Neural Mechanisms of Learning Relevance in Multidimensional Environments

多维环境中学习相关性的神经机制

• 批准号: 10577778
• 负责人: Thilo Womelsdorf
• 金额: $ 59.51万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10577778
• 关键词: Address; Animals; Anterior; Area; Attention; Behavioral; Behavioral Model; Brain; Brain region; Cells; Cephalic; Classification; Code; Cognitive; Complex; Corpus striatum structure; Data; Decision Making; Dimensions; Dorsal; Electrophysiology (science); Environment; Focused Ultrasound; Future; Hybrids; Interneurons; Intervention; Learning; Lesion; Mediating; Modeling; Monkeys; Neurons; Outcome; Performance; Population; Prefrontal Cortex; Primates; Probability; Process; Protocols documentation; Psychological reinforcement; Regulation; Research; Rewards; Role; Short-Term Memory; Speed; System; Testing; Uncertainty; Update; Visual; attentional bias; cell type; cingulate cortex; cognitive change; coping; expectation; flexibility; high dimensionality; high reward; learning strategy; neural; neural circuit; neuromechanism; nonhuman primate; recruit; tool

PROJECT SUMMARY / ABSTRACT This proposal investigates in the nonhuman primate how attentional load changes the behavioral and neural strategies for flexibly learning object relevance. High attentional load characterizes real-world learning scenarios with multiple, multidimensional objects. Evidence suggests that the neural mechanisms underlying learning during high attentional load fundamentally differs from neural mechanisms used to learn under low load. Our proposal elucidates how learning at increasing attentional load (1) changes the cognitive subcomponent processes used to succeed learning, (2) changes which brain areas are used to flexibly learn, and (3) recruits additional neural circuit mechanisms to realize fast adjustments. First, we will address the specific behavioral subcomponent processes used for learning the relevance of objects in environments with increasing number of visual feature dimensions reflecting increasing attentional load. Simple learning can be achieved efficiently with a hybrid mechanism that uses working memory (WM) of recently rewarded objects to guide future choices together with slower reinforcement learning (RL) for updating longer- term value expectations. When attentional load increases working memory breaks down, and efficient learners flexibly adjust their exploration rates and attentional prioritization to speed up reinforcement learning. Our proposal quantifies these changing learning strategies with multi-component WM-RL modeling. Second, while subjects learn with varying strategies which features to use for making a decision, we will test the causal role of three brain regions implicated to realize the respective learning mechanisms. We use transcranial focused ultrasound stimulation to induce transient, fully reversible lesions allowing to functionally disrupt confined neuronal ensembles. With this tool we elucidate the hypothesized contributions of ventrolateral prefrontal cortex to learning using fast working memory of rewarded objects, the contribution of the anterior cingulate cortex in adjusting exploration strategies and the contribution of the anterior striatum for attentional biasing of slower reinforcement learning of the highest reward-value object within a complex, multidimensional feature space. Third, our project elucidates how the local circuits in each of the three brain areas contribute to successful learning with varying strategies. We use massively parallel recordings of single neuron activity in ventrolateral prefrontal cortex, anterior cingulate cortex, and anterior striatum to extract those cell classes whose firing encodes the key learning variables. We expect that subclasses of interneurons maximally correlate their firing only during those periods when the area specific learning strategy is realized. This approach pinpoints the cell classes that maximally correlate with choice probabilities, prediction errors, working memory, and exploration rates when subjects adjust their learning strategies to successfully learn the relevance of objects with real-world complexity.

项目总结/摘要 本研究旨在探讨非人类灵长类动物的注意负荷如何改变行为和神经系统的变化。 灵活学习对象相关性的策略。高注意力负荷表征真实世界的学习场景 with multiple多dimensional多维objects对象.有证据表明，学习的神经机制 在高注意力负荷下学习的神经机制与在低负荷下学习的神经机制根本不同。我们 建议阐明了如何学习在增加注意力负荷（1）改变认知的子组成部分 用于成功学习的过程，（2）用于灵活学习的大脑区域的变化，以及（3）招募 额外的神经回路机制，以实现快速调整。 首先，我们将讨论用于学习对象相关性的特定行为子组件过程 在视觉特征维度数量增加的环境中，反映了注意力负荷的增加。 简单的学习可以有效地实现与混合机制，使用工作记忆（WM）的最近 奖励对象来指导未来的选择，以及较慢的强化学习（RL）来更新更长的时间- 长期价值预期。当注意力负荷增加时，工作记忆就会崩溃， 灵活调整他们的探索率和注意力优先级，以加快强化学习。我们 建议量化这些变化的学习策略与多组件WM-RL建模。 第二，当受试者用不同的策略学习哪些特征用于做出决定时，我们将测试 三个大脑区域的因果作用，涉及实现各自的学习机制。我们使用经颅 聚焦超声刺激诱导短暂的、完全可逆的损伤，允许功能性地破坏局限的 神经元集合用这个工具，我们阐明了腹外侧前额叶皮层的假设贡献 对于使用奖励对象的快速工作记忆进行学习，前扣带皮层的贡献 调整探索策略和前纹状体对慢动作注意偏向的贡献 在复杂的多维特征空间中对最高奖励值对象进行强化学习。 第三，我们的项目阐明了三个大脑区域中每一个区域的局部回路如何有助于成功地 学习不同的策略。我们使用大规模平行记录的单个神经元活动， 前额叶皮层、前扣带皮层和前纹状体，以提取那些 对关键学习变量进行编码。我们期望中间神经元的亚类最大程度地关联它们的放电 只有在实现特定领域学习策略的时期。这种方法可以精确定位细胞 与选择概率、预测错误、工作记忆和探索最大相关的类别 当受试者调整他们的学习策略，成功地学习对象与现实世界的相关性时， 复杂性