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。 滴答声和特殊的偏见。这种次优性的一个突出例子是序列效应： 动物倾向于根据先前的决定和结果来偏向它们的选择，这阻碍了它们的表现。 使用独立试验的常见实验室任务。 递归神经网络（RNN）已经成为研究神经网络的潜在神经机制的常用工具。 认知.然而，RNN在实验室任务中的表现通常比真实的受试者更接近最优。我们 这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：与学习之前的动物不同，训练之前的RNN是白板， 连接性专门针对任务的局部突发事件进行调整。 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之上， 即结构优先级，这些优先级是由物种在给定生态环境中的适应性进化而形成的。 利基顺序效应是这种预先设定的策略的一种表现，它最终可能支持 学习为了验证这一点，我们将描述一组感知任务学习过程中的顺序效应 并识别其潜在的神经回路。我们将用RNN比较动物的行为， 具有结构先验，可以模仿动物学习新任务的能力。 目标 目的1.将人类和大鼠的序列效应与RNN开发的序列效应进行比较。 目标2.描述皮质纹状体回路mPFC --> DMS在任务中的作用， 任务学习所必需的可塑性。 目标3.描述相关变量表征的神经机制， 老鼠的大脑和RNN。

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.