Abstract
Mental localization efforts tend to stress the where more than the what. We argue that the proper targets for localization are well-specified cognitive models. We make this case by relating an existing cognitive model of category learning to a learning circuit involving the hippocampus, perirhinal, and prefrontal cortices. Results from groups varying in function along this circuit (e.g., infants, amnesics, and older adults) are successfully simulated by reducing the model’s ability to form new clusters in response to surprising events, such as an error in supervised learning or an unfamiliar stimulus in unsupervised learning. Clusters in the model are akin to conjunctive codes that are rooted in an episodic experience (the surprising event) yet can develop to resemble abstract codes as they are updated by subsequent experiences. Thus, the model holds that the line separating episodic and semantic information can become blurred. Dissociations (categorization vs. recognition) are explained in terms of cluster recruitment demands.
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This work was supported by AFOSR Grant FA9550-04-1-0226, ARL Grant W911NF-07-2-0023, and NSF CAREER Grant 0349101 to B.C.L.
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Love, B.C., Gureckis, T.M. Models in search of a brain. Cognitive, Affective, & Behavioral Neuroscience 7, 90–108 (2007). https://doi.org/10.3758/CABN.7.2.90
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DOI: https://doi.org/10.3758/CABN.7.2.90
