Department of Psychology, University of California, Berkeley, 2121 Berkeley Way, Berkeley, CA, 94704, USA.
Department of Psychology, Princeton University, Peretsman-Scully Hall, Princeton, NJ, 08540, USA.
Nat Commun. 2019 Jan 3;10(1):40. doi: 10.1038/s41467-018-07941-0.
Computations underlying cognitive strategies in human motor learning are poorly understood. Here we investigate such strategies in a common sensorimotor transformation task. We show that strategies assume two forms, likely reflecting distinct working memory representations: discrete caching of stimulus-response contingencies, and time-consuming parametric computations. Reaction times and errors suggest that both strategies are employed during learning, and trade off based on task complexity. Experiments using pressured preparation time further support dissociable strategies: In response caching, time pressure elicits multi-modal distributions of movements; during parametric computations, time pressure elicits a shifting distribution of movements between visual targets and distal goals, consistent with analog re-computing of a movement plan. A generalization experiment reveals that discrete and parametric strategies produce, respectively, more localized or more global transfer effects. These results describe how qualitatively distinct cognitive representations are leveraged for motor learning and produce downstream consequences for behavioral flexibility.
人类运动学习中的认知策略的基础计算理解得很差。在这里，我们研究了一种常见的感觉运动转换任务中的这种策略。我们表明，策略有两种形式，可能反映了不同的工作记忆表示：刺激-反应关联的离散缓存，以及耗时的参数计算。反应时间和错误表明，在学习过程中同时使用了这两种策略，并根据任务的复杂性进行权衡。使用有压力的准备时间的实验进一步支持了可分离的策略：在响应缓存中，时间压力会引起运动的多模态分布；在参数计算中，时间压力会导致运动在视觉目标和远端目标之间的分布发生变化，这与运动计划的模拟重新计算一致。一个泛化实验表明，离散和参数策略分别产生更局部或更全局的转移效应。这些结果描述了如何利用性质截然不同的认知表示来进行运动学习，并对行为灵活性产生下游影响。
