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

被引:227
作者
Green, C. Shawn [1 ]
Pouget, Alexandre [1 ]
Bavelier, Daphne [1 ]
机构
[1] Univ Rochester, Dept Brain & Cognit Sci, Rochester, NY 14627 USA
基金
美国国家卫生研究院;
关键词
REACTION-TIME; SPEED; EXPERIENCE; INFORMATION; SPECIFICITY; PSYCHOLOGY; EFFICIENCY; ACCURACY; DYNAMICS; CHOICE;
D O I
10.1016/j.cub.2010.07.040
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:1573 / 1579
页数:7
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