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Listening to music activates representations of movement and social agents. Why? We ask whether high-level causal reasoning about how music was generated can lead people to link musical sounds with animate agents. To test this, we asked whether people (N=60) make flexible inferences about whether an agent caused musical sounds, integrating information from the sounds’ timing and from the visual context in which it was produced. Using a 2x2 within-subject design, we found evidence of causal reasoning: In a context where producing a musical sequence would require self-propelled movement, people inferred that an agent had been present causing the sounds. When the context provided an alternative possible explanation, this ‘explained away’ the agent, reducing the tendency to infer an agent was present for the same acoustic stimuli. People can use causal reasoning to infer whether an agent produced musical sounds, suggesting that high-level cognition can link music with social concepts. 

Artifacts – the objects we own, make, and choose – provide a source of rich social information. Adults use people’s artifacts to judge others’ traits, interests, and social affiliations. Here we show that 4-year-old children (N=32) infer others’ shared interests from their artifacts. When asked who had the same interests as a target character, children chose the character with a conceptually similar object to the target’s – an object used for the same activity – over a character with a perceptually similar object. When asked which person had the same arbitrary property (bedtime, birthday, or middle name), children did not systematically select either character, and most often reported that they did not know. Adults (N=32) made similar inferences, but differed in their tendency to use artifacts to infer friendships. Overall, by age 4, children show a sophisticated ability to make selective, warranted inferences about others’ interests based solely on their artifacts. 

Do children use objects to infer the people and actions that created them? We ask how children judge whether designs were socially transmitted (copied), asking if children use a simple perceptual heuristic (more similar = more likely copied), or make a rational, flexible inference (Bayesian inverse planning). We found evidence that children use inverse planning to reason about artifacts’ designs: When children saw two identical designs, they did not always infer copying occurred. Instead, similarity was weaker evidence of copying when an alternative explanation ‘explained away’ the similarity. Thus, children inferred copying had occurred less often when designs were efficient (Exp1, age 7-9; N=52), and when there was a constraint that limited the number of possible designs (Exp2, age 4-5; N=160). When thinking about artifacts, young children go beyond perceptual features and use a process like inverse planning to reason about the generative processes involved in design. 

How do people use human-made objects (artifacts) to learn about the people and actions that created them? We test the richness of people’s reasoning in this domain, focusing on the task of judging whether social transmission has occurred (i.e. whether one person copied another). We develop a formal model of this reasoning process as a form of rational inverse planning, which predicts that rather than solely focusing on artifacts’ similarity to judge whether copying occurred, people should also take into account availability constraints (the materials available), and functional constraints (which materials work). Using an artifact-building task where two characters build tools to solve a puzzle box, we find that this inverse planning model predicts trial-by-trial judgments, whereas simpler models that do not consider availability or functional constraints do not. This suggests people use a process like inverse planning to make flexible inferences from artifacts’ features about the source of design ideas. 

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.