The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility. Therefore such responses are referred to as ‘perseveration’ errors. Recent research suggests that the propensity for perseveration errors is modulated by response demands: They occur less frequently when their commitment repeats the previously executed response. Here, we propose parallel reinforcement-learning models of card sorting performance, which assume that card sorting performance can be conceptualized as resulting from model-free reinforcement learning at the level of responses that occurs in parallel with model-based reinforcement learning at the categorical level. We compared parallel reinforcement-learning models with purely model-based reinforcement learning, and with the state-of-the-art attentional-updating model. We analyzed data from 375 participants who completed a computerized WCST. Parallel reinforcement-learning models showed best predictive accuracies for the majority of participants. Only parallel reinforcement-learning models accounted for the modulation of perseveration propensity by response demands. In conclusion, parallel reinforcement-learning models provide a new theoretical perspective on card sorting and it offers a suitable framework for discerning individual differences in latent processes that subserve behavioral flexibility.

威斯康星卡片分类测试 (WCST) 被认为是评估认知灵活性的黄金标准。在 WCST 上，在负面反馈之后重复排序类别通常被视为表示认知灵活性降低。因此，此类响应被称为“坚持”错误。最近的研究表明，坚持错误的倾向受到响应需求的调节：当他们的承诺重复之前执行的响应时，它们发生的频率就会降低。在这里，我们提出了卡片分类性能的并行强化学习模型，该模型假设卡片分类性能可以被概念化为响应级别的无模型强化学习与分类级别的基于模型的强化学习并行发生的结果. 我们将并行强化学习模型与纯粹基于模型的强化学习以及最先进的注意力更新模型进行了比较。我们分析了 375 名完成计算机化 WCST 的参与者的数据。并行强化学习模型对大多数参与者显示出最佳的预测准确性。只有并行强化学习模型才能解释响应需求对坚持倾向的调节。总之，并行强化学习模型为卡片分类提供了一个新的理论视角，它提供了一个合适的框架来识别潜在过程中的个体差异，这些差异促进了行为的灵活性。我们分析了 375 名完成计算机化 WCST 的参与者的数据。并行强化学习模型对大多数参与者显示出最佳的预测准确性。只有并行强化学习模型才能解释响应需求对坚持倾向的调节。总之，并行强化学习模型为卡片分类提供了一个新的理论视角，它提供了一个合适的框架来识别潜在过程中的个体差异，这些差异促进了行为的灵活性。我们分析了 375 名完成计算机化 WCST 的参与者的数据。并行强化学习模型对大多数参与者显示出最佳的预测准确性。只有并行强化学习模型才能解释响应需求对坚持倾向的调节。总之，并行强化学习模型为卡片分类提供了一个新的理论视角，它提供了一个合适的框架来识别潜在过程中的个体差异，这些差异促进了行为的灵活性。