For easy tasks, preparation and movement time increase with the number of moves.
For easy tasks, I task) presented the highest correlation coefficient (p = 0.92), followed by U N) (0.90) and B(N) (0.80).
For easy tasks, the exponential indexes I task) and B(N) were significantly more correlated to human execution time than the linear index U N).
3) The execution times were markedly shorter for young than elderly participants and the steepness of the curve for easy tasks was markedly lower.
1) Like for elderly participants, for easy tasks (2 to 5 moves) the execution time increased but 2) it presented no clear trend for the difficult tasks (5 to 8 moves).
The correlation coefficients (Pearson r) between the execution time and the algorithmic indexes are presented in Table 5 separately for easy tasks (2 to 5 moves) (N = 20) and difficult tasks (5 to 8 moves).
Figure 6 depicts preparation and movement time as a function of the number of moves and the corresponding trend lines computed separately for easy tasks (2 to 5 moves) and difficult tasks (5 to 8 moves).
A meta-analysis by Wood et al. [ 23] reported that goal-setting effects are maximised for easy tasks.
Specific application needs can often be solved via open community platforms such as MATLAB Central, stack overflow, or stack exchange that offer access to source code for easy tasks, such as implementations of 3D skeletonizations in MATLAB (Schena, 2004) or finding branches in a skeleton (stackoverflow.com, 2013).
For easiest task, however, a slight but significant improvement was observed, indicating a learning effect after few repetitions.
For easy-task trials, subjects were eligible to win the same amount, $1.00, on each trial if they successfully completed the task.
