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

CRCNS 研究提案：感知决策过程中不确定性信息的编码和传播

• 批准号: 1132009
• 负责人: Andreas Tolias
• 金额: $ 86.23万
• 依托单位: Baylor College of Medicine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1132009&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Coding; Propagation

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.

就像阿加蒂·克里斯蒂侦探小说中的马普尔小姐一样，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，如果是的话，如何进行。该项目将人类和猴子的视觉决策实验与计算模型和最先进的猴子群体记录相结合来研究这个问题。该项目将对神经代码以及神经元与行为之间的关系产生新的见解。在一项任务中，观察者将短暂闪烁的定向刺激分为两类之一。这些类别是由方向上固定的、重叠的概率分布定义的。两种常见形式的不确定性在这项任务中发挥着作用。感官观察中的噪音会导致感官不确定性，但即使消除了这种噪音，给定的观察结果也会与任一类别一致；这是模糊性或阶级不确定性的一个例子。最佳决策策略要求观察者在每次试验中跟踪感觉不确定性，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取的计算策略是否接近最佳。第二部分涉及猴子初级视觉皮层中是否以及如何在反复试验的基础上对感觉不确定性进行编码，并随后用于猴子的决策。这项研究将测试用于表示感觉不确定性的著名理论框架。在第三部分中，将研究感觉不确定性信息从视觉皮层到更高级别的决策区域——前额叶皮层的传播。总而言之，该项目将构成皮层群体水平上最优推理的首次全面测试。