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pubmed:abstractText |
Control of familiar visually guided movements involves internal plans as well as visual and other online sensory information, though how visual and internal plans combine for reaching movements remain unclear. Traditional motor sequence learning tasks, such as the serial reaction time task, use stereotyped movements and measure only reaction time. Here, we used a continuous sequential reaching task comprised of naturalistic movements, in order to provide detailed kinematic performance measures. When we embedded pre-learned trajectories (those presumably having an internal plan) within similar but unpredictable movement sequences, participants performed the two kinds of movements with remarkable similarity, and position error alone could not reliably identify the epoch. For such embedded movements, performance during pre-learned sequences showed statistically significant but trivial decreases in measures of kinematic error, compared to performance during novel sequences. However, different sets of kinematic error variables changed significantly between learned and novel sequences for individual participants, suggesting that each participant used distinct motor strategies favoring different kinematic variables during each of the two movement types. Algorithms that incorporated multiple kinematic variables identified transitions between the two movement types well but imperfectly. Hidden Markov model classification differentiated learned and novel movements on single trials based on the above kinematic error variables with 82 +/- 5% accuracy within 244 +/- 696 ms, despite the limited extent of changes in those errors. These results suggest that the motor system can achieve markedly similar performance whether or not an internal plan is present, as only subtle changes arise from any difference between the neural substrates involved in those two conditions.
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pubmed:affiliation |
Department of Neuroscience, Warren Alpert Medical School of Brown University, Sidney Frank Hall, Providence, RI 02912, USA. Benjamin_Philip@brown.edu
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