Seeing Like a Geologist: Bayesian Use of Expert Categories in Location Memory

Cognitive Science 40 (2):440-454 (2016)
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Abstract

Memory for spatial location is typically biased, with errors trending toward the center of a surrounding region. According to the category adjustment model, this bias reflects the optimal, Bayesian combination of fine-grained and categorical representations of a location. However, there is disagreement about whether categories are malleable. For instance, can categories be redefined based on expert-level conceptual knowledge? Furthermore, if expert knowledge is used, does it dominate other information sources, or is it used adaptively so as to minimize overall error, as predicted by a Bayesian framework? We address these questions using images of geological interest. The participants were experts in structural geology, organic chemistry, or English literature. Our data indicate that expertise-based categories influence estimates of location memory—particularly when these categories better constrain errors than alternative categories. Results are discussed with respect to the CAM

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