The planning of new compositional action sequences guided by interpretation of ambiguous sensory data in a novel drawing task

在新颖的绘画任务中通过解释模糊的感官数据来规划新的构图动作序列

• 批准号: 10475124
• 负责人: Lucas Y. Tian
• 金额: $ 7.48万
• 依托单位: ROCKEFELLER UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2023-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10475124
• 关键词: Address; Algorithms; Anatomy; Animals; Area; Behavior; Behavioral; Behavioral Mechanisms; Behavioral Model; Behavioral Paradigm; Birds; Brain; Brain Diseases; Clinical Psychology; Cognition Disorders; Cognitive; Cognitive Science; Complex; Data; Disease; Electrophysiology (science); Exhibits; Foundations; Goals; Human; Impairment; Joints; Knowledge; Language; Lead; Learning; Libraries; Link; Literature; Location; Macaca; Maps; Mediating; Mental Processes; Modeling; Motor; Motor Cortex; Neurosciences; Outcome; Primates; Procedures; Process; Psyche structure; Rattus; Research; Sensory; Site; Stroke; Structure; System; Testing; Visual; Vocabulary; base; cognitive capacity; cognitive function; electrical microstimulation; flexibility; frontal lobe; high dimensionality; impaired capacity; innovation; insight; markov model; microstimulation; motor behavior; motor control; neuromechanism; novel; novel strategies; programs; relating to nervous system; spatiotemporal; tool; touchscreen

PROJECT SUMMARY/ABSTRACT Animals exhibit a remarkable array of creative, adaptive, and flexible behaviors. Birds and primates repurpose new materials to build nests and tools; rats efficiently construct navigational shortcuts, and humans generalize knowledge of one language to efficiently speak another. This ability to dynamically create novel behavior “in a single trial” depends on compositional planning, or mental processes that generate strategies by recombining previously learned behavioral components. Crucially, this depends on interpreting ambiguous problems (and associated sensory data) using prior knowledge. There is a dearth of experimental frameworks for studying compositional planning. To address this critical need for new approaches, this proposal will elucidate neural mechanisms in a novel drawing task that I have developed in the Freiwald lab, in which macaques draw copies of never-before-seen visual figures. In contrast to prior studies of action sequences that are memorized or externally guided, in this task drawings must be internally generated and depend on cognitive interpretation of ambiguous sensory data. I will test two central hypotheses: (1) that behavior depends on compositional planning, based on prior knowledge of actions and sequencing rules, and (2) that frontal cortical activity flexibly recombines a “library” of trajectories of neural activity corresponding to actions and rules. The first aim will test the working hypothesis that behavior depends on compositional planning of behavioral programs, or procedures built from a learned vocabulary of actions (i.e., like strokes for “line” or “arc”) and abstract sequencing rules (i.e., higher-order procedures, like “repeat”, “connect”). I will apply unsupervised model-fitting tools to touchscreen and video behavioral data and formally compare alternative models. The second aim is to identify the dynamic neural representations underlying complex drawings by recording large-scale neural activity in frontal cortex. I will test the working hypothesis that novel drawings are represented as combinations of a library of neural activity trajectories encoding actions and sequencing rules. The third aim is to use micro- stimulation to test the working hypothesis that the causal contribution of neural activity towards planning is temporally and anatomically specific in a manner that maps onto the latent structure of behavior. I predict that perturbation of neural trajectories at specific spatio-temporal locations will lead to specific, structured, behavioral perturbations. The expected outcome is an algorithmic account of how neural activity underlies the planning of novel complex actions guided by interpretation of ambiguous sensory data. This is significant because it leads to better understanding of how the brain deploys structured prior knowledge in creative reasoning and behavior. This research is innovative because it introduces a new behavioral paradigm focusing on internally-generated, goal-directed sequences, and integrates concepts and tools from cognitive science with large-scale electrophysiology. This will push the boundaries of our mechanistic understanding of reasoning that is based on internal manipulation of programs and symbolic knowledge.

项目概要/摘要 动物表现出一系列非凡的创造性、适应性和灵活的行为，鸟类和灵长类动物也能重新调整用途。 建造巢穴的新材料和工具；老鼠有效地构建了导航捷径，人类也进行了推广 掌握一种语言以有效地使用另一种语言的能力“以一种方式动态地创造新颖的行为”。 “单一试验”取决于构图规划，或通过重组产生策略的心理过程 至关重要的是，这取决于对模糊问题的解释（以及 使用先验知识的相关感官数据）缺乏用于研究的实验框架。 为了满足对新方法的这一迫切需求，该提案将阐明神经网络。 我在 Freiwald 实验室开发的一项新颖的绘图任务中的机制，其中猕猴绘制副本 与之前对记忆或动作序列的研究形成鲜明对比。 在外部指导下，在此任务中，绘图必须在内部生成并依赖于认知解释 我将测试两个中心假设：（1）行为取决于成分。 基于行动和排序规则的先验知识进行规划，以及（2）额叶皮层活动灵活 重新组合与动作和规则相对应的神经活动轨迹“库”。第一个目标将进行测试。 行为取决于行为程序的组合规划的工作假设，或者 根据学到的动作词汇（即“线”或“弧”的笔划）和抽象构建的程序 排序规则（即高阶过程，如“重复”、“连接”）我将应用无监督模型拟合。 第二个目标是触摸屏和视频行为数据并正式比较替代模型。 通过记录大规模神经网络来识别复杂绘图背后的动态神经表征 我将测试新图画被表示为组合的工作假设。 编码动作和排序规则的神经活动轨迹库的第三个目标是使用微观。 刺激来测试工作假设，即神经活动对计划的因果贡献是 我预测，在时间和解剖学上，以映射到行为的潜在结构的方式是特定的。 特定时空位置的神经轨迹的扰动将导致特定的、结构化的、 预期结果是对神经活动如何构成的算法解释。 通过解释模糊的感官数据来规划新颖的复杂行动，这一点很重要。 因为它可以让我们更好地理解大脑如何在创造性中部署结构化的先验知识 这项研究具有创新性，因为它引入了一种新的行为范式。 专注于内部生成的、目标导向的序列，并整合认知中的概念和工具 大规模电生理学的科学这将突破我们对机械的理解的界限。 基于程序和符号知识的内部操作的推理。