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Category Generation. Alan Jern Charles Kemp Carnegie Mellon University. Category generation: Introduction. Observation: people can imagine and create new objects. Cobb salad. Spork. Griffin. T. Ward (1994). Category generation. Outline. Define category generation Modeling approach

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category generation

Category Generation

Alan Jern

Charles Kemp

Carnegie Mellon University

category generation introduction
Category generation: Introduction
  • Observation: people can imagine and create new objects

Cobb salad

Spork

Griffin

T. Ward (1994)

Category generation

outline
Outline
  • Define category generation
  • Modeling approach
  • Experiment
category generation1
Category generation
  • Can occur at any level in a taxonomy

Category level

Exemplar level

slide6

Training

Caesar

Greek

Classification

x1

x2

x3

x4

x5

x6

slide7

Test

Caesar

Greek

?

Classification

x1

x2

x3

x4

x5

x6

xnew

slide8

Caesar

Greek

?

Classification

x1

x2

x3

x4

x5

x6

xnew

Training

Caesar

Greek

Category generation

x1

x2

x3

x4

x5

x6

slide9

Caesar

Greek

?

Classification

x1

x2

x3

x4

x5

x6

xnew

Test

Caesar

Greek

xnew

Category generation

?

x1

x2

x3

x4

x5

x6

past modeling approaches
Past modeling approaches
  • Classification
    • Prototype model (Reed, 1972)
    • GCM (Nosofsky, 1985)
    • Rational model (Anderson, 1991)
    • ALCOVE (Kruschke, 1992)
    • SUSTAIN (Love et al., 2004)
  • Category generation
    • ?
slide11

Test

Caesar

Greek

?

Classification

x1

x2

x3

x4

x5

x6

xnew

Caesar

Greek

xnew

Category generation

?

x1

x2

x3

x4

x5

x6

our approach
Our approach
  • Bayesian
    • Learn category distribution
    • Sample from that distribution
an exemplar generation task
An exemplar generation task

Pieces

Structure

G K

N B

V S

MX

HF

WR

MX

G K

Slot 1

Slot 1

Slot 2

W

M

M

Slot 2

N

G

G

K

B

K

X

X

R

cf. Fiser & Aslin, 2001

task model
Task model

Structure

?

G K

N B

V S

MX

HF

WR

MX

G K

Slot 1

Slot 2

?

Slots

Slot 1

M

W

W

M

Slot 2

G

N

N

G

B

K

B

K

R

X

X

R

experimental design
Experimental Design
  • 18 CMU undergraduates
  • 3 structures (3 conditions)
materials
Materials

Slot 1

Z N

Q J

V S

W K

DB

Seen combination

RL

Slot 2

XM

HF

results
Results

No item generated more than twice

Humans

Model

58% of responses

(Hypothesis space:

1820 possible responses)

results1
Results
  • Classification task
    • Rate likelihood of new instances

Valid

All Distractors

1 Seen Pairing

3 Seen Pairings

2 Seen Pairings

conclusions
Conclusions
  • People can:
    • Learn latent category structure
    • Generate new category members
  • People are sensitive to frequency differences
    • Predicted by our probabilistic approach
conclusions1
Conclusions
  • Category generation is an understudied aspect of human categorization
thanks
Thanks
  • Faye Han
  • John Anderson
  • David Rakison

Credit: RaynorGanan (ragbag.tumblr.com)