Department of Psychology, New York University, New York, NY 10003;
Center for Neural Science, New York University, New York, NY 10003.
Proc Natl Acad Sci U S A. 2018 Oct 23;115(43):11090-11095. doi: 10.1073/pnas.1717720115. Epub 2018 Oct 8.
Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision-making. Here we show that, in a task in which uncertainty is relevant for performance, human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer's attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer's decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.
当考虑到不确定性时，感知决策会更好。不确定性不仅来自于感觉输入的特性，还来自于认知源，例如注意力的不同水平。然而，目前尚不清楚人类在进行感知决策时是否会适当地调整这种认知来源的不确定性。在这里，我们表明，在一个与表现相关的不确定性任务中，人类的分类和置信度决策会考虑与注意力相关的不确定性。我们在一项使用仅注意力提示的方向分类任务中，逐次操纵不确定性。该分类任务旨在区分是否依赖于注意力的决策规则。使用正式的模型比较来评估决策行为，我们发现，类别和置信度决策边界随着注意力的变化而呈近似贝叶斯的方式变化。这意味着观察者在每次试验中的注意力状态会以概率的方式影响决策计算。这种观察者的决策对注意力依赖的不确定性的响应性应该会提高在自然视觉中的感知决策，在自然视觉中，注意力在场景中是不均匀分布的。
