Proceedings of the Annual Meeting of the Cognitive Science Society

The human ability to deceive others and detect deception haslong been tied to theory of mind. We make a stronger argu-ment: in order to be adept liars – to balance gain (i.e. maxi-mizing their own reward) and plausibility (i.e. maintaining arealistic lie) – humans calibrate their lies under the assumptionthat their partner is a rational, utility-maximizing agent. Wedevelop an adversarial recursive Bayesian model that aims toformalize the behaviors of liars and lie detectors. We comparethis model to (1) a model that does not perform theory of mindcomputations and (2) a model that has perfect knowledge ofthe opponent’s behavior. To test these models, we introduce anovel dyadic, stochastic game, allowing for quantitative mea-sures of lies and lie detection. In a second experiment, we varythe ground truth probability. We find that our rational modelsqualitatively predict human lying and lie detecting behaviorbetter than the non-rational model. Our findings suggest thathumans control for the extremeness of their lies in a mannerreflective of rational social inference. These findings provide anew paradigm and formal framework for nuanced quantitativeanalysis of the role of rationality and theory of mind in lyingand lie detecting behavior.