Modeling Dimensions of Individual Variation in Adaptive Foraging Decisions

自适应觅食决策中个体差异的建模维度

基本信息

批准号：
10458065
负责人：
DANIEL C FULFORD
金额：
$ 20.63万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10458065
关键词：
Animals Back Behavior Behavioral Biological Models Calibration Clinical Cognitive Communities Complex Computer Models Consequentialism Cues Data Data Set Decision Making Dimensions Elements Environment Event Exhibits Face Foundations Future Goals Habits Human Impairment Impulsivity Individual Individual Differences Informal Social Control Intervention Investigation Learning Measures Mental Health Mental disorders Modeling Motor Neurobiology Online Systems Outcome Participant Performance Pharmacology Population Preparation Probability Process Psyche structure Psychological reinforcement Psychopathology Research Rewards Sampling Source Structure Testing Theoretical model Time Translational Research Uncertainty base computational basis effective therapy experience experimental study flexibility healthy volunteer individual variation inter-individual variation novel prospective relating to nervous system response self reported behavior simulation statistical learning success trait willingness

项目摘要

PROJECT SUMMARY/ABSTRACT Persistence toward prospective rewards is a critical element of normative real-world decision making. Equally important is the ability to disengage from goals that have diminished in value. Deficits in regulating goal- directed behavior are associated with impulsivity-related traits. The present project will develop and test computational models to account for individual differences in the context-appropriate calibration of persistence. We will employ a willingness-to-wait task paradigm in which human decision makers are given repeated opportunities to persist voluntarily toward delayed monetary rewards in a foraging-like environment. The distribution of uncertain delay durations in the paradigm can be experimentally manipulated to create an environment in which either high or low persistence is advantageous. Previous results from the same paradigm have shown that, on average, decision makers tend to adjust their behavior appropriately for their environment. However, substantial differences across individuals have been observed in (1) overall levels of behavioral persistence, (2) the consistency of behavior while the environment remains stable, and (3) flexible adaptation when the environment changes. We hypothesize that inter-individual heterogeneity can be accounted for in terms of individual differences in the latent parameters of behavior-generating computational models. We further hypothesize that individual-specific parameter estimates will be proximally associated with dimensional trait measures of impulsivity. We will test the hypotheses by implementing two novel theoretical models of adaptive persistence toward delayed rewards. The first, a "statistical learning" model, hones an internal representation of reward timing on the basis of experience and produces adaptive persistence decisions using a planning mechanism. The second, a "motor preparation" model, produces responses in a habit-like manner at times when responses have been cued in the past. The internal structure of each model will be refined using task data from 160 community-based healthy volunteers, and parameter estimates will be tested for associations with trait variables. The models will then be validated and compared using an independent confirmatory sample (n = 400). The two target models will be compared to one another, to a null model, and to an existing reinforcement learning model. The results will establish a basis for future back-translational research in non-human model systems, given that the experimental foraging task is experience-based and non-linguistic. It will also establish a basis for future studies examining the computational basis of dimensional constructs relevant to psychopathology in clinical populations.

项目摘要/摘要对潜在奖励的持久性是规范现实世界决策的关键要素。同样重要的是能够脱离价值降低的目标的能力。规范目标的缺陷 - 定向行为与冲动性相关的性状有关。本项目将开发和测试计算模型，以说明持久性的上下文校准中的个体差异。我们将采用等待意愿的任务范式，其中重复人类决策者在类似觅食的环境中自愿延迟货币奖励的机会。这可以通过实验操纵范式中不确定的延迟持续时间的分布以创建一个高持久性或低持久性的环境是有利的。同一范式的先前结果已经表明，平均而言，决策者倾向于为环境适当调整其行为。但是，在（1）行为的总体水平中已经观察到了个体之间的实质差异持久性，（2）在环境保持稳定时行为的一致性，（3）灵活的适应性当环境发生变化时。我们假设可以考虑个体间的异质性行为生成计算模型的潜在参数的个体差异术语。我们进一步假设个体特异性参数估计将与维度近端相关联冲动性的特质度量。我们将通过实施两个新颖的理论模型来检验假设适应性的持久性延迟奖励。首先是“统计学习”模型，对内部进行磨练根据经验代表奖励时机，并使用计划机制。第二个“运动制备”模型以习惯方式产生反应有时，过去提出了回应。每个模型的内部结构将使用来自160位社区健康志愿者的任务数据和参数估计将进行测试与性状变量的关联。然后将使用独立的模型进行验证和比较验证性样本（n = 400）。将两个目标模型相互比较，与无效模型进行比较，并将其与现有的强化学习模型。结果将为将来的反向翻译建立基础鉴于实验觅食任务是基于经验的，并且非语言。它还将为未来研究的基础研究尺寸的计算基础与临床人群中与心理病理学相关的构造。