Model-Based fMRI of Dynamic Category Learning: The Memory and Attention Interface

基于模型的动态类别学习功能磁共振成像：记忆和注意力接口

基本信息

批准号：
8259406
负责人：
BRADLEY C LOVE
金额：
$ 15.32万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-04-21 至 2014-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8259406
关键词：
Accounting Address Affect Agaricales Alzheimer&apos s Disease Attention Attention deficit hyperactivity disorder Base of the Brain Behavior Behavioral Brain Categories Classification Cognition Cognitive Complex Conflict (Psychology)Data Decision Making Development Diagnosis Dimensions Disease Epilepsy Eye Friends Functional Magnetic Resonance Imaging Future Goals Heart Human Image Analysis Impairment Individual Knowledge Lead Learning Link Major Depressive Disorder Measures Medial Memory Methods Modeling Nature Neurobiology Outcome Participant Pattern Performance Persons Play Prefrontal Cortex Process Psyche structure Recruitment Activity Research Resources Role Sampling Schizophrenia Source Specific qualifier value Stimulus Structure System Temporal Lobe Time To specify Update Ursidae Family Variant Ventral Striatum Weight Work base cost directed attention executive function experience improved information gathering insight mental state nervous system disorder neuromechanism novel novel strategies public health relevance relating to nervous system reward processing selective attention sound theories

项目摘要

DESCRIPTION (provided by applicant): Judging a person as a friend or foe, a mushroom as edible or poisonous, or a sound as an \l\ or \r\ are examples of categorization problems. One key aspect of learning is discerning the relevant stimulus dimensions that determine category membership and the value and costs (in terms of time, cognitive efort, and dollars) associated with gathering such information. Many category learning models employ selective attention mechanisms that learn which stimulus dimensions are most critical to performance. However, these models make the unrealistic assumption that all stimulus dimensions will be encoded, and, thus, fail to address challenges that arise from limited processing resources, both cognitive and neural. Improved models are required to understand the interplay between attentional allocation and memory. By recasting category learning as a dynamic decision process, we develop a model that selectively encodes information during learning as a function of the learner's goals, task demands, and knowledge state. To capture the required interplay between attention, memory, and executive function, our model consists of two primary components: one that determines the value of potential sources of information based on the decision maker's goals and assumptions about the world and a second component that reflects the decision maker's current knowledge. Current knowledge represented by the second learning component is utilized by the first value component to direct information gathering. The learning component of the model is updated by the information selected by the first component, completing the cycle of mutual influence. A central goal of the proposal is to develop models that make realistic assumptions about human capacity limitations and to characterize how individuals' mental machinery and behavioral outcomes deviate from rational principles. A second goal is to combine our novel model-based approach with eye tracking and functional magnetic resonance imaging (fMRI) to determine the neural mechanisms that support goal-directed attention and learning. Model-based analyses of fMRI data have the power to go beyond conventional analysis methods to reveal complex dynamics between neural systems that give rise to cognitive competencies. In two proposed studies, participants must decide which information sources to sample, taking into account the conflicting needs of (1) minimizing information cost, (2) making the correct decision, and (3) learning more about the categories and information sources with the aim of increasing performance on future trials. By fitting our model to individuals' information seeking and classification behavior, we can calculate a number of regressors that track unobservable mental states that are predictive of subsequent behavior and critical for determining the brain basis of the dynamic decision making processes that support category learning. Advancing our knowledge of the brain processes that underlie these powerful aspects of cognition may have real-world consequences by providing knowledge about optimal learning strategies as well as providing insight into disorders that affect learning and memory. PUBLIC HEALTH RELEVANCE: Impairments in learning, memory, and attention deficits accompany a number of psychiatric (e.g., schizophrenia, major depression, ADHD) and neurological disorders (e.g., Alzheimer's disease, epilepsy). Accordingly, understanding the neural mechanisms of attention and memory in the healthy brain promises to advance neurobiological theory and may lead to new developments that bear on the diagnosis and treatment of such conditions.

描述（由申请人提供）：将一个人判断为朋友或敌人，蘑菇可食用或有毒，或者是\ l \或\ r \的声音是分类问题的示例。学习的一个关键方面是辨别相关的刺激维度，这些刺激维度决定了类别成员资格以及与收集此类信息相关的价值和成本（在时间，认知能力和美元方面）。许多类别学习模型采用选择性的注意机制，这些机制学习哪些刺激维度对性能至关重要。但是，这些模型做出了不切实际的假设，即所有刺激维度都将被编码，因此无法解决认知和神经的有限处理资源引起的挑战。需要改进的模型来了解注意力分配和记忆之间的相互作用。通过将类别学习作为动态决策过程，我们开发了一个模型，该模型在学习过程中选择性地编码信息，从而取决于学习者的目标，任务需求和知识状态。为了捕获注意力，记忆和执行功能之间所需的相互作用，我们的模型由两个主要组成部分组成：一个基于决策者对世界的目标和假设，确定潜在信息来源的价值，以及反映决策者当前知识的第二个组成部分。第一个值组件使用第二个学习组件代表的当前知识来指导信息收集。模型的学习组件由第一个组件选择的信息更新，从而完成了相互影响的周期。该提案的一个核心目标是开发对人类能力限制做出现实假设的模型，并表征个人的心理机制和行为结果如何偏离理性原则。第二个目标是将基于新型模型的方法与眼睛跟踪和功能磁共振成像（fMRI）相结合，以确定支持目标指导注意力和学习的神经机制。基于模型的fMRI数据分析有能力超越常规分析方法，以揭示引起认知能力的神经系统之间的复杂动态。在两项拟议的研究中，参与者必须考虑到（1）最小化信息成本的需求，（2）做出正确的决定，以及（3）对类别和信息来源的更多信息，以提高对未来试验的绩效，以最大程度地减少信息成本，（2）最小化信息成本。通过将我们的模型拟合到个人的信息寻求和分类行为中，我们可以计算许多回归器，这些回归变量跟踪无法观察的精神状态，这些状态可预测随后的行为，并且对于确定支持类别学习的动态决策过程的大脑基础至关重要。通过提供有关最佳学习策略的知识以及对影响学习和记忆的疾病的洞察力，可以促进我们对认知这些强大方面的大脑过程的了解，可能会带来现实世界中的后果。公共卫生相关性：许多精神病学（例如精神分裂症，严重抑郁症，ADHD）和神经系统疾病（例如，阿尔茨海默氏病，癫痫病）伴随着学习，记忆和注意力缺陷的障碍。因此，了解健康大脑中注意力和记忆的神经机制有望推进神经生物学理论，并可能导致对这种疾病的诊断和治疗的新发展。