Contextual modulation of visual decision-making across the visual hierarchy

跨视觉层次结构的视觉决策的上下文调制

• 批准号: 10658176
• 负责人: PAMELA REINAGEL
• 金额: $ 71.1万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658176
• 关键词: Affect; Animal Model; Animals; Area; Arousal; Attention; BRAIN initiative; Behavioral; Behavioral trial; Brain; Collaborations; Complex; Computer Models; Cues; Data; Data Set; Decision Making; Dependence; Diffusion; Discipline; Discrimination; Dissection; Dorsal; Electrophysiology (science); Environment; Foundations; Future; Goals; Image; Individual; Interdisciplinary Study; Knowledge; Learning; Link; Location; Masks; Measures; Methods; Modeling; Motion; Motivation; Movement; Mus; Pathway interactions; Persons; Preparation; Property; Protocols documentation; Rattus; Recording of previous events; Research; Research Personnel; Role; Sampling; Sensory; Shapes; Signal Transduction; Source; Stimulus; Stream; Testing; Time; Time Series Analysis; Training; Transgenic Organisms; Variant; Viral Vector; Vision; Visual; Visual System; Water; Work; area striata; density; design; experimental study; extrastriate visual cortex; food restriction; improved; innovation; insight; luminance; millisecond; neural; neural circuit; neural correlate; neuromechanism; novel; optogenetics; sound; statistics; superior colliculus Corpora quadrigemina

Project Summary/Abstract In sensory decision-making, choices are influenced by non-sensory factors such as motivation, attention, and recent trial history. We seek to incorporate these influences into a drift diffusion model (DDM), by modeling non-sensory variables as deterministic modulators of the starting point or drift rate of sensory evidence accumulation. However, decision-making models are subject to confounds due to the non-stationarity and correlations in long-term behavioral data. More work is needed to quantify these properties and develop new statistical approaches to overcome them. Existing datasets have proven inadequate, so new datasets must be collected. To gain insight into the neural mechanisms of contextual modulation in decision-making, our goal is to compare non-sensory influences on sensory decision- making across levels of the visual hierarchy and between parallel visual streams. We have evidence there are differences, which could be leveraged to identify where in the brain non-visual information enters into visual decision-making. To establish feasibility for an R01, we need to establish a new collaboration with a statistician, develop methods for training single animals in multiple visual tasks; show that visual tasks differ in their sensitivity to non- sensory modulation; and show that we can obtain the amount of trial data required to fit and compare models within subject. We propose to train individual animals in grating orientation, random-dot motion, object identity, spatial location of luminance or contrast, as well as piloting two new tasks (stochastic drifting grating, spatial location of motion). These visual features are thought to be extracted in different brain areas: in primary visual cortex (V1); in different higher visual areas (HVAs) in ventral or dorsal streams; or in a V1-independent collicular pathway. We will collect long-term data on the interleaved tasks using automated high-throughput in-cage testing, and validate that these data meet statistical requirements for model fitting. Rats are ideal for this study because individual rats can learn multiple visual tasks, and we are able to obtain 105-106 behavioral trials per rat without water or food restriction. Rats are also suitable for viral vector targeting strategies and high-density electrophysiology with optogenetics in freely behaving animals. At the end of this 2-year R34 project, the lab will have assembled a new multi-disciplinary research team poised for dissection of underlying circuit mechanisms, with validated visual tasks, training protocols, statistical approaches, and model-fitting methods. These preparations will support a BRAIN Initiative: Targeted BCP R01 application aimed at dissecting neural representations and circuit mechanisms of contextual modulation of choice.

项目总结/摘要 在感官决策中，选择受到非感官因素的影响，例如 动机注意力和最近的审判记录我们试图将这些影响纳入一个 漂移扩散模型（DDM），通过将非感觉变量建模为 感官证据积累的起点或漂移率。然而，决策 由于模型的非平稳性和长期相关性， 行为数据需要做更多的工作来量化这些属性并开发新的统计方法。 方法来克服它们。现有的数据集已被证明是不够的，因此新的数据集 必须收集。为了深入了解上下文调制的神经机制， 我们的目标是比较非感官因素对感官决策的影响， 在视觉层次的各个层次之间以及在平行的视觉流之间进行。我们 有证据表明存在差异，可以利用这些差异来确定大脑中 非视觉信息进入视觉决策。为了确定R 01的可行性，我们 需要与一名统计员建立新的合作关系，制定培训方法 单个动物在多个视觉任务中;显示视觉任务对非视觉任务的敏感性不同， 感觉调制;并表明，我们可以获得所需的试验数据量， 比较主题内的模型。我们建议训练个体动物在光栅方向， 随机点运动、对象标识、亮度或对比度的空间位置以及引导 两个新任务（随机漂移光栅、运动的空间定位）。这些视觉特征是 被认为是在不同的大脑区域提取：在初级视觉皮层（V1）;在不同的高级 腹侧或背侧流中的视觉区（HVA）;或在V1独立的丘通路中。我们 将使用自动化高通量笼内 测试，并验证这些数据满足模型拟合的统计要求。大鼠 这是这项研究的理想选择，因为单个老鼠可以学习多种视觉任务，我们能够 每只大鼠获得105-106次行为试验，无水或食物限制。老鼠也适合 病毒载体靶向策略和高密度电生理与光遗传学在自由 行为动物在这个为期两年的R34项目结束时，实验室将组装一个新的 一个多学科的研究小组准备解剖潜在的电路机制， 经过验证的视觉任务，培训协议，统计方法和模型拟合方法。 这些准备工作将支持BRAIN倡议：有针对性的BCP R 01应用， 剖析选择的语境调制的神经表征和回路机制。