Therefore, 2 follow-up experiments were performed using independent samples of task-naive participants to clarify the extent to which low-frequency rTMS was capable of disrupting task-naive probabilistic association learning, as opposed to suppressing facilitation of learning dependent on multiple session practice or between session consolidation (as per Robertson 2009).
Maintainability prediction therefore requires knowledge of failure rates in order to select the appropriate, weighted sample of tasks.
Maintainability prediction requires knowledge of failure rates in order to select the appropriate, weighted, sample of tasks.
Hypotheses were tested among a sample of task-performing group members over a four-month period (N's = 328 432).
Because of limited time resources by the experts a randomized selected sample of tasks (ca. 75%) was rated by each expert.
By examining different types of attentional disengagements, cognitive abilities, and motivation and interest with a large sample of tasks and participants, we aimed to better elucidate the nature of attentional engagement and disengagement in educational contexts.
Thus, in order to determine whether disruption of DLPFC interferes with acquisition of the probabilistic cue outcome associations during the first administration in task-naive participants, we conducted Experiment 2 in which rTMS or sham was administered to the DLPFC during the first administration of the weather prediction test in an independent sample of task-naive participants.
I divided the transcription of audio/video recordings, observation notes, and written samples of both tasks (e.g., GPT and VPT) into separate segments.
MTL is an approach to inductive transfer that improves generalization by using the domain information contained in the training samples of related tasks as an inductive bias [20].
Because the arrow appeared more frequently in task-irrelevant than in task-relevant locations, a random sample of these task-irrelevant updates (equivalent to the average number of updates contributing to the other locations) was selected for switch and repetition updates.
The first operation is to calculate the cumulative laxity of the task t (denoted CL t ) (line 5) as: {CL}_t=frac{SCT_t-{s}_j}{SCT_j-{s}_j}times {L}_j where SCT t is the sample completion time of task t.
