Global performances in the Sample Clauses

Global performances in the spatial-tapping task The statistical analyses of variance conducted on the asynchronies revealed that our participants tapped in synchrony with the regular metronome across all tempi with relatively small mean errors and variances close to ±100 ms at tempi slower than 600 ms, and ± 50 ms at faster tempi. The intervals of time were on average accurately produced across tempi, i.e. with IRIerrors close to zero, except for the fastest trials performed at 300 ms of IOI for which the IRIerror significantly increased. The spatial error varied following three speed intervals: best performance in space was reached between 1100 to 900 ms of IOI; the spatial error was slightly but significantly increased (larger mean area) between 800 to 600 ms of IOI as compared to slower trials, and was finally the largest for trials performed at tempi of 500, 400 and 300 ms of IOI. The analysis of the orientation of the endpoint scatterplots provided the means to consider whether actions were planned in a discrete or continuous way across tempi. Significant changes in the mode of planning control were revealed around 700 ms of XXX. From 700 ms of IOI, the ellipses were on average signif- icantly more oriented towards preceding targets, suggesting that less attention resources were available to focus attention on discrete elements. From 600 ms of IOI, the ellipses were on average significantly more oriented as a function of the tangent to the circle, suggesting that from this specific IOI, the movements became more circular. Hence, these results suggest that participants were able to follow the instruc- tions, which was to keep at best the synchrony across all tempi. The spatial error was increased stepwise with changing tempi with maximum errors observed for tempi faster than 500 ms of IOI. As predicted, the trajectories of the pointing actions were discrete at slow tempi and became more continuous from 600 ms of IOI. In the next paragraph, we detail the results obtained for the autocorrelation values.
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