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Data visualizations are powerful tools for communicating quantitative information. While prior work has focused on how experts design informative graphs, little is known about the intuitions non-experts have about what makes a graph effective for communicating a specific message. In the current study, we asked participants (N=398) which of eight graphs would be most useful for answering a particular question, where all graphs were generated from the same dataset but varied in how the data were arranged. We tested the degree to which participants based their decisions on sensitivity to how easily other participants (N=542) would be able to answer that question with that graph. Our results suggest that while people were biased towards graphs that were at least minimally informative (i.e., contained the relevant variables), their decisions did not necessarily reflect sensitivity to more graded but systematic variation in actual graph comprehensibility. 

The human ability to deceive others and detect deception has long been tied to theory of mind. We make a stronger argument: in order to be adept liars – to balance gain (i.e. maximizing their own reward) and plausibility (i.e. maintaining a realistic lie) – humans calibrate their lies under the assumption that their partner is a rational, utility-maximizing agent. We develop an adversarial recursive Bayesian model that aims to formalize the behaviors of liars and lie detectors. We compare this model to (1) a model that does not perform theory of mind computations and (2) a model that has perfect knowledge of the opponent’s behavior. To test these models, we introduce a novel dyadic, stochastic game, allowing for quantitative measures of lies and lie detection. In a second experiment, we vary the ground truth probability. We find that our rational models qualitatively predict human lying and lie detecting behavior better than the non-rational model. Our findings suggest that humans control for the extremeness of their lies in a manner reflective of rational social inference. These findings provide a new paradigm and formal framework for nuanced quantitative analysis of the role of rationality and theory of mind in lying and lie detecting behavior.