Neural and computational mechanisms of selective attention in decision making

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，学习被定义为刺激物和结果之间的关联。但是，在现实世界的复杂和多维的决策环境中，我们应该如何定义这些“刺激”呢？了解所有可用的刺激特征（高度、颜色、形状等）似乎是最理想的。然而，在自然环境中，只有少数几个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，并且应该关注和学习？认知心理学中相当多的行为研究在分类和概念形成的背景下探索了“注意学习”的动态——我们如何学习注意什么。然而，人们对注意学习的神经基础以及注意如何与内隐试错强化学习过程相互作用知之甚少。本项目的目标是研究人类注意学习的神经和计算基础，并了解注意机制如何与大脑中的学习机制相互作用。我们建议将计算模型、行为实验和功能神经成像相结合，以确定人脑中注意学习的神经基础，2)直接跟踪学习驱动的对刺激不同维度的注意变化，以及3)建立学习注意与决策注意的个体差异。要测试的主要神经假设有两个方面：我们假设基底节区强化学习的神经机制是通过对环境的注意过滤表征来运作的，该表征通过额顶叶皮层传入传递给纹状体。此外，我们假设这种注意力过滤器是根据正在进行的决策的结果动态调整的。在整个过程中，我们不会假设注意学习是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

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.