Neural and computational mechanisms of selective attention in decision making

描述(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吸取教训。长期以来，学习一直被概念化为在刺激和结果之间形成联系。但是，在复杂和多维的现实世界决策环境中，我们应该如何定义这些“刺激”呢？了解所有可用的刺激特征(身高、颜色、形状等)似乎是最理想的。然而，在自然环境中，只有几个维度与任何给定任务的绩效相关。只关注和学习那些与手头任务相关的维度(忽略所有其他维度)可以提高成绩，加快学习速度，并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，以及应该关注和了解这些维度？认知心理学中的相当多的行为研究已经探索了“注意学习”的动态--我们如何学习注意什么--在分类和概念形成的背景下。然而，关于注意学习的神经基础，以及注意如何与内隐反复试验强化学习过程相互作用，人们知之甚少。这个项目的目标是研究人类注意力学习的神经和计算基础，并了解注意力机制如何与大脑中的学习机制相互作用。我们建议使用计算模型、行为实验和功能神经成像的组合来1)确定人脑中注意学习的神经基础，2)直接跟踪学习驱动的注意对不同刺激维度的变化，以及3)建立个体对学习的注意的差异，而不是为了决策而注意。要检验的主要神经假说有两个：我们假设，基底神经节强化学习的神经机制作用于注意过滤的环境表征，并通过额顶皮质传入传递到纹状体。此外，我们假设这种注意过滤器是根据正在进行的决定的结果动态调整的。自始至终，我们不会假设注意力学习由一个单一的过程组成，而是调查个体使用不同策略的可能性 程度各不相同。特别是，基于我们之前的研究和分类文献中的发现，我们将重点关注两种注意力学习的计算策略--一种连续假设检验策略和一种逐渐聚焦的并行注意策略--这两种策略在不同的个体中表现不同。我们的结果将显著推进基本的 对认知决策过程的科学理解，阐明决策的关键组成部分背后的神经机制。从实践的角度来看，理解注意学习中个体差异的计算和神经基础将潜在地允许为不同的个体定制学习任务。此外， 注意学习的神经过程很可能与临床疾病有关，如 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，这项拟议的研究可能会对这些疾病的研究和治疗产生影响。

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.