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Category Generation

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

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  1. Category Generation Alan Jern Charles Kemp Carnegie Mellon University

  2. Category generation: Introduction • Observation: people can imagine and create new objects Cobb salad Spork Griffin T. Ward (1994) Category generation

  3. Outline • Define category generation • Modeling approach • Experiment

  4. Category generation • Can occur at any level in a taxonomy Category level Exemplar level

  5. Category generation This talk

  6. Training Caesar Greek Classification x1 x2 x3 x4 x5 x6

  7. Test Caesar Greek ? Classification x1 x2 x3 x4 x5 x6 xnew

  8. Caesar Greek ? Classification x1 x2 x3 x4 x5 x6 xnew Training Caesar Greek Category generation x1 x2 x3 x4 x5 x6

  9. Caesar Greek ? Classification x1 x2 x3 x4 x5 x6 xnew Test Caesar Greek xnew Category generation ? x1 x2 x3 x4 x5 x6

  10. 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 • ?

  11. Test Caesar Greek ? Classification x1 x2 x3 x4 x5 x6 xnew Caesar Greek xnew Category generation ? x1 x2 x3 x4 x5 x6

  12. Our approach • Bayesian • Learn category distribution • Sample from that distribution

  13. A category generation task … M M W G R G K B K X X R

  14. 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

  15. 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

  16. Experimental Design • 18 CMU undergraduates • 3 structures (3 conditions)

  17. Materials Slot 1 Z N Q J V S W K DB Seen combination RL Slot 2 XM HF

  18. Procedure

  19. Results No item generated more than twice Humans Model 58% of responses (Hypothesis space: 1820 possible responses)

  20. Results • Classification task • Rate likelihood of new instances Valid All Distractors 1 Seen Pairing 3 Seen Pairings 2 Seen Pairings

  21. Conclusions • People can: • Learn latent category structure • Generate new category members • People are sensitive to frequency differences • Predicted by our probabilistic approach

  22. Conclusions • Category generation is an understudied aspect of human categorization

  23. Thanks • Faye Han • John Anderson • David Rakison Credit: RaynorGanan (ragbag.tumblr.com)

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