Stochastic Models of Visual Decision Making and Visual Search

视觉决策和视觉搜索的随机模型

• 批准号: 9187469
• 负责人: Gordon Dennis Logan
• 金额: $ 27.48万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9187469
• 关键词: Address; Architecture; Behavior; Behavioral; Biophysics; Brain; Collaborations; Complex; Computer Simulation; Data; Decision Making; Diffusion; Disease; Eye; Eye Movements; Fire - disasters; Foundations; Goals; Hand; Human; Individual; Individual Differences; Injury; Intervention; Link; Literature; Manuals; Maps; Measures; Modeling; Monkeys; Movement; Nature; Neurologic; Neurons; Pattern; Pharmacology; Probability; Procedures; Process; Psychologist; Race; Reaction Time; Research; Rest; Saccades; Signal Transduction; Speed; Structure; System; Testing; Time; Training; Translational Research; Vision; Vision Disorders; Visual; Visual Perception; Visual attention; Visual impairment; Work; base; behavior prediction; cognitive control; cognitive testing; design; disability; experimental study; frontal eye fields; human data; mathematical model; neural circuit; neurophysiology; predicting response; predictive modeling; programs; public health relevance; relating to nervous system; response; scale up; superior colliculus Corpora quadrigemina; theories; visual cognition; visual control; visual search

DESCRIPTION (provided by applicant): Support is requested to continue a productive collaboration aimed to develop, test, and extend computational models of eye movement control in visual decision making and visual search. Our research program is guided by converging constraints from computational, behavioral, and neurophysiological perspectives that link detailed patterns of behavior in humans and monkeys performing visual saccade tasks with patterns of modulation in neurons recorded in monkeys through the use of computational models that predict behavioral and neural dynamics. We propose new computational modeling of existing monkey behavioral and neurophysiological experiments and new computational modeling of new human experiments that mirror and significantly extend experiments previously conducted with monkeys. Our theoretical foundation is a class of stochastic accumulation of evidence models that mathematical psychologists and systems neuroscientists have converged upon as a general theoretical framework to understand and explain the time course of visual decision making; these include an interactive race model and a gated accumulator model we proposed previously. Unlike most approaches, (1) we quantitatively test alternative model architectures (including race, diffusion, competitive, gated accumulators) on detailed behavioral data in both humans and monkeys, including response probabilities and distributions of correct and error response times for saccades, (2) we constrain model mechanisms and model parameters based on neurophysiological recordings, specifically neurons in frontal eye field (FEF) hypothesized to represent the evolving time-course of task-relevant visual evidence, (3) we quantitatively test model architectures on how well they predict the recorded dynamics of neurons involved in make a visual decision, specifically neurons in FEF that determine when and where the eyes move. Aim 1 will develop and test the gated accumulator model against alternative models of countermanding and control of saccadic eye movements. Aim 2 will develop and test the gated accumulator model against alternative models of speed-accuracy control of saccadic eye movements in visual search. Aim 3 will investigate how to scale the broad class of stochastic accumulator models, including gated accumulator, from a single accumulator associated with each response to ensembles of thousands of accumulator neurons associated with each response. To understand normal behavior as well as illness, disability, and disease, abstract computational models, like stochastic accumulation of evidence models, can be a just right theoretical level in that best-fitting parameters of these models can characterize well individual differences in behavior and provide theoretical markers for understanding brain measures - our models provide that just right theoretical level. Yet to the extent that certain neurological conditions have a biophysical basis at the level of individual neurons and neural circuits, we also need to understand how these abstract computational models map onto neural circuits - making this mapping is also core to our proposed work.

描述(由申请人提供)：要求继续开展富有成效的合作，旨在开发、测试和扩展视觉决策和视觉搜索中眼动控制的计算模型。我们的研究计划是以计算、行为和神经生理学角度的融合约束为指导的，通过使用预测行为和神经动力学的计算模型，将执行视觉眼跳任务的人类和猴子的详细行为模式与猴子记录的神经元的调制模式联系起来。我们建议对现有的猴子行为和神经生理实验进行新的计算建模，并对新的人类实验进行新的计算建模，这些实验反映并显着扩展了以前在猴子身上进行的实验。我们的理论基础是一类随机证据积累模型，数学心理学家和系统神经学家已经将其作为理解和解释视觉决策时间过程的一般理论框架；其中包括我们先前提出的交互竞赛模型和门控累加器模型。与大多数方法不同，(1)我们基于详细的行为数据(包括眼跳的正确和错误响应时间的响应概率和分布)对替代模型架构(包括种族、扩散、竞争和门控累加器)进行定量测试，(2)我们基于神经生理学记录限制模型机制和模型参数，尤其是额眼场(FEF)中的神经元，假设其代表与任务相关的视觉证据的演变时间进程，(3)我们定量测试模型架构对参与视觉决策的神经元的记录动态的预测能力，特别是FEF中决定眼睛移动时间和位置的神经元。目标1将开发和测试门控累积器模型，以对抗和控制眼球跳动的其他模型。目标2将开发和测试门控累加器模型，并与视觉搜索中眼跳运动的速度-精度控制的替代模型进行比较。目标3将研究如何将包括门控累加器在内的大类随机累加器模型从与每个反应相关联的单个累加器扩展到与每个反应相关联的数千个累加器神经元的集合。为了理解正常行为以及疾病、残疾和疾病，抽象计算模型，如随机证据积累模型，可能是一个恰到好处的理论水平，因为这些模型的最佳拟合参数可以很好地表征行为的个体差异，并为理解大脑测量提供理论标记--我们的模型提供了这个恰到好处的理论水平。然而，就某些神经疾病在单个神经元和神经回路层面上的生物物理基础而言，我们还需要了解这些抽象计算模型是如何映射到神经回路上的--这种映射也是我们拟议工作的核心。