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: 10211527
• 负责人: Thilo Womelsdorf
• 金额: $ 75.69万
• 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/10211527
• 关键词: Address; Animals; Anterior; Area; Attention; Behavioral; Behavioral Model; Brain; Brain region; Cells; 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; base; cell type; cingulate cortex; cognitive change; expectation; flexibility; high dimensionality; high reward; learning strategy; neural circuit; neuromechanism; nonhuman primate; recruit; relating to nervous system; 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.

项目摘要 /摘要 该提案在非人类灵长类动物中调查了注意力载荷如何改变行为和神经 灵活学习对象相关性的策略。高度注意负载是现实世界学习场景的特征 具有多个多维对象。证据表明，学习的神经机制 在高度注意力载荷期间，从根本上讲，与在低负荷下学习的神经机制不同。我们的 提案阐明了在增加注意力载荷时如何学习（1）改变认知子组件 用于成功学习的过程，（2）更改哪些大脑区域用于灵活学习，以及（3）新兵 实现快速调整的其他神经回路机制。 首先，我们将解决用于学习对象相关性的特定行为子组件过程 在具有越来越多的视觉特征维度的环境中，反映了注意力负载的增加。 可以通过使用最近的工作记忆（WM）的混合机制来有效地实现简单的学习 奖励对象，以指导未来的选择以及较慢的增强学习（RL），以更新更长 期限期望。当注意力负载增加工作记忆时，有效的学习者 灵活地调整其探索率和注意力优先级，以加快加强学习的速度。我们的 建议通过多组分WM-RL建模量化这些不断变化的学习策略。 其次，尽管受试者以各种策略来学习，这些策略可用于做出决定，但我们将测试 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 聚焦超声刺激以诱导短暂的，完全可逆的病变，使功能上断限制 神经元合奏。使用此工具，我们阐明了腹外侧前额叶皮层的假设贡献 学习使用奖励物体快速工作的记忆，前扣带回皮质的贡献 调整勘探策略以及前纹状体对注意较慢的偏见的贡献 在复杂的多维特征空间中对最高奖励价值对象的强化学习。 第三，我们的项目阐明了三个大脑区域中每个区域的本地电路如何成功 通过不同的策略学习。我们在腹外侧使用单个神经元活性的大量平行记录 前额叶皮层，前扣带回皮层和前纹状体，以提取那些射击的细胞类别 编码密钥学习变量。我们期望中间神经元的子类最大化他们的射击 只有在实现特定领域学习策略的那些时期。这种方法查明细胞 与选择概率，预测错误，工作记忆和探索最大相关的类 当受试者调整学习策略以成功地学习对象与现实世界的相关性时，费率 复杂。