Neural and computational mechanisms of selective attention in decision making

决策中选择性注意的神经和计算机制

• 批准号: 8547107
• 负责人: Yael Niv
• 金额: $ 34.98万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-18 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8547107
• 关键词: Affect; Area; Attention; Attention Deficit Disorder; Avocado; Basal Ganglia; Behavior; Behavioral; Brain; Categories; Choice Behavior; Clinical; Cognitive; Cognitive Science; Color; Complex; Computer Simulation; Computing Methodologies; Concept Formation; Corpus striatum structure; Data; Decision Making; Dimensions; Disease; Drug abuse; Environment; Face; Feedback; Fruit; Functional Magnetic Resonance Imaging; Future; Goals; Height; Housing; Human; Individual; Individual Differences; Intervention; Knowledge; Learning; Left; Light; Literature; Location; Marketing; Measures; Methods; Modeling; Outcome; Parietal; Pattern; Performance; Population; Process; Psychological reinforcement; Research; Rewards; Schizophrenia; Shapes; Side; Sorting - Cell Movement; Source; Speed; Staging; Stimulus; Testing; Time; Wisconsin; Work; base; cognitive function; experience; improved; neuroimaging; neuromechanism; relating to nervous system; research study; selective attention; tool

DESCRIPTION (provided by applicant): Neural and computational mechanisms of selective attention in experience-based decision making In order to make correct decisions, we must learn from our past experiences. Learning has long been conceptualized as the formation of associations between stimuli and outcomes. But how should we define these "stimuli" in real-world decision making environments that are complex and multidimensional? It would seem most optimal to learn about all available stimulus features (height, color, shape, etc.). However, in natural environments only few dimensions are relevant to performance of any given task. Attending to and learning about only those dimensions that are relevant to the task at hand (and ignoring all others) improves performance, speeding learning and simplifying generalization to future stimuli that are slightly different. How do we know what dimensions are relevant to a given task, and should be attended to and learned about? Considerable behavioral work in cognitive psychology has explored the dynamics of "attention learning"-how we learn what to attend to-within the context of categorization and concept formation. However, little is known about the neural basis of attention learning, and how attention interacts with implicit trial-and-error reinforcement learning processes. The goal of this project is to study the neural and computational substrates of attention learning in humans, and to understand how attention mechanisms interact with learning mechanisms in the brain. We propose to use a combi- nation of computational modeling, behavioral experiments and functional neuroimaging in order to 1) determine the neural substrates of attention learning in the human brain, 2) track learning-driven changes in attention to different dimensions of a stimulus directly, and 3) establish individual differences in attention for learning separately from attention for decision. The overarching neural hypothesis to be tested is two-fold: we hypothesize that neural mechanisms for reinforcement learning in the basal ganglia operate on an attentionally-filtered representation of the environment that is conveyed to the striatum by fronto-parietal cortical afferents. Moreover, we hypothesize that this attentional filter is dynamically adjusted according to the outcomes of ongoing decisions. Throughout, we will not assume that attention learning consists of one unitary process but rather investigate the possibility that individuals use different strategies to varying extents. In particular, building on our previous research and on findings in the categorization literature, we will focus on two computational strategies for attention learning-a serial hypothesis testing strategy, and a gradually focusing parallel attention strategy-that are differentially indicated in different individuals. Our results will significantly advance the basic scientific understanding of cognitive decision making processes, elucidating the neural mechanisms underlying a critical component of decision making. From a practical perspective, understanding the computational and neural underpinnings of individual differences in attention learning will potentially allow tailoring of learning tasks to different individuals. Moreover, the neural processes underlying attention learning are likely to be involved in clinical disorders such as schizophrenia, attention deficit disorder and drug abuse disorder. In the long term, the proposed research will potentially impact on the study and treatment of these disorders.

描述（由申请人提供）：基于经验的决策中选择性注意的神经和计算机制为了做出正确的决策，我们必须从过去的经验中学习。长期以来，学习一直被概念化为刺激和结果之间关联的形成。但是，我们应该如何定义这些“刺激”在现实世界的决策环境是复杂的和多维的？了解所有可用的刺激特征（高度、颜色、形状等）似乎是最佳选择。然而，在自然环境中，只有少数维度与任何给定任务的性能相关。只关注和学习那些与手头任务相关的维度（而忽略所有其他维度）可以提高表现，加快学习速度，简化对未来略有不同的刺激的概括。我们如何知道哪些维度与给定的任务相关，应该注意和了解？认知心理学中相当多的行为研究已经探索了“注意力学习”的动力学我们如何在分类和概念形成的背景下学习注意什么。然而，人们对注意力学习的神经基础以及注意力如何与内隐试错强化学习过程相互作用知之甚少。该项目的目标是研究人类注意力学习的神经和计算基底，并了解注意力机制如何与大脑中的学习机制相互作用。我们建议使用计算建模、行为实验和功能神经成像的结合，以便1）确定人脑中注意力学习的神经基质，2）直接跟踪学习驱动的注意力对刺激不同维度的变化，3）建立学习注意力与决策注意力的个体差异。要测试的总体神经假设是双重的：我们假设，在基底神经节的强化学习的神经机制上的注意力过滤表示的环境，传达到纹状体的额顶叶皮层传入。此外，我们假设，这种注意力过滤器是动态调整，根据正在进行的决策的结果。在整个过程中，我们不会假设注意力学习由一个单一的过程组成，而是研究个体使用不同策略的可能性， 程度不同特别是，建立在我们以前的研究和分类文献中的发现，我们将集中在两个计算策略的注意力学习-一个串行假设检验策略，并逐步集中平行注意力策略，在不同的个体差异表示。我们的研究结果将大大促进基本的 认知决策过程的科学理解，阐明决策的关键组成部分的神经机制。从实践的角度来看，理解注意力学习中个体差异的计算和神经基础将可能允许为不同的个体定制学习任务。而且 注意力学习的神经过程可能与临床疾病有关， 如精神分裂症、注意力缺陷障碍和药物滥用障碍。从长远来看，拟议的研究将对这些疾病的研究和治疗产生潜在影响。