The Benefits and Challenges of Implementing Motivational Features to Boost Cognitive Training Outcome

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.

N-Back Related ERPs Depend on Stimulus Type, Task Structure, Pre-processing, and Lab Factors

“…Nine variants of the N-Back task were obtained by crossing three task structures (see below) with three stimulus types : words (i.e., so, do, up), pictures (i.e., apple, fish, and bag), and colors (i.e., red, green, and blue). Task structures differed in terms of stimulus duration, ISI, response contingency, and feedback (see Figure 1 ) and were modeled after tasks used in previous studies, as mentioned above: task 1 (Pahor and Jaušovec, 2018 ), task 2 (Pergher et al, 2018 ), and task 3 (Mohammed et al, 2017 ).…”

Application of Machine Learning Models for Tracking Participant Skills in Cognitive Training

“…In these training programs, task difficulty is adaptively adjusted based on performance. The main difference between the two training programs is the game environment: Recall is a 3D space-themed game (video demo can be found here: https://www.youtube.com/watch?v=zx_t6paHB3Y ) in which the participant is in control of a spaceship (Deveau et al, 2015 ; Mohammed et al, 2017 ). The participant must zap target stimuli while also collecting fuel pods for the space ship (non-targets).…”

“…(Middle) Example for a two-back level in the gamified Recollect condition. (Bottom) Example for a two-back level in the gamified Recall condition (Mohammed et al, 2017). each, with 2 sessions conducted per day with a 10 min break between sequential sessions.…”

Editorial Special Topic: Enhancing Brain and Cognition Through Cognitive Training

“…Focusing specifically on motivational effects related to the training environment, Mohammed et al (2017) compared the effects of four weeks of training on a gamified working-memory training (a 3D space-themed "collection" game; Deveau et al, 2015) to working-memory training without motivational features. Participants in the game-training group reported that they enjoyed the intervention more and exerted more effort than the group who trained on the basic version.…”

“…Most of the studies referenced above were primarily interested in the investigation of underlying mechanisms and individual differences, and thus, it is maybe not surprising that very few of them included any outcome measures that might reflect real-world performance (but see Hering et al, 2017, Mohammed et al, 2017, and Ralph et al, 2017). Nonetheless, given the common assumption that cognitive training approaches are broadly beneficial, it would be valuable if researchers would routinely include measures that serve at least as proxies for everyday life.…”