Interestingly, the ANOVA revealed an interaction of Condition and Task (Table 1), reflecting significantly stronger responses in the function task than in the space task in the insertion action condition (t = 2.8, P < 0.02) but equal activation across both tasks in the tool action condition (t < 1), where both functional and spatiomotor judgments required knowledge of object identity.
Accuracy of the spatial rotation task (Table 4) showed a small overall improvement in the active nicotine nasal spray session and a small decrease in the placebo session with a significant drug effect (p =.037).
Finally, we compared in a hierarchical manner the AUC for the combined HVLT subscales, followed by the AUC for the detection task, and then the cumulative model based on both the HVLT and the detection task (Table 2 and Fig. 3).
After comparing the profit of the tasks, we selected one or more tasks with the lowest level of profit, then replaced it with the emergency task (Table 3).
For the cued recall task, hit rates were high (0.65 0.77); however, false alarm rates were much greater than found in the change discrimination task (Table 3).
It is being observed that learning is present only in the initial production state, i.e., after a while task times converge to steady-state task (Table 1).
We submitted various runs to evaluate the system performance on the test set for both the CDI task and the CEM task (Table 9).
For the mid-level gesture task (Table 8), the system proposed in this paper obtains the best results for all subjects except S4 when the Null class is included (the 4 first columns).
Both patients and controls showed a significant decrease of amplitudes from the first to the fourth block in train one and train two for both pooled cheeks and arm without performing the calculation task (Table 2a and b).
For the high-level locomotion task (Table 7), the system proposed in this paper obtains the best results for all subjects when the Null class is not considered (the 4 last columns).
