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- W4254541473 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." @default.
- W4254541473 created "2022-05-12" @default.
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- W4254541473 date "2014-12-12" @default.
- W4254541473 modified "2023-10-03" @default.
- W4254541473 title "Two perils of binary categorization: Why the study of concepts can’t afford true/false testing" @default.
- W4254541473 doi "https://doi.org/10.7287/peerj.preprints.688v1" @default.
- W4254541473 hasPublicationYear "2014" @default.
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