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.

We introduce a type of isomorphism among strategic games that we call localisomorphism. Local isomorphisms is a weaker version of the notions of strongand weak game isomorphism introduced in [J. Gabarro, A. Garcia and M. Serna,Theor. Comput. Sci. 412 (2011) 6675–6695]. In a localisomorphism it is required to preserve, for any player, the player’s preferences on thesets of strategy profiles that differ only in the action selected by this player. We showthat the game isomorphism problem for local isomorphism is equivalent to the same problemfor strong or weak isomorphism for strategic games given in: general, extensive andformula general form. As a consequence of the results in [J. Gabarro, A. Garcia and M.Serna, Theor. Comput. Sci. 412 (2011) 6675–6695] thisimplies that local isomorphism problem for strategic games is equivalent to (a) thecircuit isomorphism problem for games given in general form, (b) the boolean formulaisomorphism problem for formula games in general form, and (c) the graph isomorphismproblem for games given in explicit form.