Stochastic Models of Visual Decision Making and Visual Search

项目总结 需要支持以推进创新、富有成效的协作，旨在将思维、大脑和 使用性能、神经生理学和电生理学测量的猴子和 人类执行视觉搜索和视觉决策任务。总体目标是派生 使用计算模型从猴子的尖峰训练到人类行为的联系 心理状态与大脑状态的数学联系，特别是神经元，并解释神经是如何 计算产生行为。我们的门控累加器模型(GAM)假设随机累积 证据达到了可供选择的反应门槛。模型评估涉及对备选方案进行量化测试 基于行为测量、响应概率和正确分布的预测的模型架构 和错误响应时间，以及神经测量，以及这些如何随设定的大小和目标干扰物而变化 以前从进行视觉搜索的猴子那里收集的数据中的区分性。而我们之前的 资助的研究旨在了解GAM中证据积累的架构以及 我们新提出的FEF运动相关神经元动态观察模型累加器 研究的目的是通过计算了解推动这种积累的证据的性质及其 与FEF内视觉反应神经元的测量动力学的关系。目标1比较以下各项的质量 偏侧化的脑电信号和视觉反应神经元的神经放电的显著证据 猴子进行视觉搜索，作为随机累加器网络的输入证据进行预测 行为。目标2通过确定神经累积器框架是否 FEF中的运动神经元动力学实际上是坡道或台阶。AIM 3评估可选架构 证据驱动累积联合预测观察行为的视觉注意模型 以及测量到的视觉反应神经元的动力学。AIM 4将VAM扩展到更复杂的视觉任务 包括过滤和选择。其结果将是对视觉过程的更广泛和更深入的理解 来选择目标并控制眼球运动。像VAM和GAM这样的计算模型可能只是 “正确”的抽象层次。它们以解释神经活动的方式捕捉到了计算的基本细节 以及单个参与者的行为，无论是猴子还是人类。这些模型可以用来理解 正常行为以及疾病、残疾和疾病；最适合的参数可以表征 行为的个体差异，并为大脑测量提供标记物。这些模型还可以告知 在单个神经元和神经回路水平上有生物物理基础的神经疾病， 提供对神经元和电路计算的内容以及它们是如何计算的洞察力。

PROJECT SUMMARY Support is requested to advance an innovative, productive collaboration aimed at linking mind, brain, and behavior using performance, neurophysiological, and electrophysiological measures from monkeys and humans performing visual search and visual decision making tasks. The general goal is to derive the connections from spike trains in monkeys to behavior in humans using computational models that specify mental states mathematically, link them to brain states in particular neurons, and explain how the neural computations produces behavior. Our Gated Accumulator Model (GAM) assumes a stochastic accumulation of evidence to threshold for alternative responses. Model assessment involves quantitatively testing alternative model architectures on predictions of behavioral measures, response probabilities and distributions of correct and error response times, as well as neural measures and how these change with set size and target-distractor discriminability in previously collected data from monkeys performing visual search. While our previously funded research aimed to understand the architecture of evidence accumulation in GAM and the relationship of model accumulators to the observed dynamics of movement-related neurons in FEF, our newly proposed research aims to understand computationally the nature of the evidence that drives that accumulation and its relationship to the measured dynamics of visually-responsive neurons in FEF. Aim 1 compares the quality of salience evidence in lateralized EEG signals and neural discharges from visually-responsive neurons in monkeys performing visual search as input evidence to a network of stochastic accumulators to predict behavior. Aim 2 addresses a major challenge to the neural accumulator framework by determining whether movement neuron dynamics in FEF actually ramp or step. Aim 3 evaluates alternative architectures for an abstract Visual Attention Model (VAM) of the evidence driving accumulation to jointly predict observed behavior and the measured dynamics of visually-responsive neurons. Aim 4 extends VAM to more complex visual tasks involving filtering and selection. The result will be a broader and deeper understanding of the visual processes that select targets and control eye movements. Computational models like VAM and GAM may be at the “just right” level of abstraction. They capture essential details of the computation in ways that explain neural activity and behavior in single participants, whether monkey or human. These models can be used to understand normal behavior as well as illness, disability, and disease; the best-fitting parameters can characterize individual differences in behavior and provide markers for brain measures. These models can also inform neurological conditions that have a biophysical basis at the level of individual neurons and neural circuits, offering insight into what neurons and circuits compute and how they do it.