Department of Psychology, University of Bremen.
Department of Psychology, University of Zurich.
J Exp Psychol Gen. 2020 Oct;149(10):1823-1854. doi: 10.1037/xge0000747. Epub 2020 Mar 19.
Reward magnitude is a central concept in most theories of preferential decision making and learning. However, it is unknown whether variable rewards also influence cognitive processes when learning how to make accurate decisions (e.g., sorting healthy and unhealthy food differing in appeal). To test this, we conducted 3 studies. Participants learned to classify objects with 3 feature dimensions into two categories before solving a transfer task with novel objects. During learning, we rewarded all correct decisions, but specific category exemplars yielded a 10 times higher reward (high vs. low). Counterintuitively, categorization performance did not increase for high-reward stimuli, compared with an equal-reward baseline condition. Instead, performance decreased reliably for low-reward stimuli. To analyze the influence of reward magnitude on category generalization, we implemented an exemplar-categorization model and a cue-weighting model using a Bayesian modeling approach. We tested whether reward magnitude affects (a) the availability of exemplars in memory, (b) their psychological similarity to the stimulus, or (c) attention to stimulus features. In all studies, the evidence favored the hypothesis that reward magnitude affects the similarity gradients of high-reward exemplars compared with the equal-reward baseline. The results from additional reward-judgment tasks (Studies 2 and 3) strongly suggest that the cognitive processes of reward-value generalization parallel those of category generalization. Overall, the studies provide insights highlighting the need for integrating reward- and category-learning theories. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
奖励幅度是大多数偏好决策和学习理论的核心概念。然而，当学习如何做出准确决策（例如，对吸引力不同的健康和不健康食物进行分类）时，变量奖励是否也会影响认知过程尚不清楚。为了检验这一点，我们进行了 3 项研究。在解决具有新物体的转移任务之前，参与者学会用 3 个特征维度将物体分类到两个类别中。在学习过程中，我们对所有正确的决策都进行了奖励，但特定类别的示例会产生 10 倍的高奖励（高与低）。违反直觉的是，与等奖励基线条件相比，高奖励刺激物的分类表现并没有增加。相反，低奖励刺激物的表现可靠地下降。为了分析奖励幅度对类别泛化的影响，我们使用贝叶斯建模方法实施了示例分类模型和线索加权模型。我们测试了奖励幅度是否会影响（a）记忆中示例的可用性，（b）它们与刺激的心理相似性，或（c）对刺激特征的注意力。在所有研究中，证据都支持这样的假设，即奖励幅度会影响高奖励示例的相似性梯度，而不是与等奖励基线相比。来自额外奖励判断任务的结果（研究 2 和 3）强烈表明，奖励值泛化的认知过程与类别泛化的认知过程平行。总的来说，这些研究提供了一些见解，强调了需要整合奖励和类别学习理论。（PsycInfo 数据库记录（c）2020 APA，保留所有权利）。
