Imperfect Bayesian inference in visual perception.

ABSTRACT: Optimal Bayesian models have been highly successful in describing human performance on perceptual decision-making tasks, such as cue combination and visual search. However, recent studies have argued that these models are often overly flexible and therefore lack explanatory power. Moreover, there are indications that neural computation is inherently imprecise, which makes it implausible that humans would perform optimally on any non-trivial task. Here, we reconsider human performance on a visual-search task by using an approach that constrains model flexibility and tests for computational imperfections. Subjects performed a target detection task in which targets and distractors were tilted ellipses with orientations drawn from Gaussian distributions with different means. We varied the amou

SUBMITTER: Stengard E 

PROVIDER: S-EPMC6472731 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature