CRCNS: Circuit mechanisms of priors and learning during decision making

CRCNS：决策过程中先验和学习的循环机制

基本信息

批准号：
10610167
负责人：
Guangyu Yang
金额：
$ 38.78万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-05 至 2025-07-31
项目状态：
未结题

项目摘要

When learning a new task, both rats and humans exhibit suboptimal behaviors plagued with superstitious ticks and idiosyncratic biases. One prominent example of such suboptimality are sequential effects: animals tend to bias their choices based on previous decisions and outcomes, hindering performance in common laboratory tasks using independent trials. Recurrent neural networks (RNN) have become a common tool to study potential neural mechanisms of cognition. Yet, RNNs typically behave much closer to optimality in laboratory tasks than real subjects. We suggest this behavioral difference is rooted in the fundamental discrepancy between how animals and current RNNs learn: unlike animals before learning, RNNs before training are tabula rasa and their connectivity is adjusted exclusively to the local contingencies of the task. We hypothesize that animals’ learning of simple laboratory tasks builds mostly on pre-existing programs, namely structural prior, that have been shaped by evolution for the species’ fitness in a given ecological niche. Sequential effects are a manifestation of such pre-wired strategies, which may ultimately support learning. To test this, we will characterize sequential effects during learning of a set of perceptual tasks and identify their underlying neural circuitry. We will compare animals’ behavior with RNNs which, after being equipped with structural priors, can mimic the animal’s ability to learn new tasks. Objectives Objective 1. Compare sequential effects in humans and rats with those developed by RNNs. Objective 2. Characterize the role of the corticostriatal circuit mPFC --> DMS in the tasks and the site of plasticity necessary for task learning. Objective 3. Characterize the neural mechanisms underlying the representation of relevant variables in the brain of the rat and in RNNs.

当学习一项新任务时，老鼠和人类都会表现出受迷信困扰的次优行为这种次优性的一个突出例子是顺序效应：动物往往会根据之前的决定和结果来做出偏颇的选择，从而阻碍了表现使用独立试验的常见实验室任务。循环神经网络（RNN）已成为研究潜在神经机制的常用工具然而，RNN 在实验室任务中的表现通常比真实受试者更接近最优。表明这种行为差异根源于动物和动物之间的根本差异当前的 RNN 学习：与学习前的动物不同，训练前的 RNN 是白板，它们的连接性专门根据任务的本地突发情况进行调整。我们寻求动物对简单实验室任务的学习主要建立在预先存在的程序的基础上，即结构先验，是通过进化而形成的，以适应特定生态环境中物种的适应性利基效应是这种预先设定的策略的一种表现，这可能最终会支持这种策略。为了测试这一点，我们将描述学习一组感知任务期间的顺序效应。并确定其潜在的神经回路，然后将动物的行为与 RNN 进行比较。配备结构先验，可以模仿动物学习新任务的能力。目标目标 1. 将人类和大鼠的连续效应与 RNN 开发的效应进行比较。目标 2. 描述皮质纹状体回路 mPFC --> DMS 在任务和位置中的作用任务学习所必需的可塑性。目标 3. 表征相关变量表示背后的神经机制大鼠的大脑和 RNN 中。