Cooper Jessica A, Worthy Darrell A, Maddox W Todd
The University of Texas at Austin, Department of Psychology, 108 E. Dean Keeton, A8000, Austin, TX 78712, United States.
Texas A&M University, Department of Psychology, 4235 TAMU, College Station, TX 77843, United States.
Cogn Psychol. 2015 Dec;83:40-53. doi: 10.1016/j.cogpsych.2015.09.001. Epub 2015 Oct 29.
Research distinguishes between a habitual, model-free system motivated toward immediately rewarding actions, and a goal-directed, model-based system motivated toward actions that improve future state. We examined the balance of processing in these two systems during state-based decision-making. We tested a regulatory fit hypothesis (Maddox & Markman, 2010) that predicts that global trait motivation affects the balance of habitual- vs. goal-directed processing but only through its interaction with the task framing as gain-maximization or loss-minimization. We found support for the hypothesis that a match between an individual's chronic motivational state and the task framing enhances goal-directed processing, and thus state-based decision-making. Specifically, chronic promotion-focused individuals under gain-maximization and chronic prevention-focused individuals under loss-minimization both showed enhanced state-based decision-making. Computational modeling indicates that individuals in a match between global chronic motivational state and local task reward structure engaged more goal-directed processing, whereas those in a mismatch engaged more habitual processing.
研究区分了一个倾向于即时奖励行为的习惯性、无模型系统，以及一个倾向于采取能改善未来状态的行为的目标导向、基于模型的系统。我们研究了在基于状态的决策过程中这两个系统的处理平衡。我们测试了一个调节适配假设（Maddox & Markman，2010），该假设预测全局特质动机影响习惯性与目标导向处理的平衡，但仅通过其与作为收益最大化或损失最小化的任务框架的相互作用来实现。我们发现有证据支持以下假设：个体的长期动机状态与任务框架之间的匹配会增强目标导向处理，从而增强基于状态的决策。具体而言，处于收益最大化框架下的长期促进导向型个体和处于损失最小化框架下的长期预防导向型个体都表现出增强的基于状态的决策。计算建模表明，全局长期动机状态与局部任务奖励结构相匹配的个体进行更多的目标导向处理，而不匹配的个体则进行更多的习惯性处理。
