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面对损失与收益激励时,增强工作记忆表现和努力分配的有限证据:预先注册的(非)复制。

Limited evidence for enhanced working memory performance and effort allocation in the face of loss versus gain incentives: A preregistered (non) replication.

机构信息

Sleep and Cognition Laboratory, Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

出版信息

Psychophysiology. 2022 Nov;59(11):e14083. doi: 10.1111/psyp.14083. Epub 2022 May 16.

Abstract

The motivation to avoid losses is often considered a strong drive of human behavior, affecting decisions in the context of risk, temporal delay, and effort provision. However, studies measuring cognitive performance under loss and gain incentives have yielded mixed findings. In a recent study, we found evidence that losses motivated better working memory performance than gains. Since these results deviated from previous studies, we decided to perform a preregistered, high-powered, replication, and extension study. Eighty-six participants performed a working memory task (N-Back) under Gain and Loss conditions. Pupillometry was recorded during task performance, and subjective effort ratings were taken after each task run. Results showed that contrary to our previous findings, losses did not enhance working memory performance, relative to gains, at any N-Back level. Similarly, while pupil diameter was parametrically modulated by working memory load, no differences between the Gain and Loss conditions were found. Participants did believe that they were exerting more effort during Loss versus Gain conditions, regardless of N-Back level. However, this did not translate to performance differences between Gain and Loss conditions.

摘要

避免损失的动机通常被认为是人类行为的强大驱动力,它会影响风险、时间延迟和努力投入等情境下的决策。然而,衡量得失激励下认知表现的研究得出了混合的结果。在最近的一项研究中,我们发现损失比收益更能激发更好的工作记忆表现。由于这些结果与之前的研究不同,我们决定进行一项预先注册的、高影响力的、复制和扩展的研究。86 名参与者在收益和损失条件下完成了一项工作记忆任务(N 回任务)。在任务执行过程中记录了瞳孔测量数据,并在每次任务运行后进行了主观努力评估。结果表明,与我们之前的发现相反,在任何 N 回任务水平下,损失并没有比收益更能提高工作记忆表现。同样,虽然瞳孔直径随工作记忆负荷呈参数性调节,但在收益和损失条件之间没有发现差异。参与者确实认为自己在损失条件下比收益条件下付出了更多的努力,而不管 N 回任务水平如何。然而,这并没有转化为收益和损失条件之间的表现差异。

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