People can use the placement of objects to infer communicative goals.

Beyond words and gestures, people have a remarkable capacity to communicate indirectly through everyday objects: A hat on a chair can mean it is occupied, rope hanging across an entrance can mean we should not cross, and objects placed in a closed box can imply they are not ours to take. How do people generate and interpret the communicative meaning of objects? We hypothesized that this capacity is supported by social goal inference, where observers recover what social goal explains an object being placed in a particular location. To test this idea, we study a category of common ad-hoc communicative objects where a small cost is used to signal avoidance. Using computational modeling, we first show that goal inference from indirect physical evidence can give rise to the ability to use object placement to communicate. We then show that people from the U.S. and the Tsimane'-a farming-foraging group native to the Bolivian Amazon-can infer the communicative meaning of object placement in the absence of a pre-existing convention, and that people's inferences are quantitatively predicted by our model. Finally, we show evidence that people can store and retrieve this meaning for use in subsequent encounters, revealing a potential mechanism for how ad-hoc communicative objects become quickly conventionalized. Our model helps shed light on how humans use their ability to interpret other people's behavior to embed social meaning into the physical world.

Social inferences from physical evidence via bayesian event reconstruction.

Humans can make remarkable social inferences by watching each other's behavior. In many cases, however, people can also make social inferences about agents whose behavior they cannot see, based only on the physical evidence left behind. We hypothesized that this capacity is supported by a form of mental event reconstruction. Under this account, observers derive social inferences by reconstructing the agent's behavior, based on the physical evidence that revealed their presence. We present a computational model of this idea, embedded in a Bayesian framework for action understanding, and show that its predictions match human inferences with high quantitative accuracy. Specifically, Experiment 1 shows that people can infer where an agent came from and which goal they pursued in a room, all from a small pile of cookie crumbs. Experiment 2 shows that people can explicitly reconstruct the actions that the agent took, and these reconstructed trajectories can predict the entry point and goal inferences from Experiment 1. Finally, Experiment 3 shows that people can also infer whether one or two agents were in a room based on the position of two piles of cookie crumbs. Our results shed light on how people extract social information from the physical world. (PsycInfo Database Record (c) 2022 APA, all rights reserved).