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

Mark P. Holden, Nora S. Newcombe, Ilyse Resnick, Thomas F. Shipley

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)440-454
Number of pages15
JournalCognitive Science
Volume40
Issue number2
DOIs
StatePublished - Mar 1 2016

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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    Holden, M. P., Newcombe, N. S., Resnick, I., & Shipley, T. F. (2016). Seeing Like a Geologist: Bayesian Use of Expert Categories in Location Memory. Cognitive Science, 40(2), 440-454. https://doi.org/10.1111/cogs.12229