To hold someone responsible, we need to assess what causal role their action played in bringing about the outcome. Causality, in turn, can be understood in terms of difference-making -- C caused E if E would have been different had C been different. In previous work, the counterfactual of “C being different” is often construed as “C being absent”. Here, we explore how in social situations, this counterfactual can be alternatively construed by imagining how a different person would have acted in the same situation. We propose a computational model that formalizes this idea of counterfactual replacement. The model compares what actually happened with what would have happened had the person of interest been replaced by someone else, and then predicts responsibility to the extent that the replacement person would likely have changed the outcome. We test our model against people’s responsibility judgments in a variety of scenarios across several experiments.

What shape do people's mental models take? We hypothesize that people build causal models that are suited to the task at hand. These models abstract away information to represent what matters. To test this idea empirically, we presented participants with causal learning paradigms where some features were outcome-relevant and others weren't. In Experiment 1, participants had to learn what objects of different shape and color made a machine turn on. In Experiment 2, they had to predict whether blocks sliding down ramps would cross a finish line. In both experiments, participants made systematic errors in a surprise test that asked them to recall what they had seen earlier. The errors people made suggest that they had built mental models of the task that privileged causally relevant information. Our results contribute to recent efforts trying to characterize the important role that causal abstraction plays in human learning and inference.

When did something almost happen? In this paper, we in-vestigate what brings counterfactual worlds close. In Exper-iments 1 and 2, we find that participants’ judgments aboutwhether something almost happened are determined by thecausal proximity of the alternative outcome. Something almosthappened, when a small perturbation to the relevant causalevent would have been sufficient to bring it about. In contrastto previous work that has argued that prior expectations areneglected when judging the closeness of counterfactual worlds(Kahneman & Varey, 1990), we show in Experiment 3 thatparticipants are more likely to say something almost happenedwhen they did not expect it. Both prior expectations and causaldistance influence judgments of “almost”. In Experiment 4, weshow how both causal proximity and beliefs about what wouldhave happened in the absence of the cause jointly explain judg-ments of “almost caused” and “almost prevented”.

Children often need to rely on other peopleÕs testimony to figure out what happened. One challenge in learning from others is that people can disagree in their interpretations of what happened. The present research examined how children ages 7 to 11 use disagreement to make inferences about past social events. Specifically, when there is no reason to question the reliability of either informant, can children use disagreement to infer that an ambiguous social event occurred? Experiment 1 (N = 52) found that children are indeed more likely to infer that an ambiguous social event occurred after learning that people disagreed (versus agreed) about what happened and that this ability strengthens with age. Experiment 2 (n = 43; data collection ongoing) examines how children make this inference. We predict that childrenÕs ability to reason about what would cause disagreement is critical to make inferences from disagreement about what happened.

When you see a glass fall off the table, you can predict it will break without seeing it hit the ground. Similarly, if you hear the glass shatter, you can infer what happened without seeing anything at all. Our knowledge of the causal mechanisms structuring our world allows us to make impressively accurate inferences based on incomplete information spread across multiple sensory modalities. In this work, we study the cognitive processing that supports this remarkable behavior. We utilize the Plinko domain, an intuitive physics setup where marbles are dropped into a box from one of three holes, colliding with obstacles as they fall to the ground. Participants judge where they think the ball fell from based on visual and auditory evidence. We track participants' eye-gaze to gain deeper insight into their mental processes, and develop models that characterize the computational processes underlying participant behavior.

This work attempts to bridge the divide between accounts of causal reasoning with respect to agents and objects. We begin by examining the influence of animacy. In a collision-based context, we vary the animacy status of an object using 3D animations. By holding the fine-grained kinematics of the actual and counterfactual outcomes fixed across animate and inanimate conditions, we find that animacy itself has no effect on causal attribution judgments. Next, we test if causal judgments for animate and inanimate objects differ as a function of the counterfactuals they respectively afford in a disjunctive causal structure. Here, we find that the effect of perceived animacy on causal attribution is mediated by differences in counterfactual judgments. Finally, we introduce the known effect of prescriptive norm violations to this paradigm. Our results collectively highlight how normative expectations specify the counterfactual considerations that guide causal reasoning about both agents and objects.