The organization of neural representations for flexible behavior in the human brain

人脑灵活行为的神经表征的组织

• 批准号: 10664958
• 负责人: David Badre
• 金额: $ 68.84万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-06 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10664958
• 关键词: Achievement; Animal Model; Area; Behavior; Behavioral; Brain; Brain region; Code; Consensus; Data; Diagnosis; Dimensions; Electroencephalography; Elements; Employment; Exhibits; Geometry; Goals; Human; Link; Maps; Methodology; Methods; National Institute of Neurological Disorders and Stroke; Nature; Neurologic; Neurons; Neurosciences; Pattern; Performance; Population; Prefrontal Cortex; Property; Rehabilitation therapy; Research; Resolution; Short-Term Memory; Testing; Thinking; clinical application; cognitive control; executive function; experimental study; flexibility; functional magnetic resonance imaging/electroencephalography; fundamental research; high dimensionality; improved; information organization; multitask; neural; novel; programs; response; theories

PROJECT SUMMARY Cognitive control allows us to flexibly guide our actions based on our goals. Central to most prominent theories of cognitive control is the control representation. For control to be successful, this representation is maintained in working memory by the prefrontal cortex (PFC) where it allows the same input to map to different responses depending on the context. Convergent evidence has found that the PFC encodes multiple task-relevant features of a task. However, little is known about the computational features of these control representations based on how they organize this information. This is a fundamental gap in our understanding. Here we focus on one such property, termed representational dimensionality. In technical terms, representational dimensionality refers to the number of axes needed to explain the variance in activity of a neural population across its inputs. Theoretical neuroscience has demonstrated that the dimensionality of a neural population determines a fundamental computational trade-off. A low dimensional representation will discard irrelevant information and form abstractions over its inputs. It is therefore suitable for generalization to new situations. A high dimensional representation encodes multiple mixtures of inputs into highly separable firing patterns without overlap. Understanding how generalizability and separability relate to cognitive control function promises gains on some of the most fundamental problems in control, including context-guided behavior, interference resolution, multitasking, and controlled-to-automatic behavior. The goal of this research program is to link the computational properties of high dimensional control representations to cognitive control function. Our overall hypothesis is that PFC forms high dimensional representations of task features which are needed in behavioral circumstances benefitting from separability. This hypothesis is motivated by theoretical neuroscience and foundational studies that have tested the dimensionality of PFC representations in animal models. However, no study in humans has studied high dimensional codes in PFC and no evidence in any species links dimensionality to cognitive control function. Through an NINDS R21 (NS108380), we have developed and refined two novel, complementary methods for estimating representational dimensionality from fMRI and EEG data. Using these approaches, we have found preliminary evidence that the dorsolateral PFC (DLPFC) forms a high dimensional code relative to other brain areas. We also find evidence from EEG that separability of high dimensional codes improves efficient, flexible behavior and may aid stable readout. Thus, we build on these initial observations to establish the nature, functional significance, and temporal dynamics of high dimensional control representations in the human brain.

项目摘要 认知控制使我们能够根据目标灵活地指导我们的行动。最杰出理论的中心 认知控制的是控制表示。为了使控制成功，保持了这种表示 在前额叶皮层（PFC）的工作记忆中，它允许相同的输入映射到不同的响应 取决于上下文。收敛的证据发现，PFC编码多个任务相关的 任务的功能。但是，对于这些控制表示的计算特征知之甚少 根据他们如何组织此信息。这是我们理解的根本差距。我们在这里集中精力 在一个这样的属性上，称为代表性维度。用技术，代表性 维度是指解释神经种群活动差异所需的轴数 在其输入中。理论神经科学表明神经人群的维度 确定基本的计算权衡。低维表示将丢弃无关紧要 信息并在其输入上形成抽象。因此，它适用于对新情况的概括。一个 高维表示将输入的多种混合物编码为高度可分开的点火模式 没有重叠。了解概括性和可分离性与认知控制功能的关系 承诺在控制中一些最根本的问题，包括上下文引导的行为， 干扰分辨率，多任务和受控的自动行为。 该研究计划的目的是链接高维控制的计算属性 认知控制功能的表示。我们的总体假设是PFC形成高维 在行为环境中需要从可分离性中受益的任务特征的表示。 这一假设是由理论神经科学和基础研究激励的，这些研究已经测试了 PFC表示在动物模型中的维度。但是，没有对人类的研究很高 PFC中的维度代码，任何物种中没有证据将维度与认知控制功能联系起来。 通过Ninds R21（NS108380），我们开发了两种新颖的补充方法 从fMRI和EEG数据中估算代表性维度。使用这些方法，我们发现 初步证据表明背外侧PFC（DLPFC）相对于其他大脑形成高维密码 区域。我们还从脑电图中发现证据表明，高维代码的可分离性提高了有效，灵活 行为，可能有助于稳定的读数。因此，我们以这些最初的观察为基础，建立了性质， 人脑中高维控制表示的功能意义和时间动力学。

项目成果

Abstract cognitive maps of social network structure aid adaptive inference.

社会网络结构的抽象认知图有助于适应性推理。

• DOI: 10.1073/pnas.2310801120
• 发表时间: 2023
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Son,Jae-Young;Bhandari,Apoorva;FeldmanHall,Oriel
• 通讯作者: FeldmanHall,Oriel

Policy abstraction as a predictor of cognitive effort avoidance.

策略抽象作为认知努力回避的预测因子。

• DOI: 10.1037/xge0001449
• 发表时间: 2023
• 期刊: Journal of experimental psychology. General
• 作者: Sayalı,Ceyda;Rubin-McGregor,Jordan;Badre,David
• 通讯作者: Badre,David

Uncertainty aversion predicts the neural expansion of semantic representations.

不确定性厌恶预示着语义表征的神经扩展。

• DOI: 10.1038/s41562-023-01561-5
• 发表时间: 2023
• 期刊: Nature human behaviour
• 影响因子: 29.9
• 作者: Vives,Marc-Lluís;deBruin,Daantje;vanBaar,JeroenM;FeldmanHall,Oriel;Bhandari,Apoorva
• 通讯作者: Bhandari,Apoorva

Abstract task representations for inference and control.

• DOI: 10.1016/j.tics.2022.03.009
• 发表时间: 2022-06
• 期刊: TRENDS IN COGNITIVE SCIENCES
• 影响因子: 19.9
• 作者: Vaidya, Avinash R.;Badre, David
• 通讯作者: Badre, David