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.
References
[1]
Faisal A, Selen L, Wolpert D (2008) Noise in the nervous system. Nat Rev Neurosci 9: 292–303.
[2]
Ernst M, Banks M (2002) Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415: 429–433.
[3]
Blake A, Bülthoff H, Sheinberg D (1993) Shape from texture - Ideal observers and human psychophysics. Vision Res 33: 1723–1737.
[4]
Landy M, Maloney L, Johnston E, Young M (1995) Measurement and modeling of depth cue combination - In defense of weak fusion. Vision Res 35: 389–412.
[5]
van Beers R, Sittig A, Denier van der Gon J (1999) Integration of proprioceptive and visual position-information: an experimentally supported model. J Neurophysiol 81: 1355–1364.
[6]
Baddeley R, Ingram H, Miall R (2003) System identification applied to a visuomotor task: Nearoptimal human performance in a noisy changing task. J Neurosci 23: 3066–3075.
[7]
Burge J, Ernst M, Banks M (2008) The statistical determinants of adaptation rate in human reaching. J Vis 8: 1–19.
[8]
K？rding K, Wolpert D (2004) Bayesian integration in sensorimotor learning. Nature 427: 244–247.
[9]
Korenberg A, Ghahramani Z (2002) A Bayesian view of motor adaptation. Cah Psychol Cogn-Curr Psychol Cogn 21: 537–564.
[10]
Miall R, Wolpert D (1993) Is the Cerebellum a Smith Predictor? J Mot Behav 25: 203–216.
[11]
Wolpert D, Ghahramani Z, Jordan M (1995) An internal model for sensorimotor integration. Science 269: 1880–1882.
[12]
Harris C, Wolpert D (1998) Signal-dependent noise determines motor planning. Nature 394: 780–784.
[13]
Hamilton A, Wolpert D (2002) Controlling the statistics of action: Obstacle avoidance. J Neurophysiol 87: 2434–2440.
[14]
Todorov E, Jordan M (2002) Optimal feedback control as a theory of motor coordination. Nat Neurosci 5: 1226–1235.
[15]
van Beers RJ (2009) Motor Learning Is Optimally Tuned to the Properties of Motor Noise. Neuron 63: 406–417.
[16]
Sabes P, Jordan M (1997) Obstacle avoidance and a perturbation sensitivity model for motor planning. J Neurosci 17: 7119–7128.
[17]
Jeannerod M (1988) The neural and behavioral organization of goal-directed movements. New York, USA: Oxford University Press.
[18]
Goble DJ, Noble BC, Brown SH (2010) Where was my arm again? Memory-based matching of proprioceptive targets is enhanced by increased target presentation time. Neurosci Lett 481: 54–58.
[19]
Cheron G, Dan B, Borenstein S (2000) Sensory and motor interfering influences on somatosensory evoked potentials. J Clin Neurophysiol 17: 280–294.
[20]
Chapman C, Bushnell M, Miron D, Duncan G, Lund J (1987) Sensory perception during movement in man. Exp Brain Res 68: 516–524.
[21]
Post L, Zompa I, Chapman C (1994) Perception of vibrotactile stimuli during motor-activity in human-subjects. Exp Brain Res 100: 107–120.
[22]
Vitello M, Fritschi M, Ernst M (2012) Active movement reduces the tactile discrimination performance. In: Peer A, Giachritsis C, Immersive Multimodal Interactive Presence. Springer-Verlag London Limited, Springer series on Touch and Haptic Systems, 7–34.
[23]
Collins D, Refshauge K, Todd G, Gandevia S (2005) Cutaneous receptors contribute to kinesthesia at the index finger, elbow, and knee. J Neurophysiol 94: 1699–1706.
[24]
Edin B, Johansson N (1995) Skin strain patterns provide kinesthetic information to the human central-nervous-system. J Physiol-London 487: 243–251.
[25]
Edin B (2001) Cutaneous afferents provide information about knee joint movements in humans. J Physiol-London 531: 289–297.
[26]
Trommersh？user J, Gepshtein S, Maloney L, Landy M, Banks M (2005) Optimal compensation for changes in task-relevant movement variability. J Neurosci 35: 7169–7178.
[27]
Wei K, Koerding K (2010) Uncertainty of feedback and state estimation determines the speed of motor adaptation. Front Comput Neurosci 4.
[28]
Wichmann F, Hill N (2001) The psychometric function: I. fitting, sampling, and goodness of fit. 63: 1293–1313.
