Use training regimens with unrepeated, variable situations (like action video games) to foster transferable improvements in probabilistic inference¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 1 study (1 causal), q3 · 1 of 1 report an effect size · 2 claims rest on one study

Description¶

The article recommends, on the basis of its training study and mechanism account, that training regimens likely to produce broad transfer are those in which "situations are rarely, if ever, repeated", forcing learners to acquire "how to rapidly and accurately learn the statistics on the fly and how to accumulate this evidence more efficiently". Action video games exemplify this regimen, and 50 hours of such training produced measurable gains in evidence use in non-gamers.

Design Implications¶

Context¶

Requirements¶

• Training situations must vary and rarely repeat, so no task-specific solution can be learned

Constraints¶

• The neural mechanisms of the wide transfer remain speculative, per the authors

Target Learners¶

• young adult non-gamers undergoing training

Target Learning Goals¶

• transferable improvement in probabilistic inference and evidence accumulation

Affordances¶

• Improved Probabilistic Inference Transfer Mechanism

Claims¶

• Fifty hours of action video-game training causally improves probabilistic inference in non-gamers, with better use of evidence only in the action-trained group [+W]
• The improved probabilistic inference of VGPs generalizes to a novel auditory tone-location discrimination task [+W]

Related Strategies¶

Examples¶

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Key Sources¶

• Green, C.S., Pouget, A., & Bavelier, D. (2010). Improved probabilistic inference as a general learning mechanism with action video games. Current Biology. https://doi.org/10.1016/j.cub.2010.07.040