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How many exemplars are used? Explorations with the Rex Leopold I model
Maarten De Schryver1, Katleen Vandist, Yves Rosseel
1Ghent University, Ghent, Belgium. maarten.deschryver@ugent.be
Psychonomic Bulletin & Review
|March 19, 2009
Summary
Category representation may rely on a subset of observed examples, not all of them. This study found reduced exemplar models better explained category learning than full exemplar models.
Area of Science:
- Cognitive Science
- Psychology
- Machine Learning
Background:
- Traditional models suggest categories are defined by all observed examples (exemplars).
- However, cognitive efficiency may favor using a smaller, representative subset of exemplars.
Purpose of the Study:
- To test if category representation utilizes a reduced subset of exemplars.
- To compare the explanatory power of full versus reduced exemplar models in category learning.
Main Methods:
- Replicated Experiment 1 from Nosofsky, Clark, and Shin (1989).
- Collected individual-participant data on category learning.
- Fit both full and reduced exemplar models to the empirical data.
Main Results:
- Reduced exemplar models provided a superior fit to the data compared to full exemplar models.
- This indicates that not all observed exemplars are necessary for accurate category representation.
Conclusions:
- Category representation is likely achieved through a subset of relevant exemplars.
- This finding has implications for understanding human cognition and developing more efficient computational models.
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