Chapter 9
Knowledge
Some Questions to Consider
• Why is it difficult to decide if a particular
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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”
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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
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(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
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– 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
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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 basiclevel 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
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
– Cognitive economy?
– Some sentence-verification results are
problematic for the model
Semantic Networks
• 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,
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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
The Connectionist Approach
• How do neurons that can only either signal or
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not signal (on-off) represent knowledge?
Model on digital circuitry
– 1 bit can only be on or off
– Group them together and get many more
patterns (1 byte: 00110011)
Our neurons group together to form a
network to represent concepts
A network of nodes and links but operates
very differently from semantic networks
The Connectionist Approach
• “Neuron-like units”
– Input units: activated by stimulation from
environment
– Hidden units: receive input from input units
– Output units: receive input from hidden
units
Caption: A connectionist network showing input units, hidden units, and output
units. Incoming stimuli activate the input units, and signals travel through network,
activating the hidden and output units. Note that this is an extremely simplified
version of a connectionist network. Networks used in research on connectionism
contain many more units and more complex connections between units.
The Connectionist Approach
• Parallel distributed processing
• Knowledge represented in the distributed
activity of many units
• Weights determine at each connection how
strongly an incoming signal will activate the
next unit
The Connectionist Approach
• How learning occurs
– Network responds to stimulus
– Provided with correct response
– Modifies responding to match correct
response
The Connectionist Approach
• Error signal
– Difference between actual activity of each
output unit and the correct activity
The Connectionist Approach
• Back-propagation: error signal transmitted
back through the circuit
• Indicates how weights should be changed to
allow the output signal to match the correct
signal
• The process repeats until the error signal is
zero
Caption: Learning in a connectionist network. Bars represent activity
in the eight representation units. Notice how the pattern of
activation changes as learning progresses.
The Connectionist Approach
• Slow learning process that creates a network
capable of handling a wide range of inputs
• Learning can be generalized
The Connectionist Approach
• Graceful degradation: disruption of
performance occurs gradually as parts of the
system are damaged
Categories in the Brain
• Different areas of the brain may be
specialized to process information about
different categories
– Double dissociation for categories “living
things” and “nonliving things”
Categories in the Brain
• Categories are represented by distributed
activity
– More similar patterns of brain activity for
categories with similar features
– Category-specific neurons
Caption: (a) Testing procedure for determining if monkeys can categorize cats and dogs. (b) Activity recorded
from a neuron in the monkey’s IT cortex during the testing sequence. This neuron responds more to a 60
percent dog stimulus than to a 60 percent cat stimulus during presentation of the sample. (c) Activity
recorded from a neuron in the monkey’s PF cortex during the testing sequence. This neuron responds
better to the dog stimulus during the delay and test periods.