Experience and abstract reasoning in learning backward induction

Daniel R. Hawes, Alexander Vostroknutov, Aldo Rustichini

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Backward induction is a benchmark of game theoretic rationality, yet surprisingly little is known as to how humans discover and initially learn to apply this abstract solution concept in experimental settings. We use behavioral and functional magnetic resonance imaging (fMRI) data to study the way in which subjects playing in a sequential game of perfect information learn the optimal backward induction strategy for the game. Experimental data from our two studies support two main findings: First, subjects converge to a common process of recursive inference similar to the backward induction procedure for solving the game. The process is recursive because earlier insights and conclusions are used as inputs in later steps of the inference. This process is matched by a similar pattern in brain activation, which also proceeds backward, following the prediction error: brain activity initially codes the responses to losses in final positions; in later trials this activity shifts to the starting position. Second, the learning process is not exclusively cognitive, but instead combines experience-based learning and abstract reasoning. Critical experiences leading to the adoption of an improved solution strategy appear to be stimulated by brain activity in the reward system. This indicates that the negative affect induced by initial failures facilitates the switch to a different method of solving the problem. Abstract reasoning is combined with this response, and is expressed by activation in the ventrolateral prefrontal cortex. Differences in brain activation match differences in performance between subjects who show different learning speeds.

Original languageEnglish (US)
Article numberArticle 23
JournalFrontiers in Neuroscience
Issue numberFEB
StatePublished - 2012


  • Backward induction
  • Deductive reasoning
  • Game theory
  • Learning
  • Neuroeconomics


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