Chapter 7Knowledge

Terms: concept, categorization, prototype, typicality effect, object concepts, rule-governed, exemplars, hierarchical organization, basic level, superordinate/global level, subordinate/specific level, semantic network, nodes, links, spreading activation, cognitive economy, category size effect

Some Questions to Consider

• Why is it difficult to decide if a particular object belongs to a particular category, such as “chair,” by looking up its definition?

• How are the properties of various objects “filed away” in the mind?

• How is information about different categories stored in the brain?

• Can young infants respond to the categories “cat” and “dog”?

Knowledge

• Concept: mental representation used for a variety of cognitive functions

• Categorization is the process by which things are placed into groups called categories

Why Categories Are Useful

• Help to understand individual cases not previously encountered

• “Pointers to knowledge”

– Categories provide a wealth of general information about an item

– Allow us to identify the special characteristics of a particular item

Caption: Knowing that something is in a category provides a great deal of information about it.

Definitional Approach to Categorization

• Determine category membership based on whether the object meets the definition of the category

• Does not work well

• Not all members of everyday categories have the same defining features

Caption: Different objects, all possible “chairs.”

Definitional Approach to Categorization

• Family resemblance

– Things in a category resemble one another in a number of ways

The Prototype Approach

• Prototype = “typical”

• An abstract representation of the “typical” member of a category

• Characteristic features that describe what members of that concept are like

• An average of category members encountered in the past

• Contains the most salient features

• True of most instances of that category

Caption: Results of Rosch’s (1975a) experiment, in which participants judged objects on a scale of 1 (good example of a category) to 7 (poor example): (a) ratings for birds; (b) ratings for furniture.

The Prototype Approach

• High-prototypicality: category member closely resembles category prototype

– “Typical” member

– For category “bird” = robin

The Prototype Approach

• Low-prototypicality: category member does not closely resemble category prototype

– For category “bird” = penguin

The Prototype Approach

• Strong positive relationship between prototypicality and family resemblance

• When items have a large amount of overlap with characteristics of other items in the category, the family resemblance of these items is high

• Low overlap = low family resemblance

The Prototype Approach

• Typicality effect: prototypical objects are processed preferentially

– Highly prototypical objects judged more rapidly

• Sentence verification technique

Caption: Results of E.E. Smith et al.’s (1974) sentence verification experiment. Reaction times were faster for objects rated higher in prototypicality

The Prototype Approach

• Typicality effect: prototypical objects are processed preferentially

– Prototypical objects are named more rapidly

The Prototype Approach

• Typicality effect: prototypical objects are processed preferentially

– Prototypical category members are more affected by a priming stimulus

– Rosch (1975b)

• Hearing “green” primes a highly prototypical “green”

Caption: Procedure for Rosch’s (1975b) priming experiment. Results for the conditions when the test colors were the same are shown on the right. (a) The person’s “green” prototype matches the good green, but (b) is a poor match for the light green.

The Exemplar Approach

• Concept is represented by multiple examples (rather than a single prototype)

• Examples are actual category members (not abstract averages)

• To categorize, compare the new item to stored examples

The Exemplar Approach

• Similar to prototype view– Representing a category is not defining it

• Different: representation is not abstract– Descriptions of specific examples

• The more similar a specific exemplar is to a known category member, the faster it will be categorized

The Exemplar Approach

• Explains typicality effect

• Easily takes into account atypical cases

• Easily deals with variable categories

Prototypes or Exemplars?

• May use both

• Exemplars may work best for small categories

• Prototypes may work best for larger categories

Caption: Levels of categories for (a) furniture and (b) vehicles. Rosch provided evidence for the idea that the basic level is “psychologically privileged.”

Caption: Left column: category levels; middle column: examples of each level for furniture; right column: average number of common features, listed from Rosch, Mervis et al.’s (1976) experiment.

Evidence that Basic-Level Is Special

• People almost exclusively use basic-level names in free-naming tasks

• Quicker to identify basic-level category member as a member of a category

• Children learn basic-level concepts sooner than other levels

• Basic-level is much more common in adult discourse than names for superordinate categories

• Different cultures tend to use the same basic-level categories, at least for living things

A Hierarchical Organization

• To fully understand how people categorize objects, one must consider

– Properties of objects

– Learning and experience of perceivers

Caption: Results of Tanaka and Taylor’s (1991) “expert” experiment. Experts (left pair of bars) used more specific categories to name birds, whereas nonexperts (right pair of bars) used more basic categories.

Semantic Networks

• Concepts are arranged in networks that represent the way concepts are organized in the mind

Semantic Networks

• Collins and Quillian (1969)

– Node = category/concept

– Concepts are linked

– Model for how concepts and properties are associated in the mind

Caption: Collins and Quillian’s (1969) semantic network. Specific concepts are indicated in blue. Properties of concepts are indicated at the nodes for each concept. Additional properties of a concept can be determined by moving up the network, along the lines connecting the concepts. For example, moving from “canary” up to “bird” indicates that canaries have feathers and wings and can fly.

Semantic Networks

• 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

• Category size effect: verifying a concept as a member of a smaller category is faster than verifying it as a member of a larger category

Caption: The distance between concepts predicts how long it takes to retrieve information about concepts as measured by the sentence verification technique. Because it is necessary to travel on two links to get from canary to animal (left), but on only one to get from canary to bird (right) it should take longer to verify the statement “a canary is an animal.”

Semantic Networks

• Spreading activation– 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

Semantic Networks

• Lexical decision task

– Participants read stimuli and are asked to say as quickly as possible whether the item is a word or not

Semantic Networks

• Myer and Schvaneveldt (1971)

– “Yes” if both strings are words; “no” if not

– Some pairs were closely associated

– Reaction time was faster for those pairs

• Spreading activation

Semantic Networks

• Criticism of Collins and Quillian

– Cannot explain typicality effects (typical members verified as such more quickly)

– Cognitive economy?

– Some sentence-verification results are problematic for the model

• e.g., can’t explain reversal of category size effect (“A dog is an animal” faster than “A dog is a mammal”)

Semantic Networks: Spreading Activation Model (extension of hierarchical model)

• Collins and Loftus (1975) modifications

– Shorter links to connect closely related concepts

– Longer linkers for less closely related concepts

– No hierarchical structure; based on person’s experience

Caption: Semantic network proposed by Collins and Loftus (1975). (Reprinted from A.M. Collins & E.F. Loftus, “A Spreading-Activation Theory of Semantic Processing.” From Psychological Review, 82, pp. 407-428, Fig. 1. Copyright © 1975 with permission from the American Psychological Association.

Assessment of Semantic Networks

• Is predictive and explanatory of some results, but not all

• Generated multiple experiments

• Lack of falsifiability

– No rules for determining link length or how long activation will spread

– Therefore, there is no experiment that would “prove it wrong”

– Circular reasoning