Seeing Like a Geologist: Bayesian Use of Expert Categories in Location Memory. (5th May 2015)

• Record Type: Journal Article
• Authors: Holden, Mark P.; Newcombe, Nora S.; Resnick, Ilyse; Shipley, Thomas F.
• Is Part Of: Cognitive science. Volume 40:Number 2(2016:Mar.)
• Page Start: 440
• Page End: 454
• Publication Date: 2015-05-05
• Subjects: Location memory -- Expertise -- Spatial cognition -- Bayesian models -- Categorization; Cognition -- Periodicals; Psycholinguistics -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1111/cogs.12229
• Languages: English
• ISSNs: 0364-0213

Abstract

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.