Individual differences through self-reinforcement of suboptimal strategies

通过次优策略的自我强化而产生的个体差异

• 批准号: 10702117
• 负责人: Ilana Witten
• 金额: $ 113.52万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-21 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10702117
• 关键词: Address; Animals; Behavior; Behavior Control; Brain; Complex; Dimensions; Disparate; Dopamine; Environment; Event; Feedback; Individual; Individual Differences; Learning; Mediating; Modeling; Nature; Outcome; Penetrance; Psychological reinforcement; Rewards; Role; System; Systems Theory; Testing; Time; Variant; dopaminergic neuron; high dimensionality; individual variation; interest; neuropsychiatric disorder

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.

项目摘要/摘要 是什么产生个体行为差异？这个基本问题在经典上得到了两个 答案：自然和护士。在这里，我们建议这两个答案，虽然关键和正确，但 不足以完全解释个人变异性。相反，我们建议行为上的巨大差异 个体之间的部分原因是不同的个体形成不同的奖励协会 相同的环境。这是由于世界是复杂而高维的事实，因为 几乎总是可以归因于奖励的多个可能的行动或事件。鉴于 多巴胺神经元作为大脑对行为控制的积极反馈系统，具体的假设是 在初始条件下，各个个体之间的微小差异最终会产生较大的差异，其中的特征 个人归因于奖励的环境。该假设的一部分是由复杂系统的启发 理论强调了积极反馈在产生和扩大小差异，创造的作用 似乎随机的结果。为了解决这一假设，我们将利用我们最近的发现不同 多巴胺神经元计算环境不同维度的奖励预测误差。 具体而言，我们将使用多巴胺神经元记录来推断环境的时变特征 每种动物都用来预测奖励，然后根据 这些功能。最终，这个可检验的框架旨在解释个人之间的正常变异，因为 以及多巴胺在介导不同神经精神疾病范围的无处不在的贡献。