CRCNS: Circuit mechanisms of priors and learning during decision making

当学习一项新任务时，老鼠和人类都表现出受迷信困扰的次优行为 扁虱和特殊的偏见。这种次优的一个突出例子是序贯效应： 动物倾向于根据之前的决定和结果来偏见它们的选择，从而阻碍它们在 使用独立试验的常见实验室任务。 递归神经网络(RNN)已成为一种常用的研究潜在神经机制的工具 认知力。然而，RNN在实验室任务中的表现通常比真正的受试者更接近最佳状态。我们 认为这种行为差异根源于动物和动物之间的根本差异 目前的RNN学习：与学习之前的动物不同，RNN在训练前是Tabula rasa和它们的 连接性仅根据任务的本地意外情况进行调整。 我们假设，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上， 即结构先验，它是通过进化形成的，以适应给定生态环境中的物种 利基市场。顺序效应是这种预先连接的策略的一种表现，这最终可能支持 学习。为了测试这一点，我们将表征一组知觉任务学习过程中的顺序效应 并确定它们潜在的神经回路。我们将把动物的行为与RNN进行比较，之后 拥有先天结构的动物，可以模仿动物学习新任务的能力。 目标 目的1.比较在人和大鼠中的序贯效应与RNN发展的序贯效应。 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.描述相关变量表示的神经机制 大鼠的大脑和RNN中。

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.