Individual differences through self-reinforcement of suboptimal strategies

项目总结/摘要 是什么导致了个体行为的差异？这一基本问题经典地被赋予了两个 答案：先天和后天。在这里，我们建议这两个答案，虽然都是关键和正确的， 不足以完全解释个体差异。相反，我们认为行为上的巨大差异 个体之间的差异部分源于不同的个体形成不同的奖励协会， 相同的环境。这是因为世界是复杂和高维的， 几乎总是多个可能的行动或事件，可以归因于奖励。鉴于以下方面的关键作用 多巴胺神经元作为大脑行为控制的正反馈系统，具体的假设是， 个体之间在初始条件上的微小差异最终会产生很大的差异， 个人认为是奖励的环境。这一假设的灵感部分来自于复杂的系统 理论，强调积极反馈在产生和放大微小差异，创造 看似随机的结果为了解决这个假设，我们将利用我们最近的发现， 多巴胺神经元计算环境不同维度的奖励预测误差。 具体来说，我们将使用多巴胺神经元记录来推断环境的时变特征， 每个动物都使用它来预测奖励，然后根据每个动物的行为建立强化学习模型。 这些特征。最终，这个可测试的框架旨在解释个体之间的正常变化， 以及多巴胺在介导一系列不同的神经精神疾病中的普遍作用。

Project Summary/Abstract What produces individual differences in behavior? This fundamental question has classically been given two answers: nature and nurture. Here, we suggest that those two answers, while both critical and correct, are insufficient to fully explain individual variability. Instead, we propose that the vast differences in behavior between individuals arise in part from different individuals forming different reward associations within the same environment. This results from the fact that the world is complex and high-dimensional, in that there are almost always multiple possible actions or events that could be attributed to reward. Given the key role of dopamine neurons as the brain’s positive feedback system for behavioral control, the specific hypothesis is that small differences across individuals in initial conditions ultimately produce large differences in which features of the environment that the individual attributes to reward. This hypothesis is inspired in part by complex systems theory, which emphasizes the role of positive feedback in generating and amplifying small differences, creating outcomes that seem stochastic. To address this hypothesis, we will leverage our recent finding that different dopamine neurons calculate reward prediction error across different dimensions of the environment. Specifically, we will use dopamine neuron recordings to infer the time-varying features of the environment that each animal uses to predict reward, and then build reinforcement learning models of each individual based on these features. Ultimately, this testable framework aims to explain both normal variation across individuals, as well as the ubiquitous contribution of dopamine in mediating a disparate range of neuropsychiatric diseases.