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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结