Manley Harry, Dayan Peter, Diedrichsen Jörn
Institute of Cognitive Neuroscience, University College London, London, United Kingdom ; Institute for the Psychology of Elite Performance, Bangor University, Bangor, United Kingdom.
Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.
PLoS One. 2014 Jan 28;9(1):e86580. doi: 10.1371/journal.pone.0086580. eCollection 2014.
When performing a skill such as throwing a dart, many different combinations of joint motions suffice to hit the target. The motor system adapts rapidly to reduce bias in the desired outcome (i.e., the first-order moment of the error); however, the essence of skill is to produce movements with less variability (i.e., to reduce the second-order moment). It is easy to see how feedback about success or failure could sculpt performance to achieve this aim. However, it is unclear whether the dimensions responsible for success or failure need to be known explicitly by the subjects, or whether learning can proceed without explicit awareness of the movement parameters that need to change. Here, we designed a redundant, two-dimensional reaching task in which we could selectively manipulate task success and the variability of action outcomes, whilst also manipulating awareness of the dimension along which performance could be improved. Variability was manipulated either by amplifying natural errors, leaving the correlation between the executed movement and the visual feedback intact, or by adding extrinsic noise, decorrelating movement and feedback. We found that explicit, binary, feedback about success or failure was only sufficient for learning when participants were aware of the dimension along which motor behavior had to change. Without such awareness, learning was only present when extrinsic noise was added to the feedback, but not when task success or variability was manipulated in isolation; learning was also much slower. Our results highlight the importance of conscious awareness of the relevant dimension during motor learning, and suggest that higher-order moments of outcome signals are likely to play a significant role in skill learning in complex tasks.
在执行诸如投掷飞镖这样的技能时,许多不同的关节运动组合都足以命中目标。运动系统会迅速适应,以减少预期结果中的偏差(即误差的一阶矩);然而,技能的本质是产生变异性更小的动作(即减少二阶矩)。很容易理解关于成功或失败的反馈如何塑造表现以实现这一目标。然而,尚不清楚成功或失败所涉及的维度是否需要被受试者明确知晓,或者学习是否可以在没有明确意识到需要改变的运动参数的情况下进行。在此,我们设计了一个冗余的二维伸手任务,在这个任务中,我们可以有选择地操纵任务的成功与否以及动作结果的变异性,同时还能操纵对可以改进表现的维度的认知。变异性的操纵方式有两种,一种是放大自然误差,保持执行动作与视觉反馈之间的相关性不变,另一种是添加外在噪声,使动作和反馈去相关。我们发现,只有当参与者意识到运动行为必须改变的维度时,关于成功或失败的明确二元反馈才足以促进学习。如果没有这种认知,只有在反馈中添加外在噪声时才会出现学习,而当单独操纵任务成功或变异性时则不会出现学习;学习速度也会慢得多。我们的结果凸显了运动学习过程中对相关维度的意识的重要性,并表明结果信号的高阶矩可能在复杂任务的技能学习中发挥重要作用。