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.

项目概要/摘要对预期奖励的坚持是现实世界规范决策的关键要素。同样重要的是能够摆脱已经贬值的目标。调节目标方面的缺陷- 定向行为与冲动相关的特征有关。本项目将进行开发和测试计算模型来解释持久性的上下文适当校准中的个体差异。我们将采用愿意等待的任务范式，其中人类决策者被重复给予在类似觅食的环境中自愿坚持延迟金钱奖励的机会。这范式中不确定延迟持续时间的分布可以通过实验操作来创建高持久性或低持久性均有利的环境。相同范式的先前结果研究表明，平均而言，决策者倾向于根据环境适当调整自己的行为。然而，在（1）行为总体水平上观察到个体之间存在显着差异。持久性，（2）环境保持稳定时行为的一致性，以及（3）灵活的适应当环境发生变化时。我们假设个体间的异质性可以解释为行为生成计算模型的潜在参数的个体差异。我们进一步假设个体特定的参数估计将与维度密切相关冲动的特质测量。我们将通过实施两个新颖的理论模型来检验这些假设对延迟奖励的适应性坚持。第一个是“统计学习”模型，磨练内部知识根据经验表示奖励时间，并使用以下方法产生自适应持久性决策规划机制。第二种是“运动准备”模型，以类似习惯的方式产生反应有时，过去已经提示过反应。每个模型的内部结构将使用细化来自 160 名社区健康志愿者的任务数据和参数估计将被测试与特征变量的关联。然后，将使用独立的机构对模型进行验证和比较验证样本（n = 400）。两个目标模型将相互比较，与空模型进行比较，并与现有的强化学习模型。结果将为未来的反向翻译奠定基础研究非人类模型系统，考虑到实验觅食任务是基于经验的，并且非语言的。它还将为未来检查维度计算基础的研究奠定基础与临床人群的精神病理学相关的构建。