Stochastic Models of Visual Decision Making and Visual Search

描述（由申请人提供）：要求继续支持一项富有成效的合作，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。我们的研究项目以计算、行为和神经生理学的约束为指导，通过使用预测行为和神经动力学的计算模型，将人类和猴子执行视觉跳跳任务的详细行为模式与猴子记录的神经元调制模式联系起来。我们提出了现有猴子行为和神经生理实验的新计算模型，以及新的人类实验的新计算模型，这些实验反映并显著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科学家将其融合为理解和解释视觉决策的时间过程的一般理论框架；其中包括我们之前提出的交互式竞赛模型和门控累加器模型。与大多数方法不同，(1)我们对人类和猴子的详细行为数据进行了定量测试，包括反应概率和正确和错误反应时间的分布，(2)我们基于神经生理学记录约束模型机制和模型参数。(3)我们对模型架构进行了定量测试，看它们对参与视觉决策的神经元（特别是决定眼睛运动时间和位置的FEF神经元）的记录动态的预测程度。目的1将开发和测试门控累加器模型与反命令和控制跳眼运动的替代模型。目标2将开发和测试门控累加器模型与视觉搜索中跳眼运动的速度-精度控制的替代模型。目标3将研究如何扩展广泛的随机累加器模型，包括门控累加器，从与每个响应相关的单个累加器到与每个响应相关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，抽象的计算模型，如随机证据积累模型，可以是一个刚刚好的理论水平，因为这些模型的最佳拟合参数可以很好地表征个体行为差异，并为理解大脑测量提供理论标记——我们的模型提供了刚刚好的理论水平。然而，在某种程度上，某些神经系统疾病在单个神经元和神经回路水平上具有生物物理基础，我们还需要了解这些抽象的计算模型如何映射到神经回路上——绘制这种映射也是我们提出的工作的核心。

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.