Neural Mechanisms of Learning Relevance in Multidimensional Environments

项目摘要/摘要 这项建议在非人类灵长类动物中研究注意力负荷如何改变行为和神经。 灵活学习对象相关性的策略。高注意力负荷是真实世界学习场景的特点 具有多个多维对象。有证据表明，学习的神经机制 在高注意力负荷时，与在低负荷下学习的神经机制有根本的不同。我们的 提案阐明了在增加注意力负荷的情况下学习是如何改变认知子成分的 用于成功学习的过程，(2)改变哪些大脑区域用于灵活学习，以及(3)招募 增加神经回路机制，实现快速调节。 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 在视觉特征维度不断增加的环境中，这些维度反映了不断增加的注意力负荷。 简单的学习可以通过一种使用工作记忆(WM)的混合机制高效地实现 奖励对象用于指导未来的选择，以及较慢的强化学习(RL)用于更新更长时间- 期望值。当注意力负荷增加时，工作记忆就会崩溃，而高效的学习者 灵活调整他们的探索速度和注意优先顺序，以加快强化学习。我们的 Proposal使用多组件WM-RL建模来量化这些不断变化的学习策略。 其次，虽然受试者学习不同的策略来做出决定，但我们将测试 三个大脑区域的因果作用涉及到实现各自的学习机制。我们使用经颅手术 聚焦超声刺激诱导短暂的、完全可逆的损伤，允许功能性破坏受限 神经元组。利用这一工具，我们阐明了前额叶腹外侧皮质的假设贡献 前扣带回对奖赏对象快速工作记忆学习的贡献 调节探索策略和纹状体前部对慢速注意偏向的贡献 在复杂的多维特征空间中对最高奖励值对象的强化学习。 第三，我们的项目阐明了大脑三个区域中每一个区域的局部环路如何对成功 用不同的策略学习。我们使用大量平行记录的单个神经元在腹外侧区的活动。 前额叶皮质、前扣带回皮质和前纹状体，以提取其放电的细胞类别 对关键的学习变量进行编码。我们期望中间神经元的亚类最大限度地关联它们的放电。 只有在特定领域的学习策略实现的那些时期。这种方法可以精确定位细胞 与选择概率、预测误差、工作记忆和探索最相关的类 当受试者调整他们的学习策略以成功地学习对象与现实世界的相关性时的比率 复杂性。

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.