Quantifying the cognitive processes supporting computations of stochasticity and volatility in humans

量化支持人类随机性和波动性计算的认知过程

• 批准号: 10732422
• 负责人: Payam Piray
• 金额: $ 24.75万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10732422
• 关键词: Address; Amygdaloid structure; Animal Behavior; Animal Experimentation; Animal Model; Animals; Anxiety; Anxiety Disorders; Association Learning; Behavioral; Behavioral Paradigm; Biological Markers; Brain; Cognitive deficits; Community Psychiatry; Computer Models; Data; Data Collection; Decision Making; Dependence; Diagnostic; Dissociation; Failure; Functional disorder; Future; General Population; Human; Impaired cognition; Individual; Individual Differences; Joints; Learning; Link; Maps; Measures; Mental Depression; Mental Health; Mental disorders; Methods; Modeling; Neurologic; Neurosciences; Noise; Organism; Outcome; Participant; Patient Self-Report; Patients; Persons; Predisposition; Process; Psychiatry; Psychological reinforcement; Psychopathology; Research; Reversal Learning; Rewards; Running; Schizophrenia; Severities; Source; Symptoms; Technology; Testing; Uncertainty; Work; adaptive learning; addiction; anxious behavior; biomarker development; clinically relevant; cognitive process; computational neuroscience; experience; individual variation; instrument; neuroimaging; neurophysiology; novel; predictive modeling; theories; trait

Maladaptive processes of uncertainty are linked to cognitive dysfunctions in mental illness, such as anxiety, behavioral addictions, and schizophrenia. However, it is not clear what specific computations support this general notion of uncertainty, how they go wrong, and how these same computational constructs transdiagnostically influence many mental illnesses. We plan to address these questions by drawing on our recent theoretical work (Piray and Daw, 2021), which identifies specific computational hypotheses about how uncertainty processes may go wrong when organisms are faced with observations that are corrupted by two types of noise: moment-to-moment stochasticity of observations and volatility, i.e., how quickly they change. Using a novel task, we will address these questions both cross-sectionally (Aim 1) and longitudinally (Aim 2) in a large general population. Statistical principles indicate that volatility and stochasticity have opposite effects on the learning rate, a parameter that determines the reliance on each new outcome during learning. But earlier research in computational neuroscience and computational psychiatry failed to consider mutual dependencies in computing volatility and stochasticity, leaving open the question of how the brain separates these two types of noise in the real world in which they are both unknown. In recent work, we addressed this issue and introduced a model for the joint estimation of both factors. A key prediction of the model is that individuals who are less sensitive to stochasticity are more likely to mistake stochasticity for volatility, and vice versa. This situation might, in principle, arise in psychiatric conditions. Here, we propose a behavioral task that systematically manipulates both volatility and stochasticity. The task, together with the model, allows us to find two key parameters for each subject: sensitivity to stochasticity and sensitivity to volatility. In Aim 1, we will use the task to characterize the cognitive process supporting computations of volatility and stochasticity and link the process parameters to transdiagnostic constructs of psychopathology. In Aim 2, we will determine whether model parameters predict trajectories of clinically relevant symptoms. This project attempts to model complicated cognitive processes that are relevant for understanding learning and decision-making dysfunctions in mental illness by utilizing cutting-edge data collection technologies and by mapping subject-level parameters reflecting individual variations in this process to transdiagnostic self-report measures. This work makes it possible to study the neurophysiological underpinnings of volatility, stochasticity, and uncertainty in the future. This project also has the potential to pave the way for the development of biomarkers for transdiagnostic constructs related to uncertainty computations, which are relevant to anxiety, depression, behavioral addictions, and schizophrenia.

不确定性的适应不良过程与精神疾病中的认知功能障碍有关，例如 焦虑行为成瘾和精神分裂症但是，目前尚不清楚具体计算 支持这种不确定性的一般概念，它们是如何出错的，以及这些同样的计算 这些构念会影响许多精神疾病的转诊。我们计划解决这些问题 通过借鉴我们最近的理论工作（Piray和Daw，2021），它确定了特定的 计算假设关于当生物体 面对被两种类型的噪声破坏的观测： 观察和波动性，即，他们变化得有多快。使用一个新的任务，我们将解决这些问题 问题的横截面（目标1）和纵向（目标2）在一个大的一般人口。 统计学原理表明，波动性和随机性对学习率有相反的影响， 一个参数，决定了学习过程中对每个新结果的依赖。但早期的研究 在计算神经科学和计算精神病学中， 在计算波动性和随机性时，留下了一个悬而未决的问题，即大脑如何将这些 这是真实的世界中的两种噪声，在这两种噪声中，它们都是未知的。在最近的工作中，我们解决了 这一问题，并介绍了一个模型的联合估计的两个因素。一个关键的预测， 一个模型是，对随机性不太敏感的人更容易误解随机性。 波动性，反之亦然。这种情况原则上可能出现在精神病患者身上。这里我们 提出一个行为任务，系统地操纵波动性和随机性。任务， 与模型一起，使我们能够找到每个主题的两个关键参数： 随机性和对波动性的敏感性。在目标1中，我们将使用任务来描述认知 支持波动性和随机性计算的过程，并将过程参数链接到 精神病理学的跨诊断结构在目标2中，我们将确定模型是否 参数预测临床相关症状的轨迹。这个项目试图模拟 与理解学习和决策相关的复杂认知过程 通过利用尖端数据收集技术和绘制地图来研究精神疾病的功能障碍 受试者水平的参数反映了这个过程中的个体差异，以transdiagnosis自我报告 措施这项工作使得研究波动性的神经生理学基础成为可能， 随机性和未来的不确定性。该项目也有可能为 开发与不确定性计算相关的跨诊断结构的生物标志物， 与焦虑、抑郁、行为成瘾和精神分裂症有关。