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Memory and Cognition PSY 324 Topic 8: Knowledge Dr. Ellen Campana Arizona State University.

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1 Memory and Cognition PSY 324 Topic 8: Knowledge Dr. Ellen Campana Arizona State University

2 Concepts and Categories

3 Why do we have concepts? Last time … memories constructed through the process of inference Last time … memories constructed through the process of inference War of the ghosts War of the ghosts Estimating high school grades Estimating high school grades Flashbulb memories (and other episodic memories) Flashbulb memories (and other episodic memories) Inference happens all the time, not just in memory Inference happens all the time, not just in memory New store opens, scripts help us know how to buy New store opens, scripts help us know how to buy Strange hungry (but healthy) cat at the door… food? Strange hungry (but healthy) cat at the door… food?

4 Concepts Concepts are mental representations that make it possible to do inference, understand language, do reasoning, and remember Concepts are mental representations that make it possible to do inference, understand language, do reasoning, and remember Make up semantic memories from last section Make up semantic memories from last section Used for construction and other inferences, so also part of episodic memory Used for construction and other inferences, so also part of episodic memory Can be used for categorization Can be used for categorization Entities placed into groups called categories Entities placed into groups called categories

5 Categories Knowing what category an entity is a member of gives you a lot of information about it Without concepts all knowledge about each entity would come from experience with that entity Without concepts all knowledge about each entity would come from experience with that entity Each restaurant, each person, each cat, each class, etc. Broken pencil replaced – learn to use it for writing all over Helps explain things that would otherwise be odd Pittsburgh Steelers fan Pittsburgh Steelers fan

6 Categories Likes to rub up against people and objects Likes milk, fish A feline: related to lions and tigers Difficult to train Has a tail Sleeps a lot, but more active at night Catches mice CAT

7 Categories TEAPOT

8 Models of Categorization Definitional Approach (Aristotle) Definitional Approach (Aristotle) Membership by definition, like a checklist Membership by definition, like a checklist Family Resemblances (Wittgenstein) Family Resemblances (Wittgenstein) Membership by similarity Membership by similarity Prototype Approach (Rosch) Prototype Approach (Rosch) Membership by similarity to average of category Membership by similarity to average of category Exemplar Approach Exemplar Approach Membership by similarity to examples of members Membership by similarity to examples of members More specific version of family resemblances More specific version of family resemblances

9 Definitional Approach In geometry a square can be defined as a plane figure having four equal sides In geometry a square can be defined as a plane figure having four equal sides Features of a square: planar figure, four sides, sides are equal Features of a square: planar figure, four sides, sides are equal If something has all of these, it is a square If something has all of these, it is a square If something is a square it must have all of these If something is a square it must have all of these Definitional approach uses definitions like this for everything Definitional approach uses definitions like this for everything Bachelor: Male, unmarried, adult, human Bachelor: Male, unmarried, adult, human What about the pope? What about the pope?

10 Definitional Approach Assumes Assumes Sharp category boundary (in or out) Sharp category boundary (in or out) Equality of members Equality of members Representation of category is list of necessary and sufficient features Representation of category is list of necessary and sufficient features If an entity meets the conditions it is a member If an entity meets the conditions it is a member If an entity is a member we know it meets the conditions If an entity is a member we know it meets the conditions In practice it is difficult to find such features In practice it is difficult to find such features Is a bookend furniture? Is a bookend furniture?

11 Family Resemblance No one feature that all have in common…. No one feature that all have in common…. … Yet all are in some way similar to the other category members … Yet all are in some way similar to the other category members Variation within categories OK Variation within categories OK

12 Family Resemblance Assumes Assumes No strict definition of whats in/out based on individual features No strict definition of whats in/out based on individual features Membership based on similarity Membership based on similarity Some members can be better examples than others Some members can be better examples than others Think about it long and it gets confusing…. Think about it long and it gets confusing…. Similarity, but similarity to what? Similarity, but similarity to what? Next two approaches are more detailed versions Next two approaches are more detailed versions

13 Prototype Approach Whats a prototype? An average of all members Whats a prototype? An average of all members The guy in the center is closest to the prototype (highest prototypicality) but he isnt the prototype The guy in the center is closest to the prototype (highest prototypicality) but he isnt the prototype Prototype isnt here Prototype isnt here

14 Prototype Approach Features: Features: Glasses (yes/no) Glasses (yes/no) Hair (dark/light) Hair (dark/light) Nose (big/small) Nose (big/small) Ears (big/small) Ears (big/small) Mustache (yes/no) Mustache (yes/no) Prototype Prototype 2/3 glasses, 7/9 light hair, 7/9 big nose, 7/9 big ears, 5/9 mustache 2/3 glasses, 7/9 light hair, 7/9 big nose, 7/9 big ears, 5/9 mustache Center guy has the highest prototypicality Center guy has the highest prototypicality

15 Support for Prototypes Rosch (1975)- prototypicality rating Rosch (1975)- prototypicality rating Participants got category names (bird) and lists of 50 members (robin, canary, ostrich, penguin, sparrow…) Participants got category names (bird) and lists of 50 members (robin, canary, ostrich, penguin, sparrow…) Provided rating on how well the item represented the category Provided rating on how well the item represented the category Results: Much agreement on ratings between participants Results: Much agreement on ratings between participants

16 Rosch (1975) Very Good Poor CATEGORY: BIRDS BatPenguin OwlSparrow Very Good Poor CATEGORY: FURNITURE TelephoneMirror China Closet Chair, Sofa

17 Rosch and Mervis (1975) Participants wrote down as many characteristics / attributes as they could think of for each item Participants wrote down as many characteristics / attributes as they could think of for each item Bicycle: two wheels, you ride them, handlebars, pedals, dont use fuel…. Bicycle: two wheels, you ride them, handlebars, pedals, dont use fuel…. Dog: have four legs, bark, have fur… Dog: have four legs, bark, have fur… When attributes overlap with many other members attributes family resemblance is high When attributes overlap with many other members attributes family resemblance is high Results: Items with high prototypicality ratings also have high family resemblance Results: Items with high prototypicality ratings also have high family resemblance Chairs and sofas, birds, etc. Chairs and sofas, birds, etc.

18 Other Studies about Prototypes Smith and Coworkers (1974)- Typicality Effect Smith and Coworkers (1974)- Typicality Effect Used sentence verification Used sentence verification T/F: an apple is a fruit T/F: an apple is a fruit T/F: a pomegranate is a fruit T/F: a pomegranate is a fruit Results: sentences about highly prototypical objects are judged more quickly (typicality effect) Results: sentences about highly prototypical objects are judged more quickly (typicality effect) Mervis and Coworkers (1976) Mervis and Coworkers (1976) When people name objects in a category, the most prototypical objects tend to come first When people name objects in a category, the most prototypical objects tend to come first

19 Other Studies about Prototypes Rosch (1975b) – repetition priming Rosch (1975b) – repetition priming Prototypical members of a category are affected by a priming stimulus more than nonprototypical ones Prototypical members of a category are affected by a priming stimulus more than nonprototypical ones Task: Ignore words, just say whether the two color circles match or not Task: Ignore words, just say whether the two color circles match or not Hear Green Same Different Same 610ms 780ms

20 Other Studies about Prototypes Rosch (1975b) – repetition priming Rosch (1975b) – repetition priming Take-away message: people are faster to respond same when the colors are more highly prototypical Take-away message: people are faster to respond same when the colors are more highly prototypical Word green may be linked to the prototype Word green may be linked to the prototype Hear Green Same Different Same 610ms 780ms

21 Exemplar Approach Exemplars are specific examples – provide another account for the effects of we have seen Exemplars are specific examples – provide another account for the effects of we have seen Examples of category members are saved in memory (typical as well as atypical) Examples of category members are saved in memory (typical as well as atypical) Potential members compared to all exemplars Potential members compared to all exemplars Those with high family resemblance are like more of the exemplars Those with high family resemblance are like more of the exemplars Remember: Family resemblance correlates w/ prototypicality Remember: Family resemblance correlates w/ prototypicality May be more useful for smaller categories (US presidents, very tall mountains) May be more useful for smaller categories (US presidents, very tall mountains)

22 Exemplar Approach Features: Features: Glasses (yes/no) Glasses (yes/no) Hair (dark/light) Hair (dark/light) Nose (big/small) Nose (big/small) Ears (big/small) Ears (big/small) Mustache (yes/no) Mustache (yes/no) Why does center guy fit? Why does center guy fit? Glasses like 2/3, light hair like 7/9, big nose like 7/9, big ears like 7/9, mustache like 5/9 Glasses like 2/3, light hair like 7/9, big nose like 7/9, big ears like 7/9, mustache like 5/9 Center guy has high family resemblance Center guy has high family resemblance

23 Models of Categorization Four Approaches Four Approaches Definitional Approach (Aristotle) Definitional Approach (Aristotle) Family Resemblances (Wittgenstein) Family Resemblances (Wittgenstein) Prototype Approach (Rosch) Prototype Approach (Rosch) Exemplar Approach Exemplar Approach Current consensus – people use both the prototype approach and the exemplar approach (perhaps for different types of categories) Current consensus – people use both the prototype approach and the exemplar approach (perhaps for different types of categories)

24 Levels of Categories

25 Categories have hierarchical organization Categories have hierarchical organization Is one level more important or privileged? Is one level more important or privileged? Furniture ChairTable Kitchen Dining room Kitchen Dining room Superordinate Level Basic Level Subordinate Level

26 Levels of Categories LEVELEXAMPLE SuperordinateFurniture BasicTable Subordinate Kitchen Table Common Features Lose a lot of information Gain just a little info

27 Levels of Categories Rosch, Mervis and Coworkers (1976) Rosch, Mervis and Coworkers (1976) Rating of common features for each category Rating of common features for each category BASIC level seems to be special (go higher and you lose a lot of information, go lower and it doesnt help much) BASIC level seems to be special (go higher and you lose a lot of information, go lower and it doesnt help much) When given a picture (Levis/Jeans/Clothes) people label it with the basic level category When given a picture (Levis/Jeans/Clothes) people label it with the basic level category Rosch, Simpson and Coworkers (1976) Rosch, Simpson and Coworkers (1976) Category label followed by picture – is it a member? Category label followed by picture – is it a member? People were faster for basic level categories People were faster for basic level categories Coley and Coworkers (1997) Coley and Coworkers (1997) Students described trees with the word trees rather than oak tree or plant Students described trees with the word trees rather than oak tree or plant

28 Experience and Categories When people become experts at a category, they tend to use more specific categories When people become experts at a category, they tend to use more specific categories Tanaka & Taylor (1991) – bird experts and novices Tanaka & Taylor (1991) – bird experts and novices Experts: robin, sparrow, jay, cardinal Experts: robin, sparrow, jay, cardinal Nonexperts: bird Nonexperts: bird Members of Itza culture use oak tree, not tree Members of Itza culture use oak tree, not tree Partly because they live in close contact with the natural environment Partly because they live in close contact with the natural environment Basic levels may not be so special for everyone (depends on experience) Basic levels may not be so special for everyone (depends on experience)

29 Semantic Networks

30 Semantic networks describe a theory for how concepts are organized in the mind Semantic networks describe a theory for how concepts are organized in the mind Goal: to develop a computer model of memory Goal: to develop a computer model of memory Model well discuss is Collins & Quillian (1969) Model well discuss is Collins & Quillian (1969) Network of nodes connected by links Network of nodes connected by links Nodes = individual categories or concepts Nodes = individual categories or concepts Links = relationships between categories or concepts Links = relationships between categories or concepts Nodes are also associated with properties Nodes are also associated with properties

31 Semantic Network Collins & Quillian (1969) Animal BirdFish CanaryOstrich SharkSalmon Has skin Can move around Eats Breathes Has fins Can swim Has gills Swims upstream to lay eggs Is pink Is edible Can bite Is dangerous Has wings Can fly Has feathers Has long thin legs Is tall Cant fly Is yellow Can sing

32 Semantic Network Collins & Quillian (1969) Information about the properties of an individual category is retrieved by accessing a node, then following links until we find the desired property (or its opposite) Information about the properties of an individual category is retrieved by accessing a node, then following links until we find the desired property (or its opposite) Properties are associated with the highest-level category that they apply to in general Properties are associated with the highest-level category that they apply to in general saves space in memory (Cognitive economy) saves space in memory (Cognitive economy) Exceptions added at lower nodes to deal with unusual cases Exceptions added at lower nodes to deal with unusual cases Example: Properties of a canary Example: Properties of a canary

33 Properties of a Canary Animal Bird Canary Has skin Can move around Eats Breathes Has wings Can fly Has feathers Is yellow Can sing Is yellow Has wings Can fly Has feathers Breathes Has skin Can move around Eats List of all properties

34 Properties of an Ostrich Animal Bird Has skin Can move around Eats Breathes Has wings Can fly Has feathers Cant fly Has long thin legs Is tall Has wings Can fly Has feathers Breathes Has skin Can move around Eats List of all properties Ostrich Has long thin legs Is tall Cant fly

35 Semantic Network Collins & Quillian (1969) Network is a functional model, not a physiological one Network is a functional model, not a physiological one It is designed to address how concepts are organized in the mind, not the brain It is designed to address how concepts are organized in the mind, not the brain Nodes DO NOT correspond to specific brain areas Nodes DO NOT correspond to specific brain areas Links DO NOT correspond to connections between neurons or networks of neurons Links DO NOT correspond to connections between neurons or networks of neurons But how well does the model fit with data on how the mind organizes information? But how well does the model fit with data on how the mind organizes information?

36 Properties of a Canary Animal Bird Canary Has skin Can move around Eats Breathes Has wings Can fly Has feathers Is yellow Can sing Is yellow Has wings Can fly Has feathers Breathes Has skin Can move around Eats List of all properties Levels away from canary 0 1 2

37 Collins and Quillian (1969) Experiment: timed true/false judgments Experiment: timed true/false judgments Compared properties with different distances from the original concept Compared properties with different distances from the original concept Measured reaction time to simple statements Measured reaction time to simple statements

38 Collins and Quillian (1969) A canary can sing A canary can fly A canary has skin A canary is a canary A canary is a bird A canary is an animal Reaction time (higher is slower) Levels away from canary 012

39 Collins and Quillian (1969) Experiment: timed true/false judgments Experiment: timed true/false judgments Compared properties with different distances from the original concept Compared properties with different distances from the original concept Measured reaction time to simple statements Measured reaction time to simple statements Results: As properties increased in distance from the concept node, reaction times increased Results: As properties increased in distance from the concept node, reaction times increased Provides support for counter-intuitive claims related to cognitive economy Provides support for counter-intuitive claims related to cognitive economy Provides support for the model in general Provides support for the model in general

40 Spreading Activation We have been talking about retrieval as traveling through the semantic network, but the model actually claims that retrieval happens through the process of spreading activation We have been talking about retrieval as traveling through the semantic network, but the model actually claims that retrieval happens through the process of spreading activation Whenever a node becomes active, some activity travels through links to closely associated nodes Whenever a node becomes active, some activity travels through links to closely associated nodes Spreading activation leads to further predictions in the model Spreading activation leads to further predictions in the model Priming of associated concepts Priming of associated concepts

41 Spreading Activation Network as a person searches from canary to bird Network as a person searches from canary to bird Activation spreads from bird to all linked nodes Activation spreads from bird to all linked nodes Nodes that get activation are primed (faster to recognize) Nodes that get activation are primed (faster to recognize) Animal Bird Canary Robin Ostrich

42 Priming Priming leads to faster recognition of concepts Priming leads to faster recognition of concepts One type is repetition priming, which you know One type is repetition priming, which you know Myer and Schvaneveldt (1971) looked at priming of associates using a lexical decision method Myer and Schvaneveldt (1971) looked at priming of associates using a lexical decision method Presented pairs of words, participants decided whether or not both words in the pair were real words Presented pairs of words, participants decided whether or not both words in the pair were real words YES: Chair/Money or Bread/Wheat YES: Chair/Money or Bread/Wheat NO: Fundt/Glurb or Bleem/Dress NO: Fundt/Glurb or Bleem/Dress Critical comparison: pairs of real words, close associates vs. distantly related concepts Critical comparison: pairs of real words, close associates vs. distantly related concepts

43 Myer & Schvaneveldt (1971) Reaction Time Words Associated Words Not Associated

44 Problems with C & Q model Didnt account for typicality effect Didnt account for typicality effect A canary is a bird verified faster than an ostrich is a bird A canary is a bird verified faster than an ostrich is a bird Model predicts equal reaction times for both Model predicts equal reaction times for both Evidence against cognitive economy idea Evidence against cognitive economy idea People store some properties at concept node People store some properties at concept node Evidence against hierarchical structure Evidence against hierarchical structure A pig is an animal verified faster than a pig is a mammal A pig is an animal verified faster than a pig is a mammal Model predicts the opposite because mammal is supposed to be between pig and animal Model predicts the opposite because mammal is supposed to be between pig and animal

45 Collins and Lofus Version Abandon hierarchy structure in favor of a structure based on individual experience Abandon hierarchy structure in favor of a structure based on individual experience Allowed multiple links between concepts Allowed multiple links between concepts Pig to animal AND pig to mammal to animal Pig to animal AND pig to mammal to animal Link distances could affect spread of activation Link distances could affect spread of activation Shorter links for closely related concepts Shorter links for closely related concepts Longer links for more distantly related concepts Longer links for more distantly related concepts This accounts for typicality effect This accounts for typicality effect

46 Collins and Loftus Version Street Vehicle Car TruckBus Ambulance Fire Engine Too powerful!

47 A Theory that Explains Too Much? Collins & Loftuss model rejected for being too powerful (Johnson-Laird & Coworkers, 1984) Collins & Loftuss model rejected for being too powerful (Johnson-Laird & Coworkers, 1984) Too difficult to falsify Too difficult to falsify Model can explain any pattern of data by adjusting link length Model can explain any pattern of data by adjusting link length No definite methods for determining length No definite methods for determining length Can vary from person to person Can vary from person to person No constraints on how long activation hangs around, or how much information is needed to trigger a node No constraints on how long activation hangs around, or how much information is needed to trigger a node

48 Elements of Good Psychological Theories Explanatory power – the theory can tell us what caused a specific behavior Explanatory power – the theory can tell us what caused a specific behavior Predictive power – the theory can make predictions about future experiments Predictive power – the theory can make predictions about future experiments Falsifiability – the theory can be shown to be wrong through specific experimental outcomes Falsifiability – the theory can be shown to be wrong through specific experimental outcomes Generation of experiments – the theory stimulates a lot of research to test the theory, improve it, use methods suggested by it, and/or study new questions that it raises Generation of experiments – the theory stimulates a lot of research to test the theory, improve it, use methods suggested by it, and/or study new questions that it raises

49 The Connectionist Approach

50 Connectionist Networks Research in semantic networks dropped by 80s but came back with rise of connectionism Research in semantic networks dropped by 80s but came back with rise of connectionism Book series: Parallel Distributed Processing… (McClelland & Rumelhart, 1986) Book series: Parallel Distributed Processing… (McClelland & Rumelhart, 1986) Connectionist models contain structures like nodes and links, but they operate much differently from semantic networks Connectionist models contain structures like nodes and links, but they operate much differently from semantic networks Inspired by the biology of the nervous system, specifically neurons in the brain Inspired by the biology of the nervous system, specifically neurons in the brain

51 Connectionist Networks Units – neuron-like, connect to form circuits, can be activated, can inhibit/excite other units Units – neuron-like, connect to form circuits, can be activated, can inhibit/excite other units Input units – activated by stimulation from environment (like receptors) Input units – activated by stimulation from environment (like receptors) Hidden units – get input from input units, connect to output units Hidden units – get input from input units, connect to output units Output units – get input only from hidden units Output units – get input only from hidden units Concept = pattern of activation across units Concept = pattern of activation across units distributed coding (unlike semantic nets!) distributed coding (unlike semantic nets!)

52 Connectionist Networks Processing is achieved by weights at each connection between neurons Processing is achieved by weights at each connection between neurons Positive weights = excitation in neural system Positive weights = excitation in neural system Negative weights = inhibition in neural system Negative weights = inhibition in neural system Can vary in connection strength, too Can vary in connection strength, too Connectionist approach also called Parallel Distributed Processing (PDP) approach Connectionist approach also called Parallel Distributed Processing (PDP) approach Representation is distributed across neurons Representation is distributed across neurons Processing happens in parallel Processing happens in parallel

53 A Connectionist Network Output Units Hidden Units Input Units

54 Supervised Learning Weights modified through supervised learning Weights modified through supervised learning Like a child, making mistakes and being corrected Like a child, making mistakes and being corrected In the beginning all weights are random In the beginning all weights are random This is a computer program (not a person) This is a computer program (not a person)

55 Steps in Supevised Learning Step 1: input presented, activation propagates through the layers to the output layer Step 1: input presented, activation propagates through the layers to the output layer

56 Output Units Hidden Units Input Units CANARY

57 Steps in Supevised Learning Step 1: input presented, activation propagates through the layers to the output layer Step 1: input presented, activation propagates through the layers to the output layer Step 2: output compared to correct output, difference is an error signal Step 2: output compared to correct output, difference is an error signal

58 +5 Output Units Hidden Units Input Units CANARY Correct Pattern Error Signal

59 Steps in Supevised Learning Step 1: input presented, activation propagates through the layers to the output layer Step 1: input presented, activation propagates through the layers to the output layer Step 2: output compared to correct output, difference is an error signal Step 2: output compared to correct output, difference is an error signal Step 3: error signal is used to adjust weights using a process called back propagation Step 3: error signal is used to adjust weights using a process called back propagation Connections that contributed most error are adjusted the most Connections that contributed most error are adjusted the most

60 +5 Weights are all Adjusted CANARY Correct Pattern Error Signal

61 Steps in Supevised Learning Step 1: input presented, activation propagates through the layers to the output layer Step 1: input presented, activation propagates through the layers to the output layer Step 2: output compared to correct output, difference is an error signal Step 2: output compared to correct output, difference is an error signal Step 3: error signal is used to adjust weights using a process called back propagation Step 3: error signal is used to adjust weights using a process called back propagation Connections that contributed most error are adjusted the most Connections that contributed most error are adjusted the most Repeat the whole thing many times Repeat the whole thing many times Stop when error signal is 0 Stop when error signal is 0

62 Output Units Hidden Units Input Units CANARY Correct Pattern Error Signal

63 Supervised Learning Supervised learning may seem straightforward, but the trick is training the network to represent many concepts at the same time Supervised learning may seem straightforward, but the trick is training the network to represent many concepts at the same time It can do it if learning all concepts at the same time It can do it if learning all concepts at the same time Making only small changes to the weights helps Making only small changes to the weights helps Hidden units free to respond in any pattern Hidden units free to respond in any pattern Over time ( trials), hidden unit patterns differ for different concepts Over time ( trials), hidden unit patterns differ for different concepts Similar concepts have similar hidden unit activation Similar concepts have similar hidden unit activation

64 Advantages of Connectionist Models Goals of the approach Goals of the approach Slow learning process creates network that can handle many different inputs Slow learning process creates network that can handle many different inputs Representation is distributed (as in the brain) Representation is distributed (as in the brain) Graceful degradation: Damage and/or incomplete information does not completely disrupt a trained network Graceful degradation: Damage and/or incomplete information does not completely disrupt a trained network Learning can be generalized: because similar concepts have similar patterns, network can make predictions about concepts it has never seen Learning can be generalized: because similar concepts have similar patterns, network can make predictions about concepts it has never seen Computer models have been developed that match some aspects of human performance and deficits related to brain damage Computer models have been developed that match some aspects of human performance and deficits related to brain damage

65 Connectionism Today Opinions are divided Opinions are divided Some like the connection with biology, while some think the connection is tenuous at best Some like the connection with biology, while some think the connection is tenuous at best Recent trend in cognitive science to return to ideas about semantic networks and combine them with connectionist approaches Recent trend in cognitive science to return to ideas about semantic networks and combine them with connectionist approaches Those who are really interested in connectionist models and what they can express have begun to call their work connection science (not cognitive science or cognitive psychology) Those who are really interested in connectionist models and what they can express have begun to call their work connection science (not cognitive science or cognitive psychology)

66 Categories in the Brain Warrington and Shallice (1984) – patients with damage to the inferior temporal lobe (IT) Warrington and Shallice (1984) – patients with damage to the inferior temporal lobe (IT) Visual agnosia – patients can see, but not name Visual agnosia – patients can see, but not name Double dissociation for living / nonliving things Double dissociation for living / nonliving things Hills and Caramazza (1991) – IT damage as well Hills and Caramazza (1991) – IT damage as well Patient could not name nonliving things or some living things (fruits & veggies), could name other living things (animals) Patient could not name nonliving things or some living things (fruits & veggies), could name other living things (animals) Chao and Coworkers (1999) – fMRI shows specialized areas for categories… with overlap Chao and Coworkers (1999) – fMRI shows specialized areas for categories… with overlap

67 Categories in the Brain It looks like there are areas in the brain that are specialized for different categories It looks like there are areas in the brain that are specialized for different categories Distributed coding with overlap Distributed coding with overlap Categories with similar features, similar activation Categories with similar features, similar activation Category-specific neurons respond to individual categories like house or snake Category-specific neurons respond to individual categories like house or snake Result from single-cell recording Result from single-cell recording Specific example may cause a specialized pattern of activation across many category-specific neurons Specific example may cause a specialized pattern of activation across many category-specific neurons Example: faces from chapter 2 Example: faces from chapter 2

68 The End Reminder: Midterm 2 is next week!


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