Higher-Order Visual Decision Making Networks and Mechanisms

高阶视觉决策网络和机制

• 批准号: 9187472
• 负责人: ANDREW S KAYSER
• 金额: $ 39.63万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9187472
• 关键词: Address; Anterior; Area; Attention; Behavior; Behavioral; Biological Neural Networks; Brain; Brain region; Categories; Decision Making; Detection; Disease; Electroencephalography; Esthesia; Functional Magnetic Resonance Imaging; Future; Goals; Homologous Gene; Human; Impairment; Insula of Reil; Lateral; Left; Link; Macaca; Medial; Mediating; Methods; Modeling; Monitor; Motion; Nature; Neurons; Nucleus Accumbens; Outcome; Output; Parietal; Parietal Lobe; Patients; Perception; Prefrontal Cortex; Primates; Process; Psychophysics; Sensory; Signal Transduction; Source; Stimulus; Structure; Thalamic structure; Time; Translating; Trauma; Uncertainty; Variant; Vision; Visual; Visual Perception; Work; base; cognitive function; cognitive neuroscience; frontal eye fields; frontal lobe; imaging study; inferior frontal sulcus; intraparietal sulcus; lateral intraparietal area; mathematical model; neural circuit; neuroimaging; neuromechanism; public health relevance; remediation; response; stimulus processing; success; visual information; visual processing; visual stimulus

DESCRIPTION (provided by applicant): Defining the structure of a canonical decision making circuit, from its inputs to its outputs, is one of the principal goals of cognitive neuroscience. T this end, visual perceptual decision making is a fundamental cognitive function in which visual information provides the basis for choosing an appropriate response. In the simplest case, the link between sensory evidence and a behavioral choice is binary: an oncoming car suddenly veers into one's lane and a quick decision must be made - to swerve left or right. However, when vision is compromised, whether through disease or trauma, these decision mechanisms must adapt to degraded input, impaired associative visual processing, or both. As a consequence, visual misperceptions occur more frequently, and the process of translating sensation to action becomes increasingly susceptible to errors of identification and categorization that must be recognized. Understanding the neural mechanisms by which visual perception interacts with higher-order categorization and error detection is thus essential to understanding abnormalities in patients. In this proposal, we build upon previous work studying perceptual decision making in primates and humans to evaluate the process by which humans make higher-order visual decisions. One hypothesis is that both lower-order (i.e. perceptual) and higher-order (e.g. categorization) decisions may differ in the source of their inputs but are mediated by the same decision making network. An alternative hypothesis argues that categorical uncertainty, whether related to the separation of object classes or to the separation of correct from erroneous responses, represents a more abstract feature related to confidence and context that engages different decision processes. Under this hypothesis, perceptual and categorical uncertainty should activate different circuits. By using a combination of behavioral psychophysics, mathematical models, functional MRI, and EEG in a well-validated visual paradigm, here we will attempt to define the brain networks and mechanisms that allow humans to make visual categorization decisions and to detect errors.

描述（由申请人提供）：从输入到输出定义规范决策电路的结构是认知神经科学的主要目标之一。为此，视知觉决策是一种基本的认知功能，其中视觉信息为选择适当的反应提供了基础。在最简单的情况下，感官证据和行为选择之间的联系是二元的：一辆迎面而来的汽车突然转向自己的车道，必须迅速做出决定-向左或向右转弯。然而，当视力受到损害时，无论是通过疾病还是创伤，这些决策机制必须适应退化的输入，受损的联想视觉处理，或两者兼而有之。因此，视觉错觉更频繁地发生，并且将感觉转化为行动的过程变得越来越容易受到识别和分类错误的影响，必须认识到这一点。因此，理解视知觉与高阶分类和错误检测相互作用的神经机制对于理解患者的异常是至关重要的。在这个建议中，我们建立在以前的工作研究灵长类动物和人类的知觉决策，以评估人类作出高阶视觉决策的过程。一个假设是，低阶（即感知）和高阶（例如分类）决策可能在其输入的来源上不同，但由相同的决策网络介导。一个备择假设认为，分类的不确定性，无论是与对象类别的分离或正确与错误的反应的分离，代表了一个更抽象的特征，涉及不同的决策过程的信心和背景。在这个假设下，感知和分类的不确定性应该激活不同的回路。通过使用行为心理物理学的组合，数学模型，功能性MRI，并在一个良好的验证视觉范式脑电图，在这里，我们将试图定义的大脑网络和机制，使人类作出视觉分类决策和检测错误。