Optimally designing games for behavioural research

Anna N. Rafferty, Matei Zaharia, Thomas L. Griffiths

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Computer games can be motivating and engaging experiences that facilitate learning, leading to their increasing use in education and behavioural experiments. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. In this framework, we use Markov decision processes to model players' actions within a game, and then make inferences about the parameters of a cognitive model from these actions. Using a variety of concept learning games, we show that in practice, this method can predict which games will result in better estimates of the parameters of interest. The best games require only half as many players to attain the same level of precision.

Original languageEnglish (US)
Article number20130828
JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2167
StatePublished - Jul 8 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Engineering
  • General Physics and Astronomy
  • General Mathematics


  • Cognitive science
  • Computer games
  • Markov decision process
  • Optimal experiment design


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