Toward a Science of Learning Games

Howard-Jones PA
Demetriou S
Yoo JH
Leonards U

Scientific Abstract

Reinforcement learning involves a tight coupling of reward-associated behavior and a type of learning that is very different to that promoted by education. However, the emerging understanding of its underlying processes may help derive principles for effective learning games that have, until now, been elusive. This article first reviews findings from cognitive neuroscience and psychology to provide insight into the motivating role of uncertain reward in games, including educational games. Then, a short experiment is reported to illustrate the potential of reward-based neurocomputational models of behavior in the understanding and development of effective learning games. In this study, a reward-based model of behavior is shown to predict recall of newly learnt information during a simple learning game.

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Toward a Science of Learning Games

Howard-Jones PA
Demetriou S
Yoo JH
Leonards U

Scientific Abstract

Reinforcement learning involves a tight coupling of reward-associated behavior and a type of learning that is very different to that promoted by education. However, the emerging understanding of its underlying processes may help derive principles for effective learning games that have, until now, been elusive. This article first reviews findings from cognitive neuroscience and psychology to provide insight into the motivating role of uncertain reward in games, including educational games. Then, a short experiment is reported to illustrate the potential of reward-based neurocomputational models of behavior in the understanding and development of effective learning games. In this study, a reward-based model of behavior is shown to predict recall of newly learnt information during a simple learning game.

Citation

2011. Brain, Mind and Education, 5(1): 33–41.

Similar content

Preprint
Liebana Garcia S, Laffere A, Toschi C, Schilling L, Podlaski J, Fritsche M, Zatka-Haas P, Li Y, Bogacz R, Saxe A, Lak A

Striatal dopamine reflects individual long-term learning trajectories

Preprint
Pinchetti L, Qi C, Lokshyn O, Oliviers G, Emde C, Tang M, M'Charrak A, Frieder S, Menzat B, Bogacz R, Lukasiewicz T, Salvatori T

Benchmarking Predictive Coding Networks -- Made Simple