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

• 批准号: 10305061
• 负责人: DANIEL C FULFORD
• 金额: $ 24.75万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10305061
• 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

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.

项目总结/摘要 对预期回报的坚持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已经降低的目标。在目标管理方面存在缺陷- 定向行为与冲动相关的特质有关。本项目将开发和测试 计算模型，以考虑在上下文适当的持久性校准的个体差异。 我们将采用一个愿意等待的任务范式，在这个范式中， 在类似觅食的环境中自愿坚持延迟金钱奖励的机会。的 范例中不确定的延迟持续时间的分布可以通过实验来操纵， 高持久性或低持久性都是有利的。来自同一范例的先前结果 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，个体之间的实质性差异已被观察到（1）行为的整体水平 持久性，（2）在环境保持稳定的情况下行为的一致性，以及（3）灵活的适应性 当环境改变时。我们假设个体间的异质性可以解释为 行为生成计算模型潜在参数的个体差异。我们 进一步假设，个体特异性参数估计值将与维度相关 冲动的特质测量我们将通过实施两个新的理论模型来测试假设， 延迟奖励的适应性持久性。第一个是“统计学习”模型， 表示奖励时间的基础上的经验，并产生自适应的持久性决策， 规划机制。第二个是“运动准备”模型，以习惯性的方式产生反应 在过去已经提示过反应的时候。每个模型的内部结构将使用 来自160名社区健康志愿者的任务数据和参数估计值将被测试， 与特质变量的关联。然后，将使用独立的 确证性样本（n = 400）。两个目标模型将相互比较，与空模型比较，以及与 现有的强化学习模型。该结果将为以后的反向翻译奠定基础 非人类模型系统的研究，鉴于实验觅食任务是基于经验的， 非语言的它也将为未来的研究建立一个基础，检查维度的计算基础。 与临床人群中精神病理学相关的结构。