The mutual influence of attention and learning during knowledge acquisition

知识获取过程中注意力和学习的相互影响

• 批准号: 8895804
• 负责人: Michael L. Mack
• 金额: $ 5.8万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8895804
• 关键词: Address; Alzheimer' s Disease; Attention; Base of the Brain; Behavior; Behavioral; Brain; Brain region; Categories; Classification; Clinical; Cognitive; Computer Simulation; Data; Development; Diagnostic; Evaluation; Exhibits; Eye; Eye Movements; Family; Functional Magnetic Resonance Imaging; Future; Goals; Health; Human; Impaired cognition; Individual; Knowledge; Knowledge acquisition; Learning; Link; Literature; Measures; Medial; Mediating; Memory; Mental Depression; Methods; Modeling; Nature; Neurobiology; Neurosciences Research; Pattern; Persons; Population; Prefrontal Cortex; Process; Psyche structure; Research; Resources; Role; Sampling; Schizophrenia; Services; Shapes; Structure; System; Techniques; Temporal Lobe; Testing; Update; Variant; Ventral Striatum; Work; adjudicate; base; cognitive neuroscience; experience; information organization; information processing; knowledge base; neural circuit; neurobiological mechanism; neuroimaging; novel; prototype; psychologic; relating to nervous system; research study; response; selective attention; theories; tool

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.

描述（由申请人提供）：有效的学习涉及提取关键的信息模式，捕捉我们的经验的本质，并使用这些信息来建立有用的知识，使预测行为在新的情况下。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尚不清楚。此外，现有的理论在很大程度上忽略了信息采样行为是如何在学习过程中的经验和目标的指导。本研究将从一个新的理论视角，结合功能磁共振成像（fMRI）和眼动追踪技术，探讨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，个人必须选择什么样的信息样本，这将是由个人的知识和当前的目标。一类计算模型将开发重铸类学习作为一个动态的决策过程中，注意出现的信息处理引导学习者的目标，能力限制，和当前的知识。这些模型计划行动过程（例如，眼睛运动）通过向前看和评估未来的行动预期的利润。实验一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，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，模型组件将与类别学习中涉及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， 在相互竞争的认知模型中做出裁决。这种方法的关键推理是，如果一个模型代表了类别学习的真正本质，那么在学习过程中对该模型状态的连续测量应该反映在学习大脑的一次又一次的测试中。实验3将采用这种新的模型选择方法来表征前额叶皮层，腹侧纹状体和内侧颞叶之间的相互作用如何支持成功的类别学习。理解支持注意力和知识的神经生物学机制将提供一种预测和促进有效学习的方法。此外，这项工作有可能为诊断工具的开发提供信息，这些诊断工具可以精确地表征表现出学习缺陷的临床人群中的认知障碍，例如精神分裂症，抑郁症和阿尔茨海默病患者。