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

描述(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，或者声音是\L\或\r\都是分类问题的例子。学习的一个关键方面是辨别相关的刺激维度，这些维度决定了类别成员资格以及与收集此类信息相关的价值和成本(在时间、认知能力和成本方面)。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，无法解决有限的认知和神经处理资源带来的挑战。需要改进的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，该模型根据学习者的目标、任务要求和知识状态对学习过程中的信息进行选择性编码。为了捕捉注意力、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件组成：一个基于决策者的目标和对世界的假设来确定潜在信息源的价值，第二个组件反映决策者当前的知识。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。通过第一个组件选择的信息更新模型的学习组件，完成相互影响的循环。该提案的一个核心目标是开发模型，对人类能力限制做出现实的假设，并表征个人的心理机制和行为结果如何偏离理性原则。第二个目标是将我们新的基于模型的方法与眼球跟踪和功能磁共振成像(FMRI)相结合，以确定支持目标导向注意力和学习的神经机制。基于模型的功能磁共振数据分析具有超越传统分析方法的能力，可以揭示神经系统之间的复杂动态，这些动态导致认知能力的提高。在两项拟议的研究中，参与者必须考虑到(1)最大限度地减少信息成本，(2)做出正确的决定，(3)了解更多关于类别和信息来源的相互冲突的需要，以期在未来的试验中提高绩效，从而决定对哪些信息来源进行抽样。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变量，这些回归变量跟踪无法观察到的心理状态，这些心理状态可以预测后续行为，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。通过提供关于最佳学习策略的知识以及对影响学习和记忆的障碍的洞察，提高我们对构成认知这些强大方面的大脑过程的了解可能会产生现实世界的后果。 公共卫生相关性：学习、记忆和注意力缺陷伴随着许多精神疾病(如精神分裂症、严重抑郁症、多动症)和神经疾病(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，并可能导致与此类疾病的诊断和治疗相关的新发展。

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.