Is recursive belief inference the engine of mentalizing?

Editor's Note: These short, critical reviews of recent papers in the Journal, written exclusively by graduate students or postdoctoral fellows, are intended to summarize the important findings of the paper and provide additional insight and commentary. For more information on the format and purpose of the Journal Club, please see Review of Yoshida et al. Game theorists often quote the story of Sherlock Holmes fleeing London by train in direction of Dover, and applying the following recursive reasoning: if he were to continue the journey without giving thought to his pursuers (order-one strategy), then surely Moriarty would find out and follow him to Dover. Knowing his arch-enemy well, Holmes might account for this in a second-order inference and alight at Canterbury to avoid pursuit. However, Moriarty might himself operate on the second-order of reasoning and expect Holmes to do just that, in which case the latter should progress to the third level of recursion and go to Dover. Based on his past experience with Moriarty, Holmes was able to estimate his order of sophistication , k, and optimally respond to it using a k ϩ 1 order strategy (otherwise, he could continue the mind game ad infini-tum without reaching a decision). In search of the neural underpinnings of such belief-inference processes, Yoshida et al. (2010) chose an experimental task suitably underlining its potential evolutionary role, namely the conventional stag-hunt game, wherein two players independently decide to hunt a stag or a hare. If both choose the former (to cooperate), they succeed and both receive high rewards, otherwise any hare-hunter gets a low payoff and anyone in lone pursuit of the stag ends up with nothing. Although standard game theory struggles to predict the game's outcome, the authors apply a computational model demonstrating how the theory of mind (ability to infer internal states of others) facilitates coordinating on the cooperative stag-hunting solution. To this end, subjects were asked to play a dynamic variant of the game in which they and a computer-controlled counterpart took turns to navigate a two-dimensional grid in pursuit of a chosen prey. This task demanded recursive inference of the counterpart's intentions, based on the history of its actions. The order of comput-er's strategy was varied throughout the task. An agent of type k ϭ 1 assumes that the counterpart will move randomly, and hence pursues a hare— unless both players are not far (say, at most two …