Measuring the role of mental model complexity on individual behavioral and neural differences in adaptive decision making

项目总结 为了在不确定的环境中做出正确的决定，人类建立和更新相关的“心理模型” 可用于预测和指导决策的环境统计数据。当环境发生变化时 为了应对变化，这些模型需要具有适应性，以保持其可预测性。这种适应性通常 涉及理论上已很好理解但尚未应用的关键信息处理权衡 对我们对人脑功能和行为的理解大有裨益。在这里，我系统地研究如何 这些取舍都是通过行为和脑成像数据来衡量的，与 决策能力通常在不同的科目和任务条件下都很明显。我对行为的关注， 个人决策能力的计算和神经机制的差异尤其相关 心理健康方面的长期研究。在许多精神疾病中，决策受到严重干扰 包括焦虑、精神分裂症和成瘾行为，但这些行为背后的确切机制 这些干扰还没有完全阐明。我的中心假设是个体和任务依赖型 适应性决策的差异反映了心理复杂性的系统性差异 决策所依据的模型。在统计学和机器学习领域，预测模型 将过去的观察结果压缩成可以概括到未来的表示形式。模型的复杂性 确定此压缩可用于说明新信息的灵活性。复杂的模型有 更适应(低偏见)，但可能会超过虚假的观察，导致更多的行为变异性。相比之下， 较简单的模型往往具有较高的偏倚，但变异性较低。这种偏差和方差之间的权衡是很好的 在统计学和机器学习中描述，但它对人类心理模型和决策的影响 其行为并不为人所知。该项目的两个主要目标是：1)制定一项原则性措施 可应用于人类行为数据的心理复杂性；以及2)识别心理影响 神经调节脑网络参与心理探索所需的模型复杂性 适应性决策，以及这些网络中的活动在不同个人之间有何不同。通过将一个 强大的理论框架，方法来自信息论、心理学、神经科学和 计算建模，目前的提议将提供一种新的透镜，用来检查行为和 人类决策中个体变异性的神经生物学来源。此外，这项研究的结果 将为旨在理解和改进决策过程的干预措施提供至关重要的见解 受精神疾病的影响。

PROJECT SUMMARY To make good decisions in uncertain environments, humans build and update ‘mental models’ of relevant environmental statistics that can be used to make predictions and guide decision-making. When the environment changes, these models need to be adaptable to retain their predictiveness. This kind of adaptability typically involves key information-processing trade-offs that are well understood theoretically but have yet to be applied substantially to our understanding of human brain function and behavior. Here I examine systematically how these trade-offs, measured both from behavior and brain-imaging data, relate to the considerable variability in decision-making abilities that are typically evident across subjects and task conditions. My focus on behavioral, computational, and neural mechanisms of individual variability in decision-making abilities is particularly relevant to long-term research in mental health. Decision-making is severely disrupted in a number of mental illnesses including anxiety, schizophrenia, and addictive behaviors, but the exact mechanisms underlying these disruptions have yet to be fully elucidated. My central hypothesis is that individual and task-dependent differences in adaptive decision-making reflect systematic variability in the complexity of the mental models upon which the decisions are based. In the fields of statistics and machine learning, predictive models compress past observations into representations that can generalize to the future. A model’s complexity determines the flexibility with which this compression can account for new information. Complex models are more adaptive (low bias) but can overfit spurious observations, leading to more behavioral variability. In contrast, simpler models tend to have higher bias but lower variability. This tradeoff between bias and variance is well described in statistics and machine learning, but its influence on human mental models and decision-making behavior is not well known. The two primary aims of this project are: 1) to develop a principled measure of mental complexity that can be applied to human behavioral data; and 2) to identify the influence of mental model complexity on neuromodulatory brain networks involved in the mental exploration required for adaptive decision-making, and how activity in these networks differs across individuals. By linking a strong theoretical framework with methods from information theory, psychology, neuroscience, and computational modeling, the current proposal will provide a novel lens with which to examine behavioral and neurobiological sources of individual variability in human decision-making. Moreover, the results of this research will provide crucial insights for interventions aimed at understanding and improving decision-making processes affected by mental illnesses.