Modeling Dimensions of Individual Variation in Adaptive Foraging Decisions

项目摘要/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 重要的是有能力摆脱那些价值已经降低的目标。在监管目标方面存在缺陷- 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 考虑到个体差异的计算模型，在适当的情况下校准持久性。 我们将采用一种愿意等待的任务模式，在这种模式下，人类决策者被反复给予 在一个类似觅食的环境中，自愿坚持延迟的金钱回报的机会。这个 该范例中的不确定延迟持续时间的分布可以被实验性地操纵以创建 具有较高或较低持续性的有利环境。之前来自相同范式的结果 已经表明，平均而言，决策者倾向于根据他们所处的环境适当地调整他们的行为。 然而，观察到不同个体在(1)总体行为水平上存在显著差异。 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 个体差异术语在行为生成计算模型的潜在参数中。我们 进一步假设个体特定的参数估计将与维度近似相关 衡量冲动的特质。我们将通过实施两个新的理论模型来检验假设 对延迟奖励的适应性坚持。第一种是“统计学习”模型，它磨练了内部的 基于经验的奖励时间表示，并使用以下方法产生自适应持续决策 一种规划机制。第二种是“运动准备”模式，以习惯的方式产生反应。 在过去已经暗示了反应的时候。每种型号的内部结构都将使用 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 与特质变量的关联。然后，将使用独立的 确认样本(n=400)。将对这两个目标模型进行相互比较、与空模型和与 现有的强化学习模型。这一结果将为未来的回译奠定基础 在非人类模型系统中进行研究，因为实验觅食任务是基于经验的 非语言的。它还将为未来研究维度的计算基础奠定基础 临床人群中与精神病理学相关的结构。

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.