Background: Complex movement sequences are composed of segments with different levels of functionality:\r\nintended segments towards a goal and segments that spontaneously occur largely beneath our awareness. It is not\r\nknown if these spontaneously-occurring segments could be informative of the learning progression in na�¯ve\r\nsubjects trying to skillfully master a new sport routine.\r\nMethods: To address this question we asked if the hand speed variability could be modeled as a stochastic process\r\nwhere each trial speed depended on the speed of the previous trial. We specifically asked if the hand speed\r\nmaximum from a previous trial could accurately predict the maximum speed of a sub-sequent trial in both\r\nintended and spontaneous movement segments. We further asked whether experts and novices manifested similar\r\nmodels, despite different kinematic dynamics and assessed the predictive power of the spontaneous fluctuations in\r\nthe incidental motions.\r\nResults: We found a simple power rule to parameterize speed variability for expert and novices with accurate\r\npredictive value despite randomly instructed speed levels and training contexts. This rule on average tended to\r\nyield similar exponent across speed levels for intended motion segments. Yet for the spontaneous segments the\r\nspeed fluctuations had exponents that changed as a function of speed level and training context. Two conditions\r\nhighlighted the expert performance: broad bandwidth of velocity-dependent parameter values and low noise-tosignal\r\nratios that unambiguously distinguished between training regimes. Neither of these was yet manifested in\r\nthe novices.\r\nConclusions: We suggest that the statistics of intended motions may be a predictor of overall expertise level,\r\nwhereas those of spontaneously occurring incidental motions may serve to track learning progression in different\r\ntraining contexts. These spontaneous fluctuations may help the central systems to kinesthetically discriminate the\r\nperipheral re-afferent patterns of movement variability associated with changes in movement speed and training\r\ncontext. We further propose that during learning the acquisition of both broad bandwidth of speeds and low\r\nnoise-to-signal ratios may be critical to build a verifiable kinesthetic (movement) percept and reach the type of\r\nautomaticity that an expert acquires.
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