A novel approach to studying strategic decisions with eye-tracking and machine learning
Michal Krol and
Judgment and Decision Making, 2017, vol. 12, issue 6, 596-609
We propose a novel method of using eye-tracking to study strategic decisions. The conventional approach is to hypothesize what eye-patterns should be observed if a given model of decision-making was accurate, and then proceed to verify if this occurs. When such hypothesis specification is difficult a priori, we propose instead to expose subjects to a variant of the original strategic task that should induce processing it in a way consistent with the postulated model. It is then possible to use machine learning pattern recognition techniques to check if the associated eye-patterns are similar to those recorded during the original task. We illustrate the method using simple examples of 2x2 matching-pennies and coordination games with or without feedback about the counterparts' past moves. We discuss the strengths and limitations of the method in this context.
Keywords: task recognition; eye-tracking; strategic games; machine learning (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:jdm:journl:v:12:y:2017:i:6:p:596-609
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