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Seeing Like a Geologist: Bayesian Use of Expert Categories in Location Memory
Author(s) -
Holden Mark P.,
Newcombe Nora S.,
Resnick Ilyse,
Shipley Thomas F.
Publication year - 2016
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12229
Subject(s) - categorical variable , geologist , bayesian probability , computer science , artificial intelligence , bayesian inference , bayesian statistics , natural language processing , machine learning , cognitive psychology , psychology , geography , archaeology
Memory for spatial location is typically biased, with errors trending toward the center of a surrounding region. According to the category adjustment model ( CAM ), 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 (“novice”) categories. Results are discussed with respect to the CAM .