Abstract
Random errors are omnipresent in sensorimotor tasks due to perceptual and motor noise. The question is, are humans aware of their random errors on an instance-by-instance basis? The appealing answer would be ‘no’ because it seems intuitive that humans would otherwise immediately correct for the errors online, thereby increasing sensorimotor precision. However, here we show the opposite. Participants pointed to visual targets with varying degree of feedback. After movement completion participants indicated whether they believed they landed left or right of target. Surprisingly, participants' left/right-discriminability was well above chance, even without visual feedback. Only when forced to correct for the error after movement completion did participants loose knowledge about the remaining error, indicating that random errors can only be accessed offline. When correcting, participants applied the optimal correction gain, a weighting factor between perceptual and motor noise, minimizing end-point variance. Together these results show that humans optimally combine direct information about sensorimotor noise in the system (the current random error), with indirect knowledge about the variance of the perceptual and motor noise distributions. Yet, they only appear to do so offline after movement completion, not while the movement is still in progress, suggesting that during movement proprioceptive information is less precise.
Highlights
The human sensory and motor systems are less than perfect, due to noise inherent at every stage in the sensory and motor planning and execution pipeline [1]
The very fact, that the noise reveals itself in the variable outcome means that at least no corrections for the noise occurred while performing the movement, even though corrections for the noise would lead to better pointing performance in terms of precision, which is what humans generally optimize in most sensorimotor tasks [10,11,12,13,14]
We investigated human pointing performance for both fast and slow movements, and both without and with varying degrees of visual feedback, and we asked participants after movement completion to indicate the direction of the error they had made
Summary
The human sensory and motor systems are less than perfect, due to noise inherent at every stage in the sensory and motor planning and execution pipeline [1]. Bayesian modeling approaches have shown that the only knowledge needed to perform optimally in most tasks, is an estimate of the overall distribution of the errors and in particular its variance [2,3,4,5,6,7,8,9]. This leaves unaddressed to what extent humans have knowledge of their own noise on a trial-by-trial basis, that is, for each individual movement. It is generally known that the more time given to complete a movement, the more corrections can occur online and the better performance will be in both accuracy and precision (speed-accuracy tradeoff which in the framework of optimal control can be represented as the competition between two opposing cost-functions)
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