Categorization

How do we organize our knowledge? How do we retrieve knowledge when we

need it?

Do We Use Categories?

Visual agnosias (Warrington & Shallice, 1984) Double-dissociation for living and non-living Damage to Inferotemporal lobe

Category-specific neurons in temporal lobe (Kreiman et al., 2000)

Levels of Categorization

Superordinate, Basic, Subordinate More features named on Basic level (Rosch

et al., 1976) Objects tend to be named at the Basic level

(Rosch et al., 1976) Basic level can depend on knowledge –

cultural differences

Definitional Approach

Definitions determine category membership But most natural categories do not have

defining features Elements of a category instead show a

“family resemblance”

Spreading Activation Model (Collins & Loftus, 1975)

Concepts are organized in a hierarchy and linked together Cognitive Economy Spreading Activation

Example Of Network

ANIMAL eatshas skincan move

FISHhas scales

swimshas gills

BIRDhas wingsflies

has feathers

CANARYsingsis yellowTweety is one

Spreading Activation Model

Takes longer to verify statements that require travel over more nodes “A canary can fly “ - one node “A canary has skin” - two nodes up, takes about

90 ms longer to verify (Collins & Quillian, 1969)

Faster RT’s to related words in Lexical Decision Task (Meyer & Schvaneveldt, 1975)

Hedged Statements

Statements that are qualified, like: Technically, a chicken is a bird. A penguin is sort of a bird. Loosely speaking, a bat is a bird.

How do we evaluate these?

Prototype Approach(Rosch, 1973)

Compare object to the prototype of a category

Prototype is a “typical” example Average of examples (Rosch) OR Common exemplars (Medin et al., 1982)

Prototype Approach

Typicality Effect in sentence verification (Smith et al., 1974)

Typicality Effect in priming (Rosch, 1975)

Parallel Distributed Processing (Rumelhart & McClelland, 1986)

Also called Connectionist or PDP Memories are patterns of activation among a

set of “units” Each unit has an activation value Connections between units are weighted Model can learn (change weights) based on

feedback – back propagation

PDP Model

input units

output unitshiddenunits

Evolutionary Psychology

How is categorization adaptive? What do fire, women, and dangerous things

have in common?