Task sets define boundaries of learned cognitive flexibility in list-wide proportion switch manipulations.
Task sets define boundaries of learned cognitive flexibility in list-wide proportion switch manipulations.
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任务集定义了列表范围比例开关操作中学习认知灵活性的边界。
DOI：
10.1037/xhp0001138
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Egner,Tobias
中科院分区：
文献类型：
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作者：
Siqi-Liu,Audrey;Egner,Tobias
日常生活中的不同环境通常需要不同程度的认知灵活性。先前的研究表明，人们会调整自己的灵活性水平，以适应提示切换范式中任务切换不断变化的上下文需求，这些范式会改变试验列表中切换试验的比例。具体来说，与重复任务相比，切换的行为成本与切换的比例成反比——这一发现被称为列表范围比例切换（LWPS）效应。先前的研究发现，灵活性适应可以跨刺激转移，但与任务集具体相关，而不是灵活性状态的全块变化。在当前的研究中，我们对灵活性学习是 LWPS 范式中特定任务的假设进行了额外的测试。在实验 1 和 2 中，我们使用试验独特的刺激和无偏见的任务线索来控制与刺激或线索特征相关的联想学习。实验 3 进一步测试了即使针对相同刺激的综合特征执行的任务是否也会发生特定任务的学习。在这三个实验中，我们发现了强大的特定任务的灵活性学习，它可以在新的刺激和无偏见的线索之间转移，并且无论任务之间的刺激特征重叠如何，都会发生这种学习。
Different contexts in daily life often require varying levels of cognitive flexibility. Previous research has shown that people adapt their level of flexibility to match changing contextual demands for task switching in cued-switching paradigms that vary the proportion of switch trials within lists of trials. Specifically, the behavioral costs of switching as opposed to repeating tasks scale inversely with the proportion of switches—a finding referred to as the list-wide proportion switch (LWPS) effect. Previous research found that flexibility adaptations transferred across stimuli, but were specifically tied to task sets, rather than block-wide changes in flexibility state. In the current study, we conducted additional tests of the hypothesis that flexibility learning is task specific in the LWPS paradigm. In Experiments 1 and 2, we used trial-unique stimuli and unbiased task cues to control for associative learning tied to stimulus or cue features. Experiment 3 further tested whether task-specific learning occurred even for tasks performed on integrated features of the same stimuli. Across these three experiments, we found robust task-specific flexibility learning, which transferred across novel stimuli and unbiased cues and occurred regardless of stimulus-feature overlap between tasks.