No Effect of Commercial Cognitive Training on Brain Activity, Choice Behavior, or Cognitive Performance

Abstract

Increased preference for immediate over delayed rewards and for risky over certain rewards has been associated with unhealthy behavioral choices. Motivated by evidence that enhanced cognitive control can shift choice behavior away from immediate and risky rewards, we tested whether training executive cognitive function could influence choice behavior and brain responses. In this randomized controlled trial, 128 young adults (71 male, 57 female) participated in 10 weeks of training with either a commercial web-based cognitive training program or web-based video games that do not specifically target executive function or adapt the level of difficulty throughout training. Pretraining and post-training, participants completed cognitive assessments and functional magnetic resonance imaging during performance of the following validated decision-making tasks: delay discounting (choices between smaller rewards now vs larger rewards in the future) and risk sensitivity (choices between larger riskier rewards vs smaller certain rewards). Contrary to our hypothesis, we found no evidence that cognitive training influences neural activity during decision-making; nor did we find effects of cognitive training on measures of delay discounting or risk sensitivity. Participants in the commercial training condition improved with practice on the specific tasks they performed during training, but participants in both conditions showed similar improvement on standardized cognitive measures over time. Moreover, the degree of improvement was comparable to that observed in individuals who were reassessed without any training whatsoever. Commercial adaptive cognitive training appears to have no benefits in healthy young adults above those of standard video games for measures of brain activity, choice behavior, or cognitive performance.SIGNIFICANCE STATEMENT Engagement of neural regions and circuits important in executive cognitive function can bias behavioral choices away from immediate rewards. Activity in these regions may be enhanced through adaptive cognitive training. Commercial brain training programs claim to improve a broad range of mental processes; however, evidence for transfer beyond trained tasks is mixed. We undertook the first randomized controlled trial of the effects of commercial adaptive cognitive training (Lumosity) on neural activity and decision-making in young adults (N = 128) compared with an active control (playing on-line video games). We found no evidence for relative benefits of cognitive training with respect to changes in decision-making behavior or brain response, or for cognitive task performance beyond those specifically trained.

Trial registration: ClinicalTrials.gov NCT01252966.

Keywords: cognitive training; delay discounting; impulsivity; neuroimaging; working memory.

Figure 1.

Decision-making task outcomes. Performance on the delay discounting and risk sensitivity tasks in each group at pretreatment and post-treatment scan sessions. In the multiple regression models, there were no treatment by time interaction effects on decision-making task performance (p values >0.5).

Figure 2.

Whole-brain analyses of neural activity. Mean activation (choice trials vs baseline; A, B) and subjective value effects (C, D) across the whole brain, for both the delay discounting (A, C) and risk sensitivity (B, D) tasks, as well as changes in mean activation from pretreatment to post-treatment in the risk sensitivity task (E), independent of treatment condition. Subjective value effects were determined using parametric regressors based on discount rate and risk sensitivity parameters estimated from each subject and orthogonalized to the task regressor. There were no effects of treatment condition on changes in neural activity over time in either task. All brain images are height thresholded at p < 0.001 to form clusters and are corrected for multiple comparisons using permutation testing on cluster mass at p < 0.05. The 3-D brain images were generated using the surface-rendering tool Surf Ice, developed at the University of South Carolina. Source code for the program is available at www.nitrc.org/projects/surfice/.

Figure 3.

ROI analyses of neural activity. Mean activation (choice trials vs baseline; top row) and subjective value effects (parametric contrast; bottom row) in dlPFC, vmPFC, and VS ROIs for both the delay discounting and risk sensitivity tasks. There were no effects of treatment condition on changes in neural activity for any ROI in either task. Solid lines, cognitive training; dashed lines, active control. Pre, Pretreatment; Post, post-treatment.

Figure 4.

Practice effects on cognitive measures. A, Composite cognitive performance scores (averaged z-scores across all five cognitive tests) by treatment group and testing session. There were significant main effects of treatment (participants in the no-contact control group scored lower than the other two groups at all sessions; p = 0.02) and testing session (participants in all conditions improved over time; p < 0.0001), but there was no treatment by session interaction effect (p = 0.85). B, Matching subsets of participants on baseline performance. There were significant effects of testing session (p < 0.0001), but there were no main effects of treatment (p = 0.64) or a treatment by session interaction (p = 0.86).

Figure 5.

Performance over time in cognitive training group. Performance on trained tasks over time in the cognitive training group, grouped by adherence to the training schedule. In the multiple regression model, there was a significant adherence (continuous measure) by time interaction effect (β = 0.02, p < 0.001). For simplicity, adherence is graphed by tertile based on the percentage of assigned sessions that were completed (low adherence, <74% completed; moderate adherence, 75–88% completed; high adherence, 89–100% completed).