Donders Institute for Brain, Cognition & Behavior, Radboud University, 6525 EN Nijmegen, The Netherlands.
Donders Institute for Brain, Cognition & Behavior, Radboud University, 6525 EN Nijmegen, The Netherlands
J Neurosci. 2019 Oct 9;39(41):8164-8176. doi: 10.1523/JNEUROSCI.3212-18.2019. Epub 2019 Sep 3.
How does the brain represent the reliability of its sensory evidence? Here, we test whether sensory uncertainty is encoded in cortical population activity as the width of a probability distribution, a hypothesis that lies at the heart of Bayesian models of neural coding. We probe the neural representation of uncertainty by capitalizing on a well-known behavioral bias called serial dependence. Human observers of either sex reported the orientation of stimuli presented in sequence, while activity in visual cortex was measured with fMRI. We decoded probability distributions from population-level activity and found that serial dependence effects in behavior are consistent with a statistically advantageous sensory integration strategy, in which uncertain sensory information is given less weight. More fundamentally, our results suggest that probability distributions decoded from human visual cortex reflect the sensory uncertainty that observers rely on in their decisions, providing critical evidence for Bayesian theories of perception. Virtually any decision that people make is based on uncertain and incomplete information. Although uncertainty plays a major role in decision-making, we have but a nascent understanding of its neural basis. Here, we probe the neural code of uncertainty by capitalizing on a well-known perceptual illusion. We developed a computational model to explain the illusion, and tested it in behavioral and neuroimaging experiments. This revealed that the illusion is not a mistake of perception, but rather reflects a rational decision under uncertainty. No less important, we discovered that the uncertainty that people use in this decision is represented in brain activity as the width of a probability distribution, providing critical evidence for current Bayesian theories of decision-making.
大脑如何表示其感觉证据的可靠性？在这里，我们测试感觉不确定性是否作为概率分布的宽度编码在皮层群体活动中，这一假设是神经编码的贝叶斯模型的核心。我们通过利用一种称为序列依赖的已知行为偏差来探测不确定性的神经表示。无论性别如何，人类观察者都会报告按顺序呈现的刺激的方向，同时使用 fMRI 测量视觉皮层的活动。我们从群体水平的活动中解码概率分布，并发现行为中的序列依赖效应与一种统计学上有利的感觉整合策略一致，在这种策略中，不确定的感觉信息的权重较小。更根本的是，我们的结果表明，从人类视觉皮层解码的概率分布反映了观察者在决策中依赖的感觉不确定性，为感知的贝叶斯理论提供了关键证据。人们做出的几乎任何决策都是基于不确定和不完整的信息。尽管不确定性在决策中起着重要作用，但我们对其神经基础的理解还很初步。在这里，我们通过利用一种已知的感知错觉来探测不确定性的神经代码。我们开发了一个计算模型来解释这种错觉，并在行为和神经影像学实验中对其进行了测试。这表明，这种错觉不是感知的错误，而是反映了不确定情况下的理性决策。同样重要的是，我们发现人们在这个决策中使用的不确定性在大脑活动中表现为概率分布的宽度，为当前的贝叶斯决策理论提供了关键证据。
