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

• 批准号: 8114540
• 负责人: BRADLEY C LOVE
• 金额: $ 22.4万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-21 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8114540
• 关键词: Accounting; Address; Affect; Agaricales; Alzheimer' 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; relating to nervous system; reward processing; selective attention; sound; theories

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）：判断一个人是朋友还是敌人，蘑菇是可食用的还是有毒的，或者一个声音是“l”还是“r”都是分类问题的例子。学习的一个关键方面是识别相关的刺激维度，这些维度决定了类别成员资格以及与收集这些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注意机制，学习哪些刺激维度对表现最关键。然而，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处理资源有限所带来的挑战。需要改进模型来理解注意力分配和记忆之间的相互作用。通过将类别学习重塑为一个动态决策过程，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，作为学习者目标、任务要求和知识状态的函数。为了捕捉注意力、记忆力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：一个是根据决策者的目标和对世界的假设来确定潜在信息来源的价值，另一个是反映决策者当前知识的组成部分。由第二学习组件表示的当前知识被第一值组件用来指导信息收集。模型的学习组件由第一组件选择的信息更新，完成相互影响的循环。该提案的一个中心目标是开发模型，对人类能力的局限性做出现实的假设，并描述个人的心理机制和行为结果如何偏离理性原则。第二个目标是联合收割机结合我们的新的基于模型的方法与眼动跟踪和功能磁共振成像（fMRI），以确定支持目标导向的注意力和学习的神经机制。功能磁共振成像数据的基于模型的分析有能力超越传统的分析方法，揭示神经系统之间的复杂动态，引起认知能力。在两项拟议的研究中，参与者必须决定对哪些信息源进行采样，同时考虑到以下相互冲突的需求：（1）最小化信息成本，（2）做出正确的决定，（3）了解更多关于类别和信息源的信息，以提高未来试验的性能。通过将我们的模型与个人的信息寻求和分类行为相匹配，我们可以计算出一些回归量，这些回归量可以跟踪不可观察的心理状态，这些心理状态可以预测随后的行为，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提供对影响学习和记忆的障碍的洞察，提高我们对认知这些强大方面的大脑过程的认识可能会产生现实世界的后果。 公共卫生相关性：学习、记忆和注意力缺陷障碍伴随着许多精神疾病（例如，精神分裂症、重性抑郁症、ADHD）和神经障碍（例如，阿尔茨海默病、癫痫）。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，并可能导致对此类疾病的诊断和治疗产生新的发展。