Improved Probabilistic Inference as a General Learning Mechanism with Action Video Games

• DOI: 10.1016/j.cub.2010.07.040
• Publication Date: 2010-09-15T08:05:54Z
• Authors: C. Shawn Green; Alexandre Pouget; Daphne Bavelier
• Keywords: *Probability; *Learning; Agricultural and Biological Sciences(all); Biochemistry, Genetics and Molecular Biology(all); info:eu-repo/classification/ddc/150; Decision Making; 05 social sciences; info:eu-repo/classification/ddc/616.8; ddc:616.8; ddc:150; Visual Perception; Humans; Learning; 0501 psychology and cognitive sciences; Probability

Action video game play benefits performance in an array of sensory, perceptual, and attentional tasks that go well beyond the specifics of game play [1-9]. That a training regimen may induce improvements in so many different skills is notable because the majority of studies on training-induced learning report improvements on the trained task but limited transfer to other, even closely related, tasks ([10], but see also [11-13]). Here we ask whether improved probabilistic inference may explain such broad transfer. By using a visual perceptual decision making task [14, 15], the present study shows for the first time that action video game experience does indeed improve probabilistic inference. A neural model of this task [16] establishes how changing a single parameter, namely the strength of the connections between the neural layer providing the momentary evidence and the layer integrating the evidence over time, captures improvements in action-gamers behavior. These results were established in a visual, but also in a novel auditory, task, indicating generalization across modalities. Thus, improved probabilistic inference provides a general mechanism for why action video game playing enhances performance in a wide variety of tasks. In addition, this mechanism may serve as a signature of training regimens that are likely to produce transfer of learning.