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好的决策，人类建立和更新相关的“心理模型”。 环境统计可用于预测和指导决策。当环境 这些模型需要适应变化，以保持其预测性。这种适应性通常 涉及到理论上已经很好理解但尚未应用的关键信息处理权衡 对我们理解人类大脑功能和行为有很大的帮助。在这里，我系统地研究如何 这些权衡，从行为和脑成像数据测量，涉及到相当大的可变性， 决策能力，通常是明显的跨学科和任务条件。我专注于行为， 决策能力个体差异的计算和神经机制尤其相关 精神健康的长期研究。许多精神疾病患者的决策能力受到严重干扰 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 干扰还有待充分阐明。我的中心假设是，个人和任务依赖 适应性决策的差异反映了心理复杂性的系统变异性。 决策所依据的模型。在统计学和机器学习领域，预测模型 将过去的观察压缩成可以推广到未来的表示。模型的复杂性 确定了这种压缩可以解释新信息的灵活性。复杂模型 更具适应性（低偏差），但可能过度拟合虚假的观察结果，导致更多的行为变异性。与此相反， 较简单的模型倾向于具有较高的偏差但较低的可变性。偏差和方差之间的权衡是好的， 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 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， 可以应用于人类行为数据的心理复杂性; 2）识别心理复杂性的影响 模型复杂性的神经调节脑网络参与的精神探索所需的 适应性决策，以及这些网络中的活动如何在个体之间存在差异。通过联合 强大的理论框架与方法，从信息论，心理学，神经科学， 计算模型，目前的建议将提供一个新的透镜，以检查行为和 人类决策中个体差异的神经生物学来源。此外，这项研究的结果 将为旨在理解和改善决策过程的干预措施提供重要见解 受到精神疾病的影响。