Zusammenfassung
Training variability has been brought forward as one possible moderator for wider scale transfer effects in cognitive training. However, little is known about which aspects of task variability are important for optimizing training outcomes. This study systematically examined the impact of variability in the different task components on outcome measures, here manipulating content (whether the task ...
Zusammenfassung
Training variability has been brought forward as one possible moderator for wider scale transfer effects in cognitive training. However, little is known about which aspects of task variability are important for optimizing training outcomes. This study systematically examined the impact of variability in the different task components on outcome measures, here manipulating content (whether the task stimuli remained fixed or changed between blocks) and the deeper structural task configuration (task sequence: whether the task sequence was fixed or random). Short-term task switching training was implemented with one of four training variability conditions: fixed content
structure; fixed content
random structure; varied content structure and varied content
structure. The experiment consisted of a baseline block, seven training blocks (learning phase), followed by two transfer blocks, one with fixed and one with random task structure, respectively. In the learning phase, more rapid training gains were observed in the fixed content as compared to varied content. Interestingly, training with fixed content resulted in a trend for costs when transferred to a novel task switching context. In contrast, moderate transfer gains were noted in the varied content condition, manifested specifically on switch trials. These results suggest that task (content) variability is one of the means to improve positive transfer and avoid negative transfer. Additionally, and in agreement with the wide literature on training, this finding suggests that conditions that prevent training gains are in fact beneficial for learning generalization.
Altmetric