Department of Psychology, Yale University, New Haven, Connecticut, United States.
Wu Tsai Institute, Yale University, New Haven, Connecticut, United States.
J Neurophysiol. 2023 Aug 1;130(2):264-277. doi: 10.1152/jn.00022.2023. Epub 2023 Jun 28.
People form metacognitive representations of their own abilities across a range of tasks. How these representations are influenced by errors during learning is poorly understood. Here, we ask how metacognitive confidence judgments of performance during motor learning are shaped by the learner's recent history of errors. Across four motor learning experiments, our computational modeling approach demonstrated that people's confidence judgments are best explained by a recency-weighted averaging of visually observed errors. Moreover, in the formation of these confidence estimates, people appear to reweight observed motor errors according to a subjective cost function. Confidence judgments were adaptive, incorporating recent motor errors in a manner that was sensitive to the volatility of the learning environment, integrating a shallower history when the environment was more volatile. Finally, confidence tracked motor errors in the context of both implicit and explicit motor learning but only showed evidence of influencing behavior in the latter. Our study thus provides a novel descriptive model that successfully approximates the dynamics of metacognitive judgments during motor learning. This study examined how, during visuomotor learning, people's confidence in their performance is shaped by their recent history of errors. Using computational modeling, we found that confidence incorporated recent error history, tracked subjective error costs, was sensitive to environmental volatility, and in some contexts may influence learning. Together, these results provide a novel model of metacognitive judgments during motor learning that could be applied to future computational and neural studies at the interface of higher-order cognition and motor control.
人们在一系列任务中形成对自己能力的元认知表现。这些表现是如何受到学习过程中错误的影响的，目前还不太清楚。在这里，我们想知道在运动学习过程中，学习者最近的错误历史是如何影响元认知信心判断的。在四项运动学习实验中，我们的计算建模方法表明，人们的信心判断可以通过对视觉观察到的错误进行最近加权平均来最好地解释。此外，在形成这些信心估计值时，人们似乎会根据主观成本函数对观察到的运动错误进行重新加权。信心判断是自适应的，根据学习环境的波动性，以敏感的方式将最近的运动错误纳入其中，在环境更不稳定时纳入较浅的历史记录。最后，信心在内隐和外显运动学习的背景下都能跟踪运动错误，但仅在后者中表现出影响行为的证据。因此，我们的研究提供了一个新的描述性模型，成功地模拟了运动学习中元认知判断的动态。本研究考察了在视动学习过程中，人们对自己表现的信心是如何受到他们最近错误历史的影响的。通过使用计算建模，我们发现信心纳入了最近的错误历史，跟踪了主观的错误成本，对环境的波动性敏感，在某些情况下可能会影响学习。总之，这些结果为运动学习中元认知判断提供了一个新的模型，该模型可以应用于更高阶认知和运动控制界面的未来计算和神经研究。
