There are conflicting theories about how people reason through cause and effect. A key distinction between two prominent accounts pertains to whether, in judging an event’s causal relevance, people preferentially consider what actually happened (as predicted by process theories) or whether they also consider what could have happened under different conditions (as predicted by counterfactual theories). Toward adjudicating between these theories, the current work used eye tracking and Gaussian Process modeling to investigate how people form causal judgments retrospectively and in the absence of ongoing visual input. Participants played a virtual ball-shooting game: after choosing to move left or right, they encoded a video of the actual outcome and then were prompted to mentally simulate either (a) what actually happened, (b) what could have happened, or (c) what caused the outcome to happen while looking at a blank screen. During causal judgment, we found evidence that participants visually mentally simulated counterfactual possibilities: they moved their eyes in similar patterns as when they imagined a counterfactual alternative. Altogether, these results favor counterfactual theories of causal reasoning, demonstrate how visual mental simulation can support this reasoning, and provide a novel methodological approach for using eye movements to investigate causal reasoning and counterfactual thinking more broadly.

The human capacity for causal judgment has long been thought to depend on an ability to consider counterfactual alternatives: the lightning strike caused the forest fire because had it not struck, the forest fire would not have ensued. To accommodate psychological effects on causal judgment, a range of recent accounts of causal judgment have proposed that people probabilistically sample counterfactual alternatives from which they compute a graded index of causal strength. While such models have had success in describing the influence of probability on causal judgments, among other effects, we show that these models make further untested predictions: probability should also influence people's metacognitive confidence in their causal judgments. In a large (N=3020) sample of participants in a causal judgment task, we found evidence that normality indeed influences people's confidence in their causal judgments and that these influences were predicted by a counterfactual sampling model. We take this result as supporting evidence for existing Bayesian accounts of causal judgment.

Imagine that a river becomes polluted if two plants generate too much waste. One might be more inclined to say that a plant caused the river to become polluted when it produced more waste than expected. While similar normality effects on causal judgments have been observed in cases with binary variables, little work has focused on cases with continuous variables. To test whether the statistical normality of continuous variables influences causal judgments, we had participants learn statistical norms over repeated iterations of a vignette and make a causal judgment about an instance of that vignette. Following Icard et al. (2017), we manipulated the causal structure and the normality of each cause. By testing whether normality effects on causal judgment generalize to cases with continuous variables, our results help determine whether these effects are central to human cognition, or simply apply to a subset of cases studied thus far.