Associate Professor Susanne Jaeggi has co-edited with German colleagues Julia Karbach & Tilo Strobach the JCE special issue titled "Enhancing Brain and Cognition Through Cognitive Training" (Journal of Cognitive Enhancement December 2017, Volume 1, Issue 4).
The issue features 14 contributions by 62 authors, representing work that has been conducted in various countries and labs. Twelve of the papers are primary research articles, one is a meta-analytic review, and one an opinion paper.
Researchers Jaeggi, Karbach, and Strobach contributed the editorial: "Enhancing Brain and Cognition Through Cognitive Training":
Two of the featured papers are from members of Professor Jaeggi's research group:
1. "The Benefits and Challenges of Implementing Motivational Features to Boost Cognitive Training Outcome" by Shafee Mohammed, Lauren Flores, Jenni Deveau, Russell Cohen Hoffing, Calvin Phung, Chelsea M. Parlett, Ellen Sheehan, David Lee, Jacky Au, Martin Buschkuehl, Victor Zordan, Susanne M. Jaeggi, & Aaron R. Seitz
Abstract: In the current literature, there are a number of cognitive training studies that use N-back tasks as their training vehicle; however, the interventions are often bland, and many studies suffer from considerable attrition rates. An increasingly common approach to increase participant engagement has been the implementation of motivational features in training tasks; yet, the effects of such “gamification” on learning have been inconsistent. To shed more light on those issues, here, we report the results of a training study conducted at two Universities in Southern California. A total of 115 participants completed 4 weeks (20 sessions) of N-back training in the laboratory. We varied the amount of “gamification” and the motivational features that might make the training more engaging and, potentially, more effective. Thus, 47 participants trained on a basic color/identity N-back version with no motivational features, whereas 68 participants trained on a gamified version that translated the basic mechanics of the N-back task into an engaging 3D space-themed “collection” game (Deveau et al. Frontiers in Systems Neuroscience, 8, 243, 2015). Both versions used similar adaptive algorithms to increase the difficulty level as participants became more proficient. Participants’ self-reports indicated that the group who trained on the gamified version enjoyed the intervention more than the group who trained on the non-gamified version. Furthermore, the participants who trained on the gamified version exerted more effort and also improved more during training. However, despite the differential training effects, there were no significant group differences in any of the outcome measures at post-test, suggesting that the inclusion of motivational features neither substantially benefited nor hurt broader learning. Overall, our findings provide guidelines for task implementation to optimally target participants’ interest and engagement to promote learning, which may lead to broader adoption and adherence of cognitive training.
2. "Training Change Detection Leads to Substantial Task-Specific Improvement" by Martin Buschkuehl, Susanne M. Jaeggi, Shane T. Mueller, Priti Shah, & John Jonides
Abstract: Previous research has demonstrated that adaptive training of working memory can substantially increase performance on the trained task. Such training effects have been reported for performance on simple span tasks, complex span tasks, and n-back tasks. Another task that has become a popular vehicle for studying working memory is the change-detection paradigm. In a typical change-detection trial, one has to determine whether a set of stimuli is identical to a set that was presented just previously. Here, we developed an adaptive training regimen comprised of increasingly difficult change-detection trials to assess the degree to which individuals’ change-detection performance can be improved with practice. In contrast to previous work, our results demonstrate that participants are able to dramatically improve their performance in change detection over the course of 10 training sessions. We attribute this improvement to the current training method that adaptively adjusted the set size of the change-detection task to the proficiency of the trainee. Despite these considerable training effects, an exploratory investigation revealed that these improvements remained highly task specific and may not generalize to untrained tasks.
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