Faculty of Psychology, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
Faculty of Psychology, School of Health Sciences, University of Iceland, Reykjavik, Iceland; Visual Computation Lab, Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands; Cognitive Research Lab, Russian Academy of National Economy and Public Administration, Moscow, Russia.
Cognition. 2021 Dec;217:104903. doi: 10.1016/j.cognition.2021.104903. Epub 2021 Sep 14.
Recent accounts of perception and cognition propose that the brain represents information probabilistically. While this assumption is common, empirical support for such probabilistic representations in perception has recently been criticized. Here, we evaluate these criticisms and present an account based on a recently developed psychophysical methodology, Feature Distribution Learning (FDL), which provides promising evidence for probabilistic representations by avoiding these criticisms. The method uses priming and role-reversal effects in visual search. Observers' search times reveal the structure of perceptual representations, in which the probability distribution of distractor features is encoded. We explain how FDL results provide evidence for a stronger notion of representation that relies on structural correspondence between stimulus uncertainty and perceptual representations, rather than a mere co-variation between the two. Moreover, such an account allows us to demonstrate what kind of empirical evidence is needed to support probabilistic representations as posited in current probabilistic Bayesian theories of perception.
最近关于感知和认知的研究表明，大脑以概率的形式来表示信息。虽然这种假设很常见，但最近有人批评了感知中存在这种概率表示的实证依据。在这里，我们评估了这些批评，并提出了一个基于最近开发的心理物理方法——特征分布学习（Feature Distribution Learning，FDL）的解释，该方法通过避免这些批评为概率表示提供了有希望的证据。该方法使用了视觉搜索中的启动和角色反转效应。观察者的搜索时间揭示了感知表示的结构，其中，分心物特征的概率分布被编码。我们解释了 FDL 的结果如何为一种更强的表示概念提供证据，这种概念依赖于刺激不确定性和感知表示之间的结构对应关系，而不仅仅是两者之间的简单共变关系。此外，这样的解释使我们能够展示需要什么样的经验证据来支持当前感知的概率贝叶斯理论所提出的概率表示。
