权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CRCNS: Neural Basis of Planning

CRCNS：规划的神经基础

基本信息

批准号：
10410402
负责人：
DAEYEOL LEE
金额：
$ 36.9万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-02 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10410402
关键词：
Algorithms Animals Arbitration Behavior Behavioral Brain Caliber Competence Complex Computers Decision Making Decision Trees Economics Environment Evaluation Exhibits Functional Magnetic Resonance Imaging Future Goals Human Impairment Individual Knowledge Lateral Learning Macaca mulatta Machine Learning Medial Mental Depression Mental disorders Modeling Monitor Monkeys Neurons Neurosciences Outcome Participant Pattern Physiological Play Positioning Attribute Prefrontal Cortex Primates Probability Psyche structure Psychological reinforcement Pupil Research Rewards Role Schizophrenia Series Signal Transduction Speed Strategic Planning Testing Training Trees Update base expectation experimental study fictional works improved learning algorithm neuromechanism neurophysiology nonhuman primate predictive modeling relating to nervous system response simulation source guides

项目摘要

Humans and other animals can choose their actions using multiple learning algorithms and decision making strategies. For example, habitual behaviors adapted to a stable environment can be selected using so-called model-free reinforcement learning algorithms, in which the value of each action is incrementally updated according to the amount of unexpected reward. The underlying neural mechanisms for this type of reinforcement learning have been intensively studied. By contrast, how the brain utilizes the animal's knowledge of its environment to plan sequential actions using a model-based reinforcement learning algorithm remains unexplored. In this application, PIs with complementary expertise will investigate how different subdivisions of the primate prefrontal cortex contribute to the evaluation and arbitration of different learning algorithms during strategic planning in primates, using a sequential game referred to as "4-in-a row". Previous studies have revealed that with training, humans improve their competence in this game by gradually switching away from a model-free reinforcement learning towards a model-based reinforcement learning in the form of a tree search. In the first set of experiments, we will train non-human primates to play the 4-in-a-row game against a computer opponent. We predict that the complexity of the strategic planning and the opponent's move violating the animal's expectation will be reflected in the speed of animal's action and pupil diameters. Next, we will test how the medial and lateral aspects of prefrontal cortex contribute to the evaluation and selection of different learning algorithms during strategic interaction between the animal and computer opponent. We hypothesize that the lateral prefrontal cortex is involved in computing the integrated values of alternative actions originating from multiple sources and guiding the animal's choice, whereas the medial prefrontal cortex might be more involved in monitoring and resolving the discrepancies of actions favored by different learning algorithms. The results from these experiments will expand our knowledge of the neural mechanisms for complex strategic planning and unify various approaches to study naturalistic behaviors. By taking advantage of recent advances in machine learning and decision neuroscience, proposed studies will elucidate how multiple learning algorithms are simultaneously implemented and coordinated via specific patterns of activity in the prefrontal cortex. The results from these studies will transform the behavioral and analytical paradigms used to study high-order planning and their neural underpinnings in humans and animals.

人类和其他动物可以使用多种学习算法和决策策略来选择自己的行为。例如，可以使用所谓的无模型强化学习算法来选择适应稳定环境的习惯行为，其中每个动作的价值根据意外奖励的数量逐步更新。这种强化学习的潜在神经机制已被深入研究。相比之下，大脑如何利用动物对其环境的了解，使用基于模型的强化学习算法来计划顺序动作仍有待探索。在此应用中，具有互补专业知识的 PI 将使用称为“四连胜”的顺序游戏，研究灵长类动物前额叶皮层的不同细分如何在灵长类动物的战略规划过程中评估和仲裁不同的学习算法。先前的研究表明，通过训练，人类通过逐渐从无模型强化学习转向树搜索形式的基于模型的强化学习来提高他们在这个游戏中的能力。在第一组实验中，我们将训练非人类灵长类动物与计算机对手玩四连胜游戏。我们预测，战略规划的复杂性和对手违反动物预期的举动将反映在动物行动的速度和瞳孔直径上。接下来，我们将测试前额叶皮层的内侧和外侧方面如何在动物和计算机对手之间的策略交互过程中有助于评估和选择不同的学习算法。我们假设外侧前额叶皮层参与计算来自多个来源的替代动作的综合值并指导动物的选择，而内侧前额叶皮层可能更多地参与监测和解决不同学习算法所青睐的动作的差异。这些实验的结果将扩展我们对复杂战略规划的神经机制的了解，并统一研究自然行为的各种方法。通过利用机器学习和决策神经科学的最新进展，拟议的研究将阐明如何通过前额皮质的特定活动模式同时实施和协调多种学习算法。这些研究的结果将改变用于研究人类和动物高阶规划及其神经基础的行为和分析范式。