solution). Functional fixedness is a particular type of set. Functional fixedness is the assumption that objects can only serve their typical functions. Functional fixedness can be interpreted as a false constraint, i.e., the unstated constraint that objects can only be used with their typical functions. Are Insight Problems Really Different from Algorithmic Problems? Psych 355, Miyamoto, Spr '16

Are Insight Problems Really Different from Algorithmic Problems?
Basic Question: People think that insight comes suddenly. Is this really true? Metcalfe & Wiebe (1987) Study titled “Premonitions of Insight” “Warmth” Rating – rate how close you are to a solution? Algebra problems Insight problems Reminder re Chain Problem (Cheap Necklace Problem) Psych 355, Miyamoto, Spr '16

Cheap Necklace Problem (a.k.a. Chain Problem)
Cheap Necklace Problem: Convert these 4 strands of chains into a single loop by opening and closing only 3 links. (Insight problem) Solution to the Chain Problem Psych 355, Miyamoto, Spr '16

Solution to the Cheap Necklace Problem (a.k.a. the Chain Problem)
Open all three links of one strand. Use these open links to link together the other three strands. Experimental Procedure of Metcalfe & Wiebe Psych 355, Miyamoto, Spr '16

Experimental Design of Metcalfe & Wiebe (1987) Premonitions of Insight
Metcalfe & Wiebe (1987) had subjects solve problems while also rating their "warmth" towards finding a solution. (Ratings measured every 15 seconds.) Two types of problems: Algebra problems (possess algorithmic solution) Insight problems (require problem restructuring or breaking false constraints) “Warmth Ratings" – Every 15 seconds, rate how "cold" or "hot" you feel towards solving the problem. Recall the child's game where you hide something that the child must find. You tell the child that she is getting "warmer" or "colder" depending on whether she moves towards or away from the hidden object. Results Psych 355, Miyamoto, Spr '16

Metcalfe & Wiebe (1987): Premonitions of Insight
Graphs show “warmth” ratings as subject gets closer to a solution. “Time” is measured backwards from the time of solution, e.g., -15 means the rating was produced 15 seconds before the subject announced the problem solution.) For algebra problems, subjects feel as if they are getting warmer as they get closer to a solution. For insight problems, subjects do not feel as if they are getting warmer. The solution comes suddenly and without warning. Very slight premonition of insight Same Graph on Right – Discuss these Results Psych 355, Miyamoto, Spr '16

Metcalfe & Wiebe (1987): Premonitions of Insight
Subjectively, we feel like the solution to an insight problem comes “out of the blue.” This result supports the idea that solutions to insight problems depend on a sudden unanticipated restructuring of the problem representation. I.e., it documents the occurrence of sudden insight. The "aha" experience is a real cognitive experience. This result supports the idea that insight problems are different from algorithmic problems Incubation Effects Psych 355, Miyamoto, Spr '16

Incubation Effects Incubation effect – discovery of problem solution after a period during which one does not think about the problem. Temporal pattern of an incubation effect. Problem solver initially works hard but unsuccessfully on a problem. Problem solver spends some time doing something completely different. When the problem solver returns to working on the original problem, she suddenly finds a solution. Above is the subjective impression that is reported by many problem solvers. Is it real, or some kind of illusion? Psych 355, Miyamoto, Spr '16 Incubation Effects on the Cheap Necklace Problem – Silveira's (1971) Study

Incubation Effects - Why Do They Occur? Then: Analogical Reasoning

Lecture probably ends within this topic
Outline Incubation Effects Analogical Reasoning FYI: After the lecture on 5/25/2016, I reorganized the slides on the incubation effect to make them more clear. I did not change the content of what was in the slides. This presentation contains the reorganized version of the slides. Lecture probably ends within this topic Temporal Pattern of Incubation Effects Psych 355, Miyamoto, Spr ‘16

TIME Incubation Effects
Incubation effect – discovery of problem solution after a period during which one does not think about the problem. Temporal pattern of an incubation effect. Problem solver has sudden insight; solves the problem.. Problem solver spends some time doing something completely different. Problem solver initially works unsuccessfully on a problem. TIME Psych 355, Miyamoto, Spr '16 Same Slide without the Emphasis Comment Bubbles

TIME Incubation Effects
Incubation effect – discovery of problem solution after a period during which one does not think about the problem. Temporal pattern of an incubation effect. Problem solver has sudden insight; solves the problem.. Problem solver spends some time doing something completely different. Problem solver initially works unsuccessfully on a problem. TIME Psych 355, Miyamoto, Spr '16 Incubation Effects on the Cheap Necklace Problem – Silveira's (1971) Study

Study of Incubation Effects on the Cheap Necklace Problem (Silveira, 1971)
Preceding slide presents the subjective impression that is reported by many problem solvers. Is it real, or some kind of illusion? Control Group: Worked on problem for 30 minutes. Exp Group 1: Work on problem for 15 minutes. Perform other activities for 30 minutes. Return to problem for 15 minutes. Exp Group 2: Perform other activities for 4 hours. Control Group 55% solve the Circle Problem Exp Group 1 64% solve the Circle Problem Exp Group 2 85% solve the Circle Problem Conclusions re Incubation & Cheap Necklace Problem Psych 355, Miyamoto, Spr '16

Conclusions re Incubation Effects on the Cheap Necklace Problem (Silveira, 1971)
Conclusion 1: Incubation can be very beneficial. Conclusion 2: Protocol analysis suggested that people don't work on the problem unconsciously while thinking about something else. After incubation period, subjects returned to the problem at the same stage at which they had stopped, but they make more progress from that stage. Why do incubation effects occur? Why is it helpful to stop working on a problem for awhile? Why is Incubation Helpful? Psych 355, Miyamoto, Spr '16

Why Is Incubation Helpful for Problem Solving?
One benefit of incubation comes from forgetting inappropriate strategies or problem representations, i.e., a break down of an unhelpful set. Incubation weakens adverse priming of an ineffective problem solving strategy, thereby making it easier to activate alternative problem solving strategies. Strength Strategy 1 Strategy 2 Activation Strength While Thinking About Strategy 1 Activation Strength After Incubation Same Slide - Emphasis on Left Panel Psych 355, Miyamoto, Spr '16

One benefit of incubation comes from forgetting inappropriate strategies or problem representations, i.e., a break down of an unhelpful set. Incubation weakens adverse priming of an ineffective problem solving strategy, thereby making it easier to activate alternative problem solving strategies. Strength Strength Strength Strength Strength Strength Strategy 1 Strategy 2 Strategy 1 Strategy 2 Strategy 1 Strategy 2 Activation Strength While Thinking About Strategy 1 Activation Strength After Incubation Activation Strength While Thinking About Strategy 1 Same Slide - Emphasis on Middle Panel Psych 355, Miyamoto, Spr '16

One benefit of incubation comes from forgetting inappropriate strategies or problem representations, i.e., a break down of an unhelpful set. Incubation weakens adverse priming of an ineffective problem solving strategy, thereby making it easier to activate alternative problem solving strategies. Strength Strength Strength Strength Strength Strength Strategy 1 Strategy 2 Strategy 1 Strategy 2 Strategy 1 Strategy 2 Activation Strength While Thinking About Strategy 1 Activation Strength After Incubation Activation Strength While Thinking About Strategy 1 Same Slide - Emphasis on Right Panel Psych 355, Miyamoto, Spr '16

One benefit of incubation comes from forgetting inappropriate strategies or problem representations, i.e., a break down of an unhelpful set. Incubation weakens adverse priming of an ineffective problem solving strategy, thereby making it easier to activate alternative problem solving strategies. Strength Strength Strength Strength Strength Strength Strategy 1 Strategy 2 Strategy 1 Strategy 2 Strategy 1 Strategy 2 Activation Strength While Thinking About Strategy 1 Activation Strength After Incubation Activation Strength While Thinking About Strategy 2 Exposure to Helpful Hints – Kaplan's Study Psych 355, Miyamoto, Spr '16

One benefit of incubation comes from forgetting inappropriate strategies or problem representations, i.e., a break down of an unhelpful set. Incubation weakens adverse priming of an ineffective problem solving strategy, thereby making it easier to activate alternative problem solving strategies. Another benefit comes from consolidation of the memory of problem structure. Analogous to the effects of spaced practice on recall from LTM. Another benefit comes from sleep and dreaming (not yet known how this benefit occurs). Another benefit of incubation: Exposure to helpful hints (next slide) Exposure to Helpful Hints – Kaplan's Study Psych 355, Miyamoto, Spr '16

Exposure to Helpful Hints Can Help to Solve a Problem
Kaplan (1989) dissertation experiment. Subjects were given a list of insight problems. Sample Problem: On this hill there was a green house. And inside the green house there was a white house. And inside the white house, there was a red house. And inside the red house there were a lot of little blacks and whites sitting there. What place is this? Subjects worked on the problems over many days. Subjects were given pagers, a small microphone and tape recorder. Periodically, subjects were beeped – they had to describe their progress on the problems. Effect of Helpful Hints Psych 355, Miyamoto, Spr '16

Exposure to Helpful Hints Can Help to Solve a Problem
Sample Problem: On this hill there was a green house. And inside the green house there was a white house. And inside the white house, there was a red house. And inside the red house there were a lot of little blacks and whites sitting there. What place is this? Every so often, Kaplan would put up a hint for a problem in the form of graffitti. For example, something like this image was posted in the men’s bathroom in the psychology department. Finding: Subjects would suddenly discover solutions to problems shortly after the posting of hints without being aware that they had been given a hint. Summary re Incubation Effects Psych 355, Miyamoto, Spr '16

Summary re Incubation Effects
Incubation effect – discovery of problem solution after spending time thinking about something else. Incubation can be beneficial. One benefit of incubation comes from forgetting of inappropriate strategies or problem representations, i.e., a break down of an unhelpful set. Another benefit of incubation: Exposure to helpful hints – even accidental hints provided by daily experience. Other benefits of incubation: Consolidation of problem structure. Sleep and dreaming contributes to problem solving. Summary – Obstacles to Problem Solving Psych 355, Miyamoto, Spr '16

Summary: Obstacles to Problem Solving
Misleading or uninformative problem representation. Recommendation: Look for alternative problem representations. Adopting unnecessary assumptions (false constraints). Recommendation: Check your assumptions. Are they really necessary? Inappropriate Set: Recommendation: If you are stuck on a problem, look for alternative strategies. General Advice: Incubation can be helpful. Recommendation: Get away from the problem, then return to it. Sleep on it (problems can be solved after sleeping on it). START: Analogical Reasoning Psych 355, Miyamoto, Spr '16

Outline of the Analogical Reasoning Topic
Analogical Reasoning – What helps or hinders the discovery of useful analogies? Examples of analogies Structural definition of an analogy Studies of the discovery of analogies What are the cognitive processes during discovery of useful analogies? Experiments on analogies that influence decisions Lecture probably ends here Examples of Analogical Reasoning Psych 355, Miyamoto, Spr '16

Examples of Analogical Reasoning
Is the current international political instability analogous to the political situation that preceded World War I? Is a successful business enterprise analogous to a successful football team? Is the mutilated checkerboard problem analogous to the Russian marriage problem? Is the structure of an atom analogous to the structure of the solar system? "pony" is to "horse" as ____ is to "cow" "plane" is to "air" as "boat" is to ____ Seeing useful analogies is one of the basic mechanisms of problem solving. Why Are We Interested in Analogical Reasoning? Psych 355, Miyamoto, Spr '16

Why Are We Interested In Analogical Reasoning?
Seeing useful analogies is one of the basic mechanisms of problem solving. Analogies influence decisions. Is the current instability in Ukraine analogous to the German annexation of Austria in 1938? Maureen Dowd writing in the New York Times (January 17, 2010) about President Obama’s reluctance to support gay marriage: “Obama sees himself as such a huge change that he can be cautious about other societal changes. But what he doesn’t realize is that legalizing gay marriage is like electing a black president. Before you do it, it seems inconceivable. Once it’s done, you can’t remember what all the fuss was about.” [Italics added to the quotation] Structure of an Analogy Psych 355, Miyamoto, Spr '16

The Structure of an Analogy
Example: The structure of an atom is analogous to the structure of the solar system. Source Target Atom Solar System Source (Base Problem): Typically, a well understood problem or system to which an analogy is made. E.g., structure of the solar system Target (Test Problem): Typically, a less understood problem or system about which we can learn by analogy to the source. E.g., structure of the atom Representation: A description of the structure of the source and the target. Main Steps in the Mental Construction of an Analogy Psych 355, Miyamoto, Spr '16

Main Steps in the Mental Construction of an Analogy
Create representations of the source and target. Noticing: Noticing that a potential analogy exists. Mapping: Constructing a correspondence between the representations of the source and the target. Application: Applying the mapping from source to target, i.e., drawing inferences about the target based on what is known about the source. Dunker’s Radiation Problem - Outline Psych 355, Miyamoto, Spr '16

Dunker's Radiation Problem - Outline
Doctor must kill a tumor in a patient's stomach. Surgery is not possible. There is a ray that can kill the tumor. In high dosages it will kill the tumor, but it will also kill healthy tissue in front of the tumor. In low dosages, it won't harm the healthy tissue, but it also won't kill the tumor. Question: How can the doctor kill the tumor without killing the healthy tissue? The Convergence Solution Psych 355, Miyamoto, Spr '16

Convergence Solution for the Radiation Problem
Beam the ray at the tumor from many different angles. All rays should have low intensity, but the combination of rays at point of intersection (at the tumor) will have high intensity. The convergence solution respects the constraint that the ray cannot be high intensity. Gick & Holyoak (1983): With no other hints, about 10% of subjects (University of Michigan undergrads) produced the convergence solution. Analogical Transfer Psych 355, Miyamoto, Spr '16

Analogical Transfer Analogical transfer – seeing analogies and using the analogies to solve a new problem. Gick and Holyoak studied whether exposure to analogous problems and their solutions would help people solve the radiation problem. Train subjects on one problem. (Referred to as the "base problem.") Test subjects on another problem that is analogous to the first problem. (Referred to as the "target" or "test" problem.) Gick & Holyoak on Analogical Transfer – Basic Idea Psych 355, Miyamoto, Spr '16

Gick & Holyoak's Study of Analogical Transfer
Step 1: Train subjects to solve (or at least think about) one or more base problems (source for an analogy). Step 2: Subjects are asked to solve the Radiation Problem (target problem). Compare the following two measures: How many subjects (%) solve the target problem without seeing the base problem first? How many subjects (%) solve the target problem after seeing the base problem? Base Problem: The Fortress Problem Psych 355, Miyamoto, Spr '16

Base Problem: Duncker's Fortress Problem
A general needs to capture a fortress with his army. An attack by his entire army would capture the fortress, but the roads are mined. Since the dictator needs to move his workers to and from the fortress, the mines are set to let small bodies of men pass over them safely, Any large force would detonate the mines. How can the general attack the fortress with all of his army? Convergence solution: Attack the fortress with multiple smaller forces from many different directions. Other Base Problems – Red Adair & Arrow Diagram Psych 355, Miyamoto, Spr '16

Other Base Problems Red Adair Problem: (Red Adair was famous for being able to put out burning oil wells) Need to put out a burning oil well but can't deliver enough water from any one position. Convergence Solution: Direct streams of water at the well from many directions. Arrow Diagram: In some conditions, subjects were given an arrow diagram to see if that would be helpful. Three Conditions in the Experiment on Analogical Transfer Psych 355, Miyamoto, Spr '16

Gick & Holyoak: Study of Analogical Transfer
Base Problem: The Fortress Problem Target Problem: The Radiation Problem Three Experimental Conditions Subjects are not shown the base problem. Subject attempt to solve the target problem. This condition tests for the rate of spontaneous solutions to the target problem. Similar findings with other base problems or the arrow diagram. Same Slide with Condition 2 Added Psych 355, Miyamoto, Spr '16

Base Problem: The Fortress Problem Target Problem: The Radiation Problem Three Experimental Conditions Subjects are not shown the base problem. Subject attempt to solve the target problem. Subjects are shown the base problem. Subject attempt to solve the target problem. This condition tests for the rate of spontaneous use of the analogy of the base problem when attempting to solve the target problem. Similar findings with other base problems or the arrow diagram. Same Slide with Condition 3 Added Psych 355, Miyamoto, Spr '16

Base Problem: The Fortress Problem Target Problem: The Radiation Problem Three Experimental Conditions Subjects are not shown the base problem. Subject attempt to solve the target problem. Subjects are shown the base problem. Subject attempt to solve the target problem. Subjects are shown the base problem plus a hint that the base problem may be useful when working on the next problem.. Subject attempt to solve the target problem. This condition tests for the rate of using the analogy when the subjects are informed that it may be useful. Similar findings with other base problems or the arrow diagram. Results of Gick & Holyoak's Study Psych 355, Miyamoto, Spr '16

Summary of Results (Gick & Holyoak, 1980, 1983)
% Solutions Three Conditions 10% 1. Control: No base problem, no hint 30% 2. Base problem, no hint 75% 3. Base problem + hint These results show that noticing the analogy is a separate step from constructing the analogy. (Condition 3 is better than Condition 2.) Same Slide with Emphasis Rectangles Psych 355, Miyamoto, Spr '16

Summary of Results (Gick & Holyoak, 1980, 1983)
% Solutions Three Conditions 10% 1. Control: No base problem, no hint 30% 2. Base problem, no hint 75% 3. Base problem + hint These results show that noticing the analogy is a separate step from constructing the analogy. (Condition 3 is much better than Condition 2.) Noticing Analogies: Effects of Superficial Similarities Psych 355, Miyamoto, Spr '16

Noticing Analogies: The Effects of Superficial Similarities
How to increase the rate at which people notice an analogy? Hypothesis: People are more likely to notice an analogy if the base and target problem share superficial features. Evidence for this is given by the Lightbulb Problem (next). Lightbulb Problem – Standard Version Psych 355, Miyamoto, Spr '16

Effect of Superficial Features
Lightbulb Problem (see Goldstein, pp. 352) Ruth must repair an expensive lightbulb. The filament is broken. A high intensity laser can repair the filament, but it will break the glass. Solution: Beam many low intensity lasers at the filament from many different directions. Holyoak & Koh (1987): Subjects who were only given the Lightbulb Problem solved it 10% of the time. Subjects who were first saw the Radiation Problem and its solution solved the Lightbulb Problem 81% of the time. Excellent transfer! Recall that the Fortress Problem transferred to the Radiation Problem 30% of the time. Why is transfer from the Radiation Problem to the Lightbulb Problem much better than transfer from the Radiation Problem to the Lightbulb Problem? Radiation Problem and Lightbulb problem are similar w.r.t. both superficial features and structural features. Radiation Problem and Fortress Problem are similar w.r.t. structural features, but not w.r.t. superficial features. Comparing Effects of Superficial Features & Structural Features Psych 355, Miyamoto, Spr '16

Effects of Superficial Features versus Structural Features
Subjects first saw the Radiation Problem and its solution. They then tried to solve a version of the Lightbulb Problem. Lightbulb Problem (Fragile Glass Version) – shares both superficial & structural features with the Radiation Problem: Ruth must repair an expensive lightbulb. The filament is broken. A high intensity laser can repair the filament, but it will break the glass. (Same as scenario on preceding slide.) Lightbulb Problem (Insufficient Intensity Version) – shares superficial BUT NOT structural features with the Radiation Problem: Ruth must repair an expensive lightbulb. The filament is broken. A high intensity laser can repair the filament, but she doesn’t have one. She only has low intensity lasers available to her. Solution for both versions: Beam many low intensity lasers at the filament from many different directions. Results for Two Versions of the Lightbulb Problem Psych 355, Miyamoto, Spr '16

Results: Superficial Features versus Structural Features
Subjects first saw the Radiation Problem and its solution. They then tried to solve one version of the Lightbulb Problem. Lightbulb Problem (Fragile Glass Version) – shares both superficial & structural features with the Radiation Problem: Lightbulb Problem (Insufficient Intensity Version) – shares superficial BUT NOT structural features with the Radiation Problem: Results: % Solution Version 69% Fragile Glass Version 33% Insufficient Intensity Version Superficial Similarities + Structural Similarities Analogical Transfer Comparison of Features for Different Problems Psych 355, Miyamoto, Spr '16

Wednesday, May 25, 2016: The Lecture Ended Here
Psych 355, Miyamoto, Spr '16

Comparison of Features Among the Problems
Superficial Feature Structural Feature Problem Medium of Action Why One Strong Beam/Attack Not Possible Analogical Transfer Successful? Radiation Problem X-ray beam One strong x-ray beam will injure the intervening tissue. Radiation problem is the source problem Fortress Problem Attack by troops One strong attacking army will detonate mines on roads. poor Lightbulb Problem (Insufficient Intensity Version) Laser beam High intensity laser not available Lightbulb Problem (Fragile Glass Version) High intensity laser will break the glass. good Same Slide with Emphasis Rectangles Psych 355, Miyamoto, Spr '16

Superficial Feature Structural Feature Problem Medium of Action Why One Strong Beam/Line of Attack Not Possible Analogical Transfer Successful? Radiation Problem X-ray beam One strong x-ray beam will injure the intervening tissue. Radiation problem is the source problem Fortress Problem Attack by troops One strong attacking army will detonate mines on roads. poor Lightbulb Problem (Insufficient Intensity Version) Laser beam High intensity laser not available Lightbulb Problem (Fragile Glass Version) High intensity laser will break the glass. good What Influences the Likelihood of Analogical Transfer? Psych 355, Miyamoto, Spr '16

The Psychology of Inductive Inference

Outline Finish: What promotes analogical transfer? Deductive and inductive reasoning Expected utility theory - the normative theory of rational action The heuristics & biases research program The availability heuristic – What is it? The representativeness heuristic – What is it? Lecture probably ends here The Radiation Problem and Two Versions of the Lightbulb Problem Psych 355, Miyamoto, Spr ‘16

Radiation Problem and Two Versions of the Lightbulb Problem
Radiation Problem: How to kill a tumor with a radiation beam without killing the intervening healthy tissue? (This was the base problem in the study of the lightbulb problem.) Lightbulb Problem: How to mend the lightbulb filament with a laser without causing the glass to shatter? (This is the test problem.) Fragile Glass Version – shares both superficial & structural features with the Radiation Problem: Insufficient Intensity Version – shares superficial BUT NOT structural features with the Radiation Problem: Psych 355,, Miyamoto, Spr '16

Superficial Feature Structural Feature Problem Medium of Action Why One Strong Beam/Attack Not Possible Analogical Transfer Successful? Radiation Problem X-ray beam One strong x-ray beam will injure the intervening tissue. Radiation problem is the source problem Lightbulb Problem (Insufficient Intensity Version) Laser beam High intensity laser not available poor Lightbulb Problem (Fragile Glass Version) High intensity laser will break the glass. good Same Slide with Emphasis Rectangles Psych 355, Miyamoto, Spr '16

Superficial Feature Structural Feature Problem Medium of Action Why One Strong Beam/Line of Attack Not Possible Analogical Transfer Successful? Radiation Problem X-ray beam One strong x-ray beam will injure the intervening tissue. Radiation problem is the source problem Lightbulb Problem (Insufficient Intensity Version) Laser beam High intensity laser not available poor Lightbulb Problem (Fragile Glass Version) High intensity laser will break the glass. good What Influences the Likelihood of Analogical Transfer? Psych 355, Miyamoto, Spr '16

What Influences the Likelihood of Analogical Transfer?
Superficial Similarities + Structural Similarities Analogical Transfer Introduction to Inductive Inference Psych 355, Miyamoto, Spr '16

Introduction to Reasoning
Deductive & inductive reasoning - what are they? Expected utility theory - the normative theory of rational action The heuristics & biases research program - what is it? The availability heuristic – What is it? The representativeness heuristic – What is it? Definition - Deductive Reasoning Psych 355, Miyamoto, Spr '16

Deductive Reasoning Deductive reasoning is reasoning ....
FROM: Premises that are assumed to be true TO: Conclusions that are certain to be true if the premises are true. Examples of deductive reasoning: Math problem solving Logic problems Some aspects of physics problem solving; and other natural science problem solving Four Card Problem Definition of Inductive Reasoning Psych 355, Miyamoto, Spr '16

Inductive Reasoning Inductive reasoning is reasoning ....
FROM: Evidence TO: Strength of belief with respect to one or more conclusions (judged likelihood that a possible conclusion is true) Examples of inductive reasoning: How likely is it that it will rain tomorrow in Seattle? How likely is it that the defendant in a criminal trial is guilty? What do the results of an experiment imply about a hypothesis that is tested in the experiment? Economic forecasts: How likely is a recession in Europe during 2015/2016? Based on what we know about American history and culture, what is likely to happen in the next national elections? Why Psychologists Are Interested in Inductive Reasoning Psych 355, Miyamoto, Spr '16

Why Psychologists Are Interested in Inductive Reasoning
Most real-world questions involve uncertainties. How do people make decisions when faced with risk and uncertainty? Rational decision model: Expected utility theory Bayesian decision theory Central assumption of economic theory & business decision making: Rational decision makers obey the Bayesian decision theory. Heuristics & biases research: Cognitive critique of the rational agent model. Modern behavioral economics General issue of how humans acquire knowledge from uncertain information. Psychology of Risk – What Are Basic Issues? Psych 355, Miyamoto, Spr '16

Psychology of Risk and Likelihood – What Are Basic Issues?
How do people make decisions when faced with risk and uncertainty? Example: Deciding whether to buy a house. Deciding which house to buy among the available choices. Example: Deciding what medical treatment is best for a given patient (maybe yourself; maybe for someone else). How do people judge the likelihood of events? Example: How likely is it that North Korea will sell nuclear technology to other terrorists? Example: How likely is it that you will find a good job if you pursue a career in X, e.g., marketing? How do people judge how much they like or dislike particular possibilities? How do people predict their future preferences? Basic Elements of a Rational Decision Model Psych 355, Miyamoto, Spr '16

Basic Elements of a Rational Decision Model
All decisions can/should be represented as choices between gambles. Every possible action should be represented as a specific gamble. Mathematicians, economists and philosophers have identified rules of reasoning that govern how a rational agent would choose a best course of action (best gamble) from the available actions. Psychological Issues that Arise in the Critique of Rational Decision Models (fix this) How do humans perceive risks? How do humans respond to risks? How do humans evaluate uncertainties? How do humans evaluate the relative strength of preference for different outcomes. Rational Decision Model & JDM Psych 355,, Miyamoto, Spr '16

Rational Decision Model & JDM
Rational decision model: Expected utility theory Bayesian decision theory Central assumption of economic theory & business decision making: Rational decision makers obey the Bayesian decision theory. Judgment & Decision Making (JDM) – a branch of cognitive psychology; generally critical of the rational decision model Human cognitive processes lead to counterproductive (suboptimal) judgments and decisions Heuristics & biases research: Cognitive critique of the rational agent model. Heuristics & biases research program is a major part of JDM research. Definition - Heuristic Reasoning Strategies Psych 355, Miyamoto, Spr '16

Heuristic Reasoning Strategies
Heuristic reasoning strategies – reasoning strategies that are useful because they are easy and generally effective, even though they can sometimes lead to errors. Main Claims of the Heuristics & Biases Movement Psych 355, Miyamoto, Spr '16

Main Claims of the Heuristics & Biases (H&B) Movement
Human cognitive processes do not follow the pattern of a rational model. (Rational model = expected utility theory & Bayesian decision model) Human decision making uses heuristic strategies that are useful, but they can lead to systematic errors. Heuristic reasoning strategies .... .... are often fast and effective, .... place low demands on cognitive resources. .... but they can lead to errors in particular situations. Behavioral economics – the application of cognitive psychology to the analysis of economic behavior. Heuristic Reasoning Strategies - Definition Psych 355, Miyamoto, Spr '16

Thursday, May 26, 2016: The Lecture Ended Here
Psych 355,, Miyamoto, Spr '16

Some Heuristics in Inductive Reasoning
Availability Representativeness Anchoring & Adjustment Confirmation bias Focusing illusion Framing effects Mental accounting Only some of the more important heuristics are listed here. There are many more. Availability Heuristic Psych 355, Miyamoto, Spr '16

Next: Lecture 10 - 1 There is no lecture file for this date
Next: Lecture There is no lecture file for this date. There was no lecture on this date because it was a holiday. Psych 355, Miyamoto, Spr '15

Heuristics & Biases: The Availability Heuristic and The Representativeness Heuristic

Lecture probably ends within this topic
Outline Reminder about the heuristics & biases program in judgment and decision making (JDM) The availability heuristics - definition and examples The representativeness heuristic Definition Some examples Discussion of why people tend to judge probability based on similarity Lecture probably ends within this topic Heuristic Reasoning Strategies - Definition Psych 355, Miyamoto, Spr ‘16

Heuristic Reasoning Strategies
Heuristic reasoning strategies – reasoning strategies that are useful because they are easy and generally effective, even though they can sometimes lead to errors. Main Claims of the Heuristics & Biases Movement Psych 355, Miyamoto, Spr '16

Main Claims of the Heuristics & Biases (H&B) Movement
Human cognitive processes do not follow the pattern of a rational model. (Rational model = expected utility theory & Bayesian decision model) Human decision making uses heuristic strategies. Heuristic reasoning strategies .... .... are often fast and effective, .... place low demands on cognitive resources. .... but they can lead to errors in particular situations. Heuristic Reasoning Strategies - Definition Psych 355, Miyamoto, Spr '16

Some Heuristics in Inductive Reasoning
Today's lecture Availability Representativeness Anchoring & Adjustment Confirmation bias Focusing illusion Framing effects Mental accounting More heuristics that have been proposed than are listed here. Availability Heuristic Psych 355, Miyamoto, Spr '16

Availability Heuristic
Frequency of Experience Other Factors Availability of Memory for an Event Learning Judged Likelihood of a Similar Event Judgment Availability heuristic – events are judged more probable if similar events are easy to recall or easy to imagine. In general, frequently encountered events are easier to recall. The availability heuristic exploits the converse of this relationship: Events that are easy to recall are thought to be frequent in occurrence. Availability heuristic causes biased probability judgments when other factors that influence availability are not taken into account. Psych 355, Miyamoto, Spr '16 Same Slide Without Emphasis Rectangles

Availability Heuristic
Frequency of Experience Other Factors Availability of Memory for an Event Learning Judged Likelihood of a Similar Event Judgment Availability heuristic – events are judged more probable if similar events are easy to recall or easy to imagine. In general, frequently encountered events are easier to recall. The availability heuristic exploits the converse of this relationship: Events that are easy to recall are thought to be frequent in occurrence. Availability heuristic causes biased probability judgments when other factors that influence availability are not taken into account. Psych 355, Miyamoto, Spr '16 Availability Experiment: Lists of Famous & Non-Famous Names

Condition I: Famous Male Non-Famous Female
Availability Bias Due to Ease of Encoding Famous/Non-Famous Names x Male/Female Condition I: Famous Male Non-Famous Female Bill Clinton Tom Hanks Michael Jordan Mary Brooks Andrea Forbus Leanne Faris Condition II: Famous Female Non-Famous Male William Hale Murray Jencks Lionel Worley Michelle Obama Angelina Jolie Sarah Palin Subjects saw a list of names, one at a time, that mixed famous males with non-famous females, or vice versa. There were 18 famous and 19 non-famous names in the list. Next: Same Slide with No Barriers & Results Psych 355, Miyamoto, Spr '16

Results: Famous/Non-Famous Names x Male/Female
Condition I: Famous Male Non-Famous Female Bill Clinton Tom Hanks Michael Jordan Mary Brooks Andrea Forbus Leanne Faris Condition II: Famous Female Non-Famous Male William Hale Murray Jencks Lionel Worley Michelle Obama Angelina Jolie Sarah Palin Results: Subjects reported more males if the males were famous; Subjects reported more females if the females were famous. Availability influences perceived frequency. Reminder of Link to Memory Model Psych 355, Miyamoto, Spr '16

Importance of "Other Factors" in Causing the Availability Heuristic
Frequency of Experience Other Factors Availability of Memory for an Event Learning Judged Likelihood of a Similar Event Judgment “Other Factors” that influence availability of a memory Famous names are easy to encode and easy to retrieve. Non-famous names are harder to encode and harder to retrieve. Psych 355, Miyamoto, Spr '16 Experimental Demonstration of Egocentric Bias

Egocentric Bias (Example of Availability Heuristic)
Egocentric bias: People overestimate the proportion that they have contributed to a project or activity. Ross & Sicoly (1979): Subjects were 37 married couples. Working separately, husband and wife rated self and spouse for their work on 20 activities: making breakfast; cleaning dishes; cleaning house; making important decisions; ... ; causing arguments between themselves; making the house messy; irritating spouse. primarily primarily husband wife Psych 355, Miyamoto, Spr '16 How Are Responses Scored for Husband's and Wife's Perception of Contribution

Rating Procedure in Egocentric Bias Study
Husband and wife rated self and spouse for their work on 20 activities primarily primarily husband wife Subjects rated their responsibility on a line as shown above. Husband's rating measured as distance from the right end; wife's ratings measured as distance from the left end. If husband and wife have accurate perceptions of responsibility, the sum of their ratings should equal the length of the line. Wife's Mark Husband's Mark Husband's Rating Wife's Rating Predicted Response Pattern If No Bias Existed Psych 355, Miyamoto, Spr '16

Example: Suppose that Husband & Wife's Ratings Are Consistent With Each Other
Husband and wife agree as to contribution of each to a task like washing dishes: primarily primarily husband wife If husband and wife were not egocentric, the couple's ratings would sum to +100. ▐ + 25 in husband's scoring ▐ + 75 in wife's scoring +100 total of husband & wife _________________________ Predicted Response Pattern If Egocentric Bias Exists Psych 355, Miyamoto, Spr '16

Example: Suppose that Husband & Wife's Ratings Are Inconsistent With Each Other
Husband and wife disagree about their contributions to washing dishes. primarily primarily husband wife If husband and wife are egocentric, the couple's ratings would sum to more than 100. ▐ + 48 in husband's scoring ▐ + 75 in wife's scoring +123 total of husband & wife _________________________ Results for Egocentric Bias Study Psych 355, Miyamoto, Spr '16

Results for Egocentric Bias Study
The inconsistent pattern is typical: On many activities, .... Ratings consistently summed to number greater than +100 across many activities, showing an excessive attribution of credit or blame to the self. The result holds for both good things (wash the dishes) and bad things (buy unnecessary things). Husband’s Rating + Wife’s Rating > 100 Conclusion re Egocentric Bias – Relation to Availability Psych 355, Miyamoto, Spr '16

Importance of "Other Factors" in Causing the Availability Heuristic
Frequency of Experience Other Factors Availability of Memory for an Event Learning Judged Likelihood of a Similar Event Judgment Egocentric bias is probably due to the greater availability of self-actions than partner actions "Other Factors" = Greater Awareness of Self than of Other Other Examples: Self versus supervisor focus in attributing responsibility for BA thesis work. Basketball players attributing responsibility for win or loss to actions of own team more than to actions of the other team. Psych 355, Miyamoto, Spr '16 Sampling Bias in Everyday Media

Sampling Bias in Everyday Media
Biases in Information Sources Biases in Availability Biases in Perceived Likelihood of Events Things we all know: TV ads do not give an accurate picture of the value of products. Political spin doctors are trying to manipulate our beliefs. TV news is emphasizes dramatic events; it ignores undramatic events. The portrayal of men/women, black/whites, rich/poor, gay/straight, on TV is not a representative presentation of these groups. Our own experiences are not typical of everybody’s experience. Etc. We all know that these information sources are biased, but can we really correct for these biases when forming beliefs? Doubtful. Insensitivity to sampling bias (exposure bias) is not strictly a cognitive bias. The world is biased, but we have difficulty taking this into account. Psych 355, Miyamoto, Spr '16 Return to the Diagram of the Availability Heuristic & List of “Other Factors”

“Other Factors” that Influence the Availability of Events
Examples of “Other Factors” Egocentric bias. Dramatic events seem more common than non-dramatic events. Biases in the media create biases in the availability of stereotypes. Recent events seem more common than earlier events. Anything that makes events easier to encode or retrieve can make the events seem more frequent than they are. Conclude with remarks re the importance of probability judgment in decision making. Tomorrow: Representativeness heuristic Summary re the Availability Heuristic Psych 355, Miyamoto, Spr '16

Summary re the Availability Heuristic
Judging probability in terms of availability is a heuristic. I.e., it is generally a reasonable way to estimate likelihood, but it can lead to systematic errors. Factors that are not related to experienced frequency can make make particular events very available. E.g., the perceived probability of being killed by a random crazy person will tend to be exaggerated. Definition of the Representativeness Heuristic Psych 355, Miyamoto, Spr '16

Representativeness Heuristic
Event A is more representative than Event B Event A is more probable than Event B "more representative" means "more similar to a stereotype of a class or to a typical member of a class." Representativeness Heuristic: Judge the probability of an event E by the representativeness of the event E. We need some example to make this idea more clear (see next). Psych 355, Miyamoto, Spr '16 Example of Jim: An Athletic, Muscular & Competitive Guy

Representativeness Heuristic – An Example
Question: Jim is tall and very muscular. He's also very competitive He drives an expensive car and wears flashy clothing Which is more probable? Jim is a professional athlete. Jim is a lawyer or financial analyst. People predict that Jim is a professional athlete because Jim is similar to a stereotype of a professional athlete. It is a better bet that Jim is a lawyer or financial analyst because there are many more lawyers and financial analysts than professional athletes. This response is predicted by the Representativeness Heuristic This is the better bet. Return to Slide with Diagram of Representativeness Heuristic Psych 355, Miyamoto, Spr '16

Event A is more representative than Event B Event A is more probable than Event B Representativeness Heuristic: Events that are more representative are regarded as more probable. Example: Jim is muscular/athletic/competitive. Clarify meaning of “representativeness.” a professional athlete? a lawyer or financial analyst? Is he ..... Jim is similar to a stereotype. Jim is less similar to the stereotype. Intro to the Lawyer/Engineer Problem Psych 355, Miyamoto, Spr '16

Lawyer/Engineer Problem (K&T, 1973)
DESCRIPTION OF JACK: Jack is a 45-year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues. (This description is designed to fit the stereotype of an engineer more than the stereotype of a lawyer.) 30:70 Condition: High Base Rate for Engineer If Jack's description were drawn at random from a set of lawyers and 70 engineers, what would be the probability that Jack is one of the engineers? 70:30 Condition: Low Base Rate for Engineer If Jack's description were drawn at random from a set of lawyers and 30 engineers, what would be the probability that Jack is one of the engineers? Findings re Lawyer/Engineer Problem Psych 355, Miyamoto, Spr '16

Results re Lawyer/Engineer Problem
Probability of "engineer" was rated to be about the same in the low and high base rate conditions (Insensitivity to Base Rate, a.k.a. Base Rate Neglect) High base rate condition = 30:70 Condition Low base rate condition = 70:30 Condition Probability theory implies that Jack is much more likely to be an engineer in the high base rate condition than in the low base rate condition. Why do people ignore base rates? See next slide Why Do People Ignore Base Rates? The Representativeness Explanation Psych 355, Miyamoto, Spr '16

Why Do People Often Ignore Base Rates?
The Representativeness Heuristic: People judge probability based on the similarity of the current case to a stereotype. Jack is equally similar to a typical engineer in the low and high base rate conditions. People ignore the base rate because the base rate is irrelevant to the judgment of how similar Jack is to a typical engineer. Probability theory shows that the base rate is very relevant to judging the probability that Jack is an engineer. Cognitive theory shows that the base rate is often not psychologically relevant to judging the probability that Jack is an engineer. When Does It Matter Whether People Ignore Base Rates? Psych 355, Miyamoto, Spr '16

Tuesday, May 31, 2016: The Lecture Ended Here
Psych 355,, Miyamoto, Spr '16

When Does It Matter Whether People Ignore Base Rates?
Evidence shows that physicians sometimes overlook base rates when attempting to diagnose a disease. Evidence suggests that investors are overly influenced by short-term information regarding the value of stocks. Business decisions tend to be overly influenced by short-term trends. Criticism of Goldstein’s Description of the Lawyer/Engineer Problem Psych 355, Miyamoto, Spr '16

Criticism of Goldstein’s Description of the Lawyer/Engineer Problem
The Goldstein description of this study is inadequate because it does not contrast the 30:70 condition with the 70:30 condition. It only mentions the 70:30 condition. The important finding is that subjects in the 30:70 and 70:30 conditions are equally confident that Jack is an engineer (subjects in the two conditions overlook the difference in the base rate). Knowing only the result for the 70:30 condition does not establish that subjects ignore base rates. See Goldstein p. 374. Psych 355, Miyamoto, Spr '16 Explain Idea of a Regression Effect - What Is a Regression Effect?

The Representativeness Heuristic then: Risk Attitude and Framing Effects

Outline Review: The Lawyer/Engineer Problem (representativeness heuristic and base rate neglect) The conjunction fallacy The conjunction fallacy is predicted by the hypothesis that people use a representativeness heuristic. Introduction to Preference Under Risk Risk attitude (risk aversion and risk seeking) Reflection effect Framing effects: Gain frames and loss frames Mental accounting Lecture probably ends here Diagram that Depicts Use of a Representativeness Heuristic Psych 355, Miyamoto, Spr ‘16

Event A is more representative than Event B Event A is more probable than Event B "more representative" means "more similar to a stereotype of a class or to a typical member of a class." Representativeness Heuristic: Judge the probability of an event E by the representativeness of the event E. We need some example to make this idea more clear (see next). Psych 355, Miyamoto, Spr '16 The Lawyer/Engineer Problem

Lawyer/Engineer Problem (K&T, 1973)
DESCRIPTION OF JACK: Jack is a 45-year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in political and social issues. (This description is designed to fit the stereotype of an engineer more than the stereotype of a lawyer.) 30:70 Condition: High Base Rate for Engineer If Jack's description were drawn at random from a set of lawyers and 70 engineers, what would be the probability that Jack is one of the engineers? 70:30 Condition: Low Base Rate for Engineer If Jack's description were drawn at random from a set of lawyers and 30 engineers, what would be the probability that Jack is one of the engineers? Findings re Lawyer/Engineer Problem Psych 355, Miyamoto, Spr '16

Results re Lawyer/Engineer Problem
Probability of "engineer" was rated to be about the same in the low and high base rate conditions (Insensitivity to Base Rate, a.k.a. Base Rate Neglect) High base rate condition = 30:70 Condition Low base rate condition = 70:30 Condition Probability theory implies that Jack is much more likely to be an engineer in the high base rate condition than in the low base rate condition. (This is an application of Bayes' Rule - an important rule of reasoning.) Why do people ignore base rates? See next slide High versus low base rate has no effect, even though it ought to influence the probability judgment. Why Do People Ignore Base Rates? The Representativeness Explanation Psych 355, Miyamoto, Spr '16

Why does the Representativeness Heuristic Cause Base Rate Neglect?
Judgment Process for the Representativeness Heuristic Event A is more representative than Event B Event A is more probable than Event B The similarity of the particular case to the stereotype of a category influences how representative this category appears to be. Therefore similarity influences the judgment of probability. Example: Similarity of Jack to the stereotype of an engineer influences the judged likelihood that Jack is an engineer. The base rate of events is unrelated to how representative an event seems to be. Therefore base rate will not influence the judgment of probability. Example: The base rate for engineers (70:30 or 30:70) is unrelated to how representative Jack would be of the engineer category. Therefore the base rate of engineers should not influence the judged likelihood that Jack is an engineer. Psych 355, Miyamoto, Spr '16 #

Why Do People Often Ignore Base Rates?
The Representativeness Heuristic: People judge probability based on the similarity of the current case to a stereotype. Jack is equally similar to a typical engineer in the low and high base rate conditions. People ignore the base rate because the base rate is irrelevant to the judgment of how similar Jack is to a typical engineer. Probability theory shows that the base rate is very relevant to judging the probability that Jack is an engineer. Cognitive theory shows that the base rate is often not psychologically relevant to judging the probability that Jack is an engineer. When Does It Matter Whether People Ignore Base Rates? Psych 355, Miyamoto, Spr '16

When Does It Matter Whether People Ignore Base Rates?
Evidence shows that physicians sometimes overlook base rates when attempting to diagnose a disease. Evidence suggests that investors are overly influenced by short-term information regarding the value of stocks. Business decisions tend to be overly influenced by short-term trends. Criticism of Goldstein’s Description of the Lawyer/Engineer Problem Psych 355, Miyamoto, Spr '16

Criticism of Goldstein’s Description of the Lawyer/Engineer Problem
The Goldstein description of this study is inadequate because it does not contrast the 30:70 condition with the 70:30 condition. It only mentions the 70:30 condition. The important finding is that subjects in the 30:70 and 70:30 conditions are equally confident that Jack is an engineer (subjects in the two conditions overlook the difference in the base rate). Knowing only the result for the 70:30 condition does not establish that subjects ignore base rates. See Goldstein p. 374. Psych 355, Miyamoto, Spr '16 The Conjunction Fallacy - The Famous "Linda" Problem

Conjunction Fallacies – The Famous "Linda" Problem
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. F: Judge the probability that Linda is a feminist. T: Judge the probability that Linda is a bank teller. F & T: Judge the probability that Linda is a feminist and a bank teller. Probability Theory: P(F) ≥ P(F & T), P(T) ≥ P(F & T) Typical Judgment: P(F) > P(F & T) > P(T) Why Are Conjunction Fallacies Psychologically Interesting? Psych 355, Miyamoto, Spr '16

Why Conjunction Fallacies Are Psychologically Interesting?
Conjunction fallacies strongly support the claim: Human reasoning with uncertainty is different from probability theory. Human reasoning with uncertainty is based on a various heuristics – the conjunction fallacy is caused by the use of a representativeness heuristic. Two Question Regarding Conjunction Fallacies: What is wrong with the judgment pattern: P(F) > P(F & T) > P(T)? Why do people's judgments have this pattern? Probability & the Set Inclusion Principle Psych 355, Miyamoto, Spr '16

Probability and the Set Inclusion Principle
If set B is a subset of set A, then the probability of B must be equal or less than the probability of A. B  A  P(B) < P(A) Rationale: When B occurs, A also occurs, so the probability of B cannot exceed the probability of A. A B Sample Space (set of all possibilities) Interpretation of Linda Problem in terms of Set Inclusion Psych 355, Miyamoto, Spr '16

Conjunction Fallacy F F & T T
Sample Space F F & T T Linda Problem: 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. F: Judge the probability that Linda is a feminist. T: Judge the probability that Linda is a bank teller. F & T: Judge the probability that Linda is a feminist and a bank teller. Probability Theory: P(F) ≥ P(F & T), P(T) ≥ P(F & T) Typical Judgment: P(F) > P(F & T) > P(T) Why Do People Make Conjunction Errors? Psych 355, Miyamoto, Spr '16

Why Do People Make Conjunction Errors?
Remember: The representativeness heuristic predicts that people judge the probability based on how similar the individual case is to a typical member (stereotype) of a group. The description of Linda sounds more similar to someone who is a feminist and a bank teller, than to someone who is only a bank teller. stronger similarity Description of Linda Bank Teller Prototype Feminist Bank Teller Prototype weaker similarity Criticisms of the Representativeness Explanation of Conjunction Fallacies Psych 355, Miyamoto, Spr '16

Criticisms of This Interpretation
Criticism: The Linda problem is just one problem. Reply: Same pattern is found with many similar problems. Criticism: Maybe people think “bank teller” means someone who is a bank teller and not a feminist. Criticism: Conjunction errors can be eliminated by stating the question in terms of frequencies instead of probabilities. Summary re Representativeness Heuristic Psych 355, Miyamoto, Spr '16

Summary re Representativeness Heuristic
There is nothing wrong with using similarity as a factor in judging a probability. The problem is that attention to similarity causes people to ignore other factors, like base rates, regression effects and set inclusion, that are also relevant to judging probability. Representativeness Heuristic Bayes' Rule says: The Probability of an Event X The Base Rate of the Event X The Evidence for and against Event X Two Major Issues in Psych of Decision Making - Probability & Preference Psych 355, Miyamoto, Spr '16

Two Major Issues in Psychology of Decision Making
Judgments of likelihood What outcomes are likely? Which are unlikely? How likely? Slightly possible? Almost certain? Etc. Judgments of preference & making choices How strongly do you like or dislike different possible outcomes? How risky are difference choices? What risks are worth taking for potential gains? We’ve been talking briefly about this topic. Next topic. Digression re Risk Attitude Psych 355, Miyamoto, Spr '16

Risk Attitude Risk averse action: A person chooses a sure-thing X over a gamble G where X is less than the expected value of G. Example of a Risk Averse Decision Prefer a sure win of $500 over a gamble for $1,010 or $ (Note: Expected value of gamble = $505) Risk seeking action: A person chooses a gamble G over a sure thing X where the expected value of G is less than X. Example of a Risk Seeking Decision Prefer a gamble for $1000 or $0 over a sure win of $ (Note: Expected value of gamble = +$500) Psych 355, Miyamoto, Spr '16 Examples of Risk Aversion & Risk Seeking

Examples of Risk Aversion & Risk Seeking
Whenever you buy insurance, you are acting in a risk averse way. The cost of car insurance is a sure loss that is a bigger loss than the expected value of the gamble of driving an uninsured car. Whenever you gamble at a professional casino or in state lottery, you are acting in a risk seeking way. The cost of the lottery ticket is greater than the expected value of the lottery ticket. In a casino, all of the mechanical gambles (roulette or slot machine) have a negative expected gamble. Psych 355, Miyamoto, Spr '16 Is It More Rational to be Risk Averse or Risk Seeking?

Is It More Rational to be Risk Averse or Risk Seeking?
There is no rational requirement to be risk averse. It is equally rational to be generally risk averse or generally risk seeking. It is also rational to be risk seeking for some money quantities, e.g., small amounts of money, and risk averse for other money quantities, e.g., large amounts of money. It is also rational to be risk averse in some domains, e.g., gambles for the health of your children, and risk seeking in other domains, e.g., gambles for business profit and loss. Before the work of Kahneman & Tversky, many theorists thought that people were generally risk averse. Next slide: Reflection effect shows that people are risk averse for some kinds of gambles, and risk seeking for other types of gambles. Psych 355, Miyamoto, Spr '16 Reflection Effect Example

Reflection Effect – Example
Choice 1: Which would you prefer? Option A: .80 chance to win $4,000. Option B: chance to win $3,000. Choice 2: Which would you prefer? Option C: .80 chance to lose $4, Option D: 1.0 chance to lose $3,000. People are typically risk averse for gains and risk seeking for losses. This pattern is called the reflection effect. Typical preference  when gambling for gains  Typical preference when gambling for losses Example: Bettors at at horse track bet on long shots at the end of the day (many bettors are in a state of trying to recoup losses at the end of the day). Psych 355, Miyamoto, Spr '16 Reflection Effect - Definition

Framing Effects and Focusing Illusion

Outline Framing effects Affective forecasting The focusing illusion
Example: The Asian Disease Problem Affective forecasting The focusing illusion Reminder: Course evaluations Reminder: Risk Attitude Psych 355, Miyamoto, Spr ‘16

Risk Aversion Risk Seeking Risk Attitude
Mr. A's attitude toward monetary risks is risk averse if Mr. A prefers a sure-thing of $y over a gamble G whose expected value is slightly greater than $y. E.g., prefers $5,000 to a gamble for $10,100 and $0. Risk Seeking Ms. B's attitude toward monetary risks is risk seeking if Ms. B prefers a gamble G* over a sure-thing $z when the expected value of G* is slightly less than $z. E.g., prefers a gamble for $12,000 over a sure-thing of $6,500. Reminder: Risk Averse for Gains; Risk Seeking for Losses; Reflection Effect Psych 355,, Miyamoto, Spr '16

Risk Averse for Gains/Risk Seeking for Losses
Choice Among Gains Example: Choice 1: Which would you prefer? Option A: chance to win $3,000. Option B: .80 chance to win $4,000. Choice 2: Which would you prefer? Option C: 1.0 chance to lose $3,000. Option D: .80 chance to lose $4,000. Choose between: * A Sure-GAIN, X. * A gamble G, where the expected value of G is somewhat greater than X. Choice Among Losses Example: Bettors at at horse track bet on long shots at the end of the day (many bettors are in a state of trying to recoup losses at the end of the day). Choose between: * A Sure-LOSS, X. * A gamble G, where the expected value of G < X. The pattern, risk averse for gains and risk seeking for losses, is called the reflection effect. Framing Effect - Definition Psych 355, Miyamoto, Spr '16

Framing Effects Definition: A framing effect has occurred if people’s preferences change when: the description of the choice problem is changed, and ... the content of the choice problem is not changed By “content” I mean the logical structure of the problem. If two problems are logically equivalent, they have the same content. The content is the same if different versions of a problem have the same probabilities and the same outcomes – only the wording or "framing" of the problem changes. Basic Principle of Rational Choice: The framing of a problem should not affect the decisions of a rational agent (preference should not change as a function of problem description). Psych 355, Miyamoto, Spr '16 Distinction Between Reflection Effect & Framing Effect

Reflection Effects and Framing Effects
By itself, a reflection effect is not a framing effect, but .... reflection effects can be part of what causes a framing effect. How to create a framing effect: Change the wording of the choices to emphasize gains or to emphasize losses. Emphasize gains in the options Become more risk averse Emphasize losses in the options Become more risk seeking Psych 355, Miyamoto, Spr '16 Asian Disease Problem - Gain Frame

Asian Disease Problem: Gain Frame
Problem 1: Imagine that the US is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. If Program A is adopted, 200 people will be saved. If Program B is adopted, there is 1/3 probability that 600 people will be saved, and /3 probability that no people will be saved. Which program would you favor? Psych 355, Miyamoto, Spr '16 Asian Disease Problem – Loss Frame

Asian Disease Problem: Loss Frame
Problem 2: Imagine that the US is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. If Program C is adopted, 400 people will die. If Program D is adopted there is 1/3 probability that nobody will die, and /3 probability that 600 people will die. Which program would you favor? Results – Asian Disease Problem Psych 355, Miyamoto, Spr '16

Asian Disease Problem: Results
Problem 1 [N = 152]: (Gain Frame  Risk Averse Choice) If Program A is adopted, 200 people will be saved. [72 %] If Program B is adopted, [28 %] there is 1/3 probability that 600 people will be saved, and a 2/3 probability that no people will be saved. Problem 2 [N = 155]: (Loss Frame  Risk Seeking Choice) If Program C is adopted 400 people will die. [22 %] If Program D is adopted [78 %] there is 1/3 probability that nobody will die, and a 2/3 probability that 600 people will die. Asian Disease Problem Satisfies the Definition of a Framing Effect Psych 355, Miyamoto, Spr '16

The Asian Disease Problem Is an Example of a Framing Effect
A framing effects is a change in preference that is due only to the way that the options are described. The logical structure of the choice remains the same in the different frames. In the Asian disease problem: The gain frame and loss frame versions of the problem are logically identical but ... People have different preferences depending on whether the outcomes are described as potential gains or potential losses. An emphasis on gains elicits risk averse behavior. An emphasis on losses elicits risk seeking behavior. (Remember: People tend to risk averse for gains and risk seeking for losses.) This shows that the Asian Disease Problem is an example of a framing effect. Psych 355, Miyamoto, Spr '16 Comment on the Name - "Asian Disease Problem"

Comment on the Name: "The Asian Disease Problem"
Goldstein discusses the Asian Disease Problem, but the textbook does not refer to this problem by this name. Goldstein discusses a health policy decision on p Experimental results are presented in Figure 13.7 (p. 385). This decision is actually a description of the Asian Disease Problem, but it is not referred to by that name. The most common name for this problem in the cog psych literature is the "Asian Disease Problem." Summary re Framing Effects Psych 355, Miyamoto, Spr '16

Summary re Framing Effects
Reflection effect (not a framing effect): People are generally risk averse for gains and risk seeking for losses. Framing effects are produced when we change the emphasis from gains to losses without changing the “bottom lines” for the options. (Asian Disease Problem) There are other examples of framing effects that do not involve manipulating the framing of gains and losses (see examples of mental accounting). Basic Psychological Principle: Judgments are Relative Psych 355, Miyamoto, Spr '16

Humans Respond to Relative Values of Outcomes, Not Absolute Values of Outcomes
Humans do not judge the absolute magnitude of dimensions. Human judgment is comparative: We judge how much better or worse one state is than another. Examples When we evaluate risks we encode the outcomes as gains or losses relative to a standard of comparison like my personal current status. (We don't consider outcomes as absolute amounts without respect to our status quo.) Next example: Focusing illusion Affective Forecasting Psych 355, Miyamoto, Spr '16

Affective Forecasting
Affective forecasting – predicting how we will feel if different outcomes were to occur. Brickman et al. – Small differences in life satisfaction between paraplegics and lottery winners. Sackett & Torrance report that the general public rated life on chronic dialysis as 39 whereas patients who were treated with chronic dialysis rated it as 56 (100 point scale). Mellers & McGraw – Women who did not want to be pregnant, awaiting pregnancy test results at a Planned Parenthood clinic, anticipated more negative affect if they found out they were pregnant than they actually did feel if they turned out to be pregnant. Psych 355, Miyamoto, Spr '16 Why Do People Have Difficulty with Affective Forecasting?

Why Do People Have Difficulty With Affective Forecasting?
People have incomplete or inaccurate self-theories. Response to novel experiences is hard to anticipate. Affective set point – overall happiness/unhappiness is determined by internal personal factors. People sometimes fail to take this into account. Possible biases: Focusing illusion Impact bias Duration neglect Focusing Illusion Psych 355, Miyamoto, Spr '16

Focusing Illusion Focusing illusion – if attention is focused on some but not all of attributesa of an option, these attributes will have greater influence over the predicted affective outcome than they will if the outcome is actually experienced. a An "attribute" is a discernable quality of the option; other words for the same idea would be "aspects of an option" or "issues regarding an option." Study of Working Women – Ratings of Self and Predictions for Others Psych 355, Miyamoto, Spr '16

How Would You Feel If You Were ______ ?
Kahneman, D., Krueger, A. B., Schkade, D., Schwarz, N., & Stone, A. A. (2006). Would you be happier if you were richer? A focusing illusion. Science, 312, Participants were working women. Participants were asked .... to state the percentage of time they were in a bad mood on the preceding day, and .... to predict the percentage of time a woman similar to themselves would be in a bad mood if she were in specific social categories Contrasting social categories: income < $20,000 income > $100,000 40 yrs & living alone 40 yrs & married no health insurance has health insurance Since some women were in each of these categories, we can compare predicted differences to actual differences. Since some of the women did have income < $20,000 or > $100,000, or were alone or married, etc., we also have the actual self-rating of time in a bad mood. How Would You Feel If You Were ____? - Results Psych 355, Miyamoto, Spr '16

Would You Feel If You Were ______? - Results
Sample sizes for different groups were at least n = 59, and for most cases were 75 – 225. Mean sample size of all groups = 107. Median sample size = 84 Minimum sample size = 59 Total sample size = 1,719 In general, ACTUAL difference < PREDICTED difference The effects of good or bad circumstances were exaggerated. Good or bad circumstances do have an effect, but not as much as predicted. Psych 355, Miyamoto, Spr '16 Same Slide Without Emphasis Rectangles

Would You Feel If You Were ______? - Results
Sample sizes for different groups were at least n = 59, and for most cases were 75 – 225. Focalism (a.k.a. the focusing illusion) – people tend to overestimate the impact of variables that that they focus on. Psych 355, Miyamoto, Spr '16 Focusing Illusion - Thinking About a Possibility Makes It Seem More Influencial

Focusing Illusion - Summary
Focusing on any one aspect of a larger situation exaggerates its perceived impact on quality of life. Schkade and Kahneman (1998), "Nothing in life is quite as important as you think it is while you are thinking about it." Psych 355, Miyamoto, Spr '16 Psych 466: Judgment and Decision Making - What Is It?

Psych 466: Judgment and Decision Making
Psych 466: Judgment and Decision Making Autumn 2016 Human judgments of likelihood and uncertainty Preference - what looks more or less desirable Heuristics and biases Decisions where risk is relevant Influence of framing and context on decisions Affective forecasting Comments Regarding the Final Exam Psych 355,, Miyamoto, Spr '16

Comments Regarding the Final Exam
Bring a scantron form; bring a pencil for the scantron form. One essay question You do not need to bring a blue book to the exam. Obviously bring a pen if you prefer to write with a pen. All other questions will be multiple choice, or true/false, or short answer (fill in a few words) Approximate distribution of questions: 50% Chapters 1 – 8 50% Chapters 9, 10, 12, 13 +1 essay question Psych 355, Miyamoto, Spr '16 What Are Available Materials for Studying for the Final Exam

Materials for Studying for the Final Exam
Obviously, look at the lecture slides, your section notes, and the textbook. Available on the Psych 355 webpage: What will be covered on the Final Exam? Study questions for all chapters ( See the Spring 2015 Psych 355 exams. Use the version without answer key as well as with the answer key. Suggestions for Studying for the Final Exam Psych 355, Miyamoto, Spr '16

Suggestions for Studying for the Final Exam
Spaced practice is better than massed practice. Generate your own ideas with respect to the course material – they will serve as retrieval cues. Create good associative links to information that you want to remember. E.g., does the study material remind you of anything else that you know? E.g., does the study material help to explain something about your own experience? Test yourself without an answer key in front of you. Memory tricks: Memorization (maintenance rehearsal) is an ineffective memory aid. Create interactive images that capture ideas that you want to remember. Try to relate facts or ideas to yourself. Study in an environment that is similar to the test environment. Good Luck! Psych 355, Miyamoto, Spr '16

Best of Luck Don’t overstress for the exam.
Focusing illusion – your exam performance seems more important than it really is (for your future happiness). Have a good break END Psych 355, Miyamoto, Spr '16

End of Lectures: to 10-4 Psych 355, Miyamoto, Spr '15

Combined Lecture Slides: Weeks 08 – 10

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