Stochastic Models of Visual Decision Making and Visual Search

视觉决策和视觉搜索的随机模型

基本信息

批准号：
8817898
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
项目类别：
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/8817898
关键词：
Address Architecture Behavior Behavioral 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 Probability Procedures Process Psychologist Race Reaction Time Research Rest Saccades Signal Transduction Speed Structure System Testing Time Training Translational Research Vision Visual Visual Perception Visual attention Work abstracting base behavior prediction cognitive control cognitive testing design disability frontal eye fields human data mathematical model neural circuit neurophysiology programs public health relevance relating to nervous system research study response scale up superior colliculus Corpora quadrigemina theories visual cognition visual search

项目摘要

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.