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

项目总结/摘要 动物表现出一系列非凡的创造性、适应性和灵活性行为。鸟类和灵长类动物 新的材料来建造巢穴和工具;老鼠有效地构建导航捷径，人类概括 一种语言的知识，以有效地说另一种语言。这种动态地创造新颖行为的能力“在一个 单次试验”依赖于组合计划，或通过重组产生策略的心理过程 以前学习的行为成分。至关重要的是，这取决于解释模棱两可的问题（以及 相关的传感数据）。缺乏实验框架来研究 组合规划为了解决这种对新方法的迫切需求，这项建议将阐明神经 我在Freiwald实验室开发的一种新的绘画任务中， 从未见过的视觉形象与先前的研究相反， 外部指导，在这项任务中，图纸必须是内部生成的，并依赖于对 模糊的感官数据我将测试两个中心假设：（1）行为取决于成分 计划，基于行动和排序规则的先验知识，以及（2）额叶皮层活动灵活 重组一个对应于动作和规则的神经活动轨迹“库”。第一个目标将测试 行为取决于行为程序的组成计划的工作假设，或者 从学习的动作词汇表构建的过程（即，如笔画“线”或“弧”）和抽象 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 触摸屏和视频行为数据的工具，并正式比较替代模型。第二个目标是 通过记录大规模的神经元信号， 额叶皮层的活动我将检验一个工作假设，即小说的绘画表现为组合 一个神经活动轨迹编码库的行动和排序规则。第三个目标是利用微 刺激来测试工作假设，即神经活动对计划的因果贡献是 在时间上和解剖学上都是特定的，映射到行为的潜在结构上。我预测 在特定时空位置的神经轨迹的扰动将导致特定的，结构化的， 行为紊乱预期的结果是一个算法的帐户，神经活动是如何基础， 通过对模糊的感觉数据的解释来指导新的复杂动作的规划。这是重要 因为它可以更好地理解大脑如何在创造性思维中运用结构化的先验知识， 推理和行为。这项研究是创新的，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，并整合了认知的概念和工具， 科学与大规模电生理学。这将推动我们对机械的理解的界限， 基于程序内部操作和符号知识的推理。

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.