CRCNS Research Proposal: Coding and Propagation of Uncertainty Information During Perceptual Decisions

就像阿加西·克里斯蒂（Agathie Christie）侦探小说中的马普尔小姐（Miss Marple）一样，大脑经常面临着从嘈杂和模糊的线索中推断世界状态的任务。一个重要的问题是，大脑是否以近乎最佳的方式进行这种推理，如果是，又是如何进行的。本项目将人类和猴子的视觉决策实验与计算建模和最先进的猴子种群记录相结合来研究这个问题。该项目将为神经密码以及神经元与行为之间的关系带来新的见解。在一项任务中，观察者将一个短暂闪现的定向刺激分为两类。这些类是由固定的、重叠的概率分布定义的。两种常见的不确定性在这项任务中起作用。感官观察中的噪音会导致感官的不确定性，但即使这种噪音被去除，给定的观察结果也会与任何一类一致；这是一个模棱两可或类不确定性的例子。最优决策策略要求观察者在每次试验中跟踪感官不确定性，并将这些信息与两个类别的知识适当地结合起来。该项目的第一部分将确定人类和猴子在这项任务中采用的计算策略是否接近最优。第二部分是关于感官不确定性是否以及如何在猴子初级视觉皮层中进行一次又一次的编码，并随后用于猴子的决策。本研究将对感官不确定性表征的著名理论框架进行检验。在第三部分，感官不确定性信息的传播从视觉皮层到一个更高层次的决策区，前额叶皮层，将被检查。综上所述，该项目将构成皮层种群水平上的最佳推理的第一个综合测试。

Like Miss Marple in an Agathie Christie detective novel, the brain is often faced with the task of inferring a state of the world from noisy and ambiguous clues. An important question is whether, and if so how, the brain performs such inference in a near-optimal manner. This project combines visual decision-making experiments in humans and monkeys with computational modeling and state-of-the-art population recordings in monkey to investigate this question. The project will lead to new insights into the neural code and the relation between neurons and behavior.A task is used in which an observer classifies a briefly flashed oriented stimulus into one of two classes. The classes are defined by fixed, overlapping probability distributions over orientation. Two common forms of uncertainty play a role in this task. Noise in the sensory observation causes sensory uncertainty, but even if this noise were taken away, a given observation would be consistent with either class; this is an example of ambiguity or class uncertainty. The optimal decision strategy requires the observer to keep track of sensory uncertainty on every trial and to appropriately combine this information with knowledge of the two classes.The first part of this project will determine whether the computational strategy taken by humans and monkeys during this task is near-optimal. The second part is concerned with whether and how sensory uncertainty is encoded on a trial-by-trial basis in monkey primary visual cortex, and subsequently used in the monkey's decision. This study will put to the test well-known theoretical frameworks for the representation of sensory uncertainty. In the third part, the propagation of sensory uncertainty information from visual cortex to a higher-level decision area, prefrontal cortex, will be examined. Taken together, this project will constitute the first comprehensive test of optimal inference at the level of cortical populations.