Two perils of binary categorization: Why the study of concepts can’t afford true/false testing
- Published
- Accepted
- Subject Areas
- Animal Behavior, Psychiatry and Psychology
- Keywords
- Machine Learning, Categorization, Animal Cognition, Concepts, Comparative Cognition
- Copyright
- © 2015 Jensen et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Two perils of binary categorization: Why the study of concepts can’t afford true/false testing. PeerJ PrePrints 3:e688v2 https://doi.org/10.7287/peerj.preprints.688v2
Abstract
In this opinion piece, we outline two shortcomings in experimental design that limit the claims that can be made about concept learning in animals. On the one hand, most studies of concept learning train too few concepts in parallel to support general claims about their capacity of subsequent abstraction. On the other hand, even studies that train many categories of stimulus in parallel only test one or two stimuli at a time, allowing even a simplistic learning rule to succeed by making informed guesses. To demonstrate these shortcomings, we include simulations performed using an off-the-shelf image classifier. These simulations demonstrate that, when either training or testing are overly simplistic, a classification algorithm that is incapable of abstraction nevertheless yields levels of performance that have been described in the literature as proof of concept learning in animals.
Author Comment
This updated version contains minor modifications for clarification purposes.