The mutual influence of attention and learning during knowledge acquisition

描述（由申请人提供）：有效的学习包括提取信息的关键模式，这些模式捕捉了我们经验的本质，并利用这些信息构建有用的知识，从而在新情况下实现预测行为。人类有多个与不同大脑区域相关的学习系统，但这些学习系统如何相互作用还不是很清楚。此外，现有理论在很大程度上忽略了学习过程中经验和目标对信息采样行为的指导作用。本研究将采用新颖的理论视角，结合功能磁共振成像（fMRI）和眼动追踪来研究类别学习过程中注意和学习的机制及其相互作用。关键的假设是，在学习过程中，个人必须选择要采样的信息，这将受到个人知识和当前目标的指导。将开发一类计算模型，将类别学习重塑为一个动态决策过程，在这个过程中，注意力作为信息处理出现，受学习者的目标、能力限制和当前知识的指导。这些模型通过预测和评估未来的预期利润来计划行动过程（例如，眼球运动）。实验1将检验这些模型的关键预测，即类别学习过程中的顺序抽样行为是由注意和知识成分的相互作用介导的。将这些模型拟合到完美和不完美学习者的眼动和分类行为中，将表征促进最佳类别学习的信息采样和学习过程的性质。在实验2和3中，模型组件将与涉及类别学习的特定神经生物学学习系统联系起来。实验2将开发和验证一种新的方法，通过使用功能磁共振成像数据的多变量大脑模式来连接形式模型和学习大脑

DESCRIPTION (provided by applicant): Effective learning involves extracting key patterns of information that capture the essence of our experiences and using this information to build useful knowledge that enables predictive behavior in novel situations. Humans have multiple learning systems associated with different brain regions, yet how these learning systems interact is not well understood. Also, existing theories largely ignore how information sampling behaviors are guided by experience and goals during learning. The research presented in this proposal will use a novel theoretical perspective in combination with functional magnetic resonance imaging (fMRI) and eye tracking to investigate the mechanisms of attention and learning and their interactions during category learning. The key hypothesis is that during learning, individuals must choose what information to sample, which will be guided by the person's knowledge and current goals. A class of computational models will be developed that recasts category learning as a dynamic decision process in which attention emerges as information processing guided by the learner's goals, capacity limitations, and current knowledge. These models plan a course of action (e.g., eye movements) by looking ahead and evaluating future actions for expected profit. Experiment 1 will test the key prediction of these models that sequential sampling behavior during category learning is mediated by the interaction of attention and knowledge components. Fitting these models to eye movement and classification behavior from perfect and imperfect learners will characterize the nature of information sampling and learning processes that promote optimal category learning. In Experiments 2 and 3, model components will be linked to the specific neurobiological learning systems implicated in category learning. Experiment 2 will develop and validate a novel method of linking formal models to the learning brain by using multivariate brain patterns of fMRI data to adjudicate among competing cognitive models. The key reasoning of this method is that if a model represents the true nature of category learning, continuous measures of that model's states during learning should be reflected in trial-by-trial measures of the learning brain. Experiment 3 will employ this novel model selection method to characterize how interactions between prefrontal cortex, ventral striatum, and the medial temporal lobe support successful category learning. Understanding the neurobiological mechanisms that support attention and knowledge will provide a means of predicting and promoting effective learning. Moreover, this work has the potential to inform the development of diagnostic tools that precisely characterize cognitive impairments in clinical populations that exhibit learning deficits, such as individuals with schizophrenia, depression, and Alzheimer's disease.