4 research outputs found
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Learning from Agentic Actions: Modelling Causal Inference from Intention
People have the fascinating ability to infer causality by observing other humans’ actions. We modelled this process using a Bayesian rational agent model and showed how people can reason about another agent’s beliefs and, by extension, infer the world’s causal structure. We compared the model’s predictions against humans’ causal judgements on a novel inference task. Participants (N = 171) were shown a dynamic scene depicting either a human agent, robot agent, or both agents acting on two objects sequentially before observing an outcome. After observing the human (vs the less intentional robot) agent, people were more likely to infer that both objects (in sequential order) caused the outcome. When two agents of different intentionality were shown, people favored the object that the intentional agent interacted with as the cause of the outcome. Our model captured these inference patterns well and revealed insights into reasoning about semi-intentional agents and multi-agent contexts
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Modeling Causal Inference from Emotional Displays
Can people learn causal relationships about the world from someone’s emotions? We present a computational model integrating observational causal learning with emotional information, which uses emotional displays to disambiguate the beliefs, desires, and knowledge of other agents, in turn allowing causal inferences about the world. We compared our model predictions to human causal judgements on two observational learning tasks involving multiple possible causes or multiple possible outcomes. Across three studies (N = 129,127,125), emotional displays (compared to actions alone) led people to interpret agents’ beliefs differently, which in some contexts resulted in different causal inferences. Our model closely reflected these patterns of belief and causal inference and revealed new insights on how people learn causal relationships from others’ emotions