We apply the “wisdom of the crowd” idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals for 28 previously collected datasets. We then extend the approach so that it does not require people to categorize every stimulus. We do this using a model‐based method that predicts the categorization behavior people would produce for new stimuli, based on their behavior with observed stimuli, and uses the majority of these predicted decisions. We demonstrate and evaluate the model‐based approach in two case studies. In the first, we use the general recognition theory decision‐bound model of categorization (Ashby & Townsend,
- Award ID(s):
- 1631428
- NSF-PAR ID:
- 10299594
- Date Published:
- Journal Name:
- Neural Computation
- Volume:
- 33
- Issue:
- 2
- ISSN:
- 0899-7667
- Page Range / eLocation ID:
- 376 to 397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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