Holden Mark P, Newcombe Nora S, Resnick Ilyse, Shipley Thomas F
Department of Psychology, Temple University.
Department of Psychology, University of Western Ontario.
Cogn Sci. 2016 Mar;40(2):440-54. doi: 10.1111/cogs.12229. Epub 2015 May 5.
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.
对空间位置的记忆通常存在偏差，误差趋势朝着周边区域的中心。根据类别调整模型（CAM），这种偏差反映了位置的细粒度和类别表征的最优贝叶斯组合。然而，关于类别是否具有可塑性存在分歧。例如，类别能否基于专家级概念知识重新定义？此外，如果使用专家知识，它是否会主导其他信息源，还是会如贝叶斯框架所预测的那样被适应性地使用，以尽量减少总体误差？我们使用地质相关图像来解决这些问题。参与者是构造地质学、有机化学或英国文学方面的专家。我们的数据表明，基于专业知识的类别会影响位置记忆的估计——尤其是当这些类别比其他（“新手”）类别能更好地限制误差时。结合CAM对结果进行了讨论。
