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

衡量心理模型复杂性对适应性决策中个体行为和神经差异的作用

• 批准号: 9758624
• 负责人: Alexandre L. Filipowicz
• 金额: $ 6.12万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9758624
• 关键词: Adaptive Behaviors; Addictive Behavior; Affect; Anterior; Anxiety; Arousal; Behavior; Behavioral; Brain; Brain imaging; Caliber; Cognitive; Complex; Computer Simulation; Data; Decision Making; Effectiveness; Environment; Event; Future; Goals; Human; Impairment; Individual; Individual Differences; Information Theory; Insula of Reil; Intervention; Learning; Link; Machine Learning; Measures; Mental Health; Mental disorders; Methods; Modeling; Neurobiology; Neurosciences; Noise; Norepinephrine; Pattern; Physiological; Prefrontal Cortex; Process; Property; Psyche structure; Psychiatry; Psychology; Pupil; Research; Research Training; Resistance; Role; Schizophrenia; Source; System; Testing; Training Programs; Uncertainty; Update; Work; adaptive learning; base; behavioral response; career; field study; flexibility; improved; individual variation; information processing; insight; lens; locus ceruleus structure; neuromechanism; neuroregulation; norepinephrine system; novel; novel strategies; predictive modeling; relating to nervous system; statistics; tool

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.

项目摘要 为了在不确定的环境中做出良好的决定，人类建立和更新相关的“心理模型” 可用于做出预测和指导决策的环境统计数据。当环境时 变化，这些模型需要适应以保留其预测性。这种适应性通常 涉及关键的信息处理权衡，这些权衡良好，但尚未应用 基本上是我们对人脑功能和行为的理解。在这里，我系统地检查了如何 这些权衡是通过行为和大脑成像数据来衡量的，与考虑变异性有关 决策能力通常是跨科目和任务条件的证据。我对行为的关注， 决策能力中个人变异性的计算和神经力学特别相关 进行心理健康研究。许多精神疾病严重破坏了决策 包括动画，精神分裂症和加性行为，但是这些确切机制 干扰尚未完全阐明。我的中心假设是个人和任务依赖性 自适应决策的差异反映了精神复杂性的系统变化 决策所基于的模型。在统计和机器学习领域，预测模型 将过去的观察结果压缩为可以推广到未来的表示。模型的复杂性 确定这种压缩可以说明新信息的灵活性。复杂模型是 更具适应性的（低偏见），但可以过度拟合虚假观察，从而导致更多的行为差异。相比之下， 更简单的模型往往具有较高的偏差，但可变性较低。偏见与差异之间的这种权衡很好 在统计和机器学习中描述，但其对人类心理模型和决策的影响 行为不是众所周知的。该项目的两个主要目的是：1） 可以应用于人类行为数据的精神复杂性； 2）确定心理的影响 模型对参与精神探索的神经调节性脑网络的复杂性 自适应决策以及这些网络中的活动在个人之间的不同。通过链接 具有信息理论，心理学，神经科学和 计算建模，当前的建议将提供一种新颖的镜头，以检查行为和 人类决策中个体变异性的神经生物学来源。而且，这项研究的结果 将为旨在理解和改善决策过程的干预措施提供关键见解 受精神疾病的影响。