Learning, attentional control, and action video games

Abstract

While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world training or rehabilitation paradigms, which necessarily seek to promote more general learning. In contrast to these typical findings, research over the past decade has shown that training on 'action video games' produces learning that transfers well beyond the training task. This has led to substantial interest among those interested in rehabilitation, for instance, after stroke or to treat amblyopia, or training for various precision-demanding jobs, for instance, endoscopic surgery or piloting unmanned aerial drones. Although the predominant focus of the field has been on outlining the breadth of possible action-game-related enhancements, recent work has concentrated on uncovering the mechanisms that underlie these changes, an important first step towards the goal of designing and using video games for more definite purposes. Game playing may not convey an immediate advantage on new tasks (increased performance from the very first trial), but rather the true effect of action video game playing may be to enhance the ability to learn new tasks. Such a mechanism may serve as a signature of training regimens that are likely to produce transfer of learning.

Copyright © 2012 Elsevier Ltd. All rights reserved.

Figures

Figure 1

Learning is highly specific to the training conditions. (A) Subjects trained to discriminate motion direction around one reference angle (45°) improve substantially over the course of training (improvement shown in red). However, when tested on the same task, but the opposite direction (225°), no benefit of training is observed (transfer, or lack thereof, illustrated in blue-adapted from [25]). (B) Subjects trained to produce one nonsense string while the mouth is physically perturbed show substantial learning (red bar). However, the learning does not transfer to a task wherein the same perturbation is used, but a new nonsense string must be produced (blue bar). Aadapted from [90]. (C) Expert baseball players show substantial reaction-time advantages over non-experts in a task with similar processing demands as those that occur while batting in baseball (Go/NoGo = Swing/Don’t Swing; red bar). However, no such advantage is observed in a simple reaction-time task, which has no such baseball equivalent (blue bar ). Adapted from [91]. D) Expert chess players show a substantial advantage over non-experts in the ability to recall the position of chess pieces only when the pieces are in real-game typical positions (red bar). When pieces are positioned randomly, no advantage is observed (blue bar). Adapted from [21].

Figure 2

Needs a short title. Although experience with many tasks with the same underlying structure did not result in enhanced performance on trial number one (indeed, it cannot help, the best the animal can do is guess the correct answer), it did result in a substantial increase in the rate at which the new tasks were learned. Adapted from [35].

Figure 3

Improved selective spatial attention after action game play. (A) Several versions of the Useful Field of View Task (different timings, masks, targets, and so on) have been employed to test changes in selective spatial attention that arise due to action video game experience [92]. (B) Across all task versions, avid action game players (blue) demonstrate enhanced performance as compared to non-action game players (green). (C) A causal link between playing action video games and enhanced performance on the Useful Field of View task has been assessed in a number of training studies. Training non-action game players on action games leads to an increase in Useful Field of View performance (blue bars highlight performance before and after action training), while training on non-action games leads to lesser, or no such improvement (green bars highlight performance before and after control training). Adapted from [55,56,59,60,73].

Figure 4

Training study design. Individuals who report playing little to no video games (both males and females) are recruited and pre-tested on measures of interest. The pre-test measures are specifically designed to minimize task specific learning (for example, small numbers of trials, no feedback). Following pre-test, the groups are randomly assigned to play either an action game or a non-action, control game. The games are matched as closely as possible for as many aspects of game play as possible (identification with character, fun, ‘flow’, and so on) while leaving attentional and action demands different. Subjects come to the lab to play the game one to two hours a day (maximum of 10 hours a week) for anywhere from 10 to 50 hours depending on the study. Once the training is completed (and at least 24 hours after the last training session ends to ensure that any observed effects are not due to transient changes in physiology/arousal), subjects complete similar tasks as during pre-test. A causal role of action game playing is indicated by a larger change from pre- to post-test in the action trained group than in the non-action trained group.