Independent control of finger movements characterizes skilled motor behaviors such as tool use and musical performance. covariation between the striking and buy 552292-08-7 nonstriking fingers at both metacarpo-phalangeal and proximal-interphalangeal joints across the two tempi, which indicated no effect of tempo on independent finger movements in piano playing. In addition, the standard deviation of interkeystroke interval across strokes did not differ between the two tempi, indicating maintenance of rhythmic accuracy of keystrokes. Strong temporal constraints on finger movements during piano playing may underlie the maintained independent control of fingers over a wider range of tempi, a feature being likely to be specific to skilled pianists. Op. 10, Nos. 1, 4, and 8 and Op. 25, Nos. 11 and 12 by buy 552292-08-7 Bnip3 Frdric Chopin; touch, meaning that a key was not released until the next key was depressed. Pianists were asked to play at the loudness of 100 MIDI velocity. Fig. 4. Combined results from all pianists summarize the similarities and differences in playing tempo and rhythmic accuracy of strokes between normal and fast tempi. Mean (basic PC waveforms, computed from the covariance matrix of the tone sequence vectors (ranging from 49 to 64). The covariance calculation removes the mean from each of the columns of the input matrix. Thus the angular velocity waveforms at each joint for each tone sequence (400 time units for each joint) could be perfectly reconstructed as the average angular velocity at a PC waveforms (PC1are the weighting coefficients for an of the first four PCs at all tone sequences in the buy 552292-08-7 cluster analysis, those PCs accounting for >60% of the total variance for all digits at both of the two tempi (for a keypress with each of the index, middle, ring, and little fingers, the group mean of variance accounted for by the first 4 PCs was 66.7 2.1, 65.6 3.8, 65.2 3.7, and 61.8 4.7 at the normal tempo and 68.6 4.7, 68.2 5.1, 66.9 6.8, and 61.3 6.3% at the fast tempo, respectively). The number of clusters set for the EM algorithm was two to six, and for each of the numbers the sum of variance of the weighting coefficients within each cluster (the sum of within-cluster variance) was computed as follows: is the quantity of clusters (= 1, 2,, 6), is the quantity of a sequence belonging to that cluster, is the total number of sequences, and is the vector consisting of the of the 1st four PCs. The value wc2 was computed buy 552292-08-7 for each pianist and for the keystroke with each of four fingers separately and then averaged across pianists. The average value was plotted relative to the number of clusters utilized for the EM. A breakpoint of the plotted curve was used to determine an ideal quantity of clusters for further analysis. To further ascertain the optimal quantity of clusters, we also computed the silhouette value for each quantity of clusters, using the weighting buy 552292-08-7 coefficients (Rousseeuw 1987). The value becomes larger as the within-cluster variance and between-cluster variance become smaller and larger, respectively. The silhouette value ranges from ?1 to 1 1, depending on whether the sample has been assigned to an appropriate cluster or misclassified. We consequently reasoned the mean silhouette value across pianists should be largest at the optimal cluster number. Statistics To assess the amount of covariation of joint motion across fingers, a linear regression analysis was performed for motions in the MCP and PIP bones between the impressive finger and each of the nonstriking fingers at each firmness sequence for each of the index, middle, ring, and little finger keystrokes. The derived and (Fig. 2[tempo effect, < 0.01). In the PIP joint, ANOVA found that none of any pair of fingers/bones showed significant difference in the value for tempo effect was 0.07,.