Causal reasoning is a core facet of our cognitive abilities. However, the time-course of causal reasoning is not studied to its fullest. The duration of reasoning, including those that yield a reasoning error, might prove crucial in understanding the cognitive processes underlying causal reasoning. In two experiments we asked participants to make probabilistic causal inferences while manipulating external time pressure and measuring response times. We found that participants are less accurate under time pressure, which can be interpreted as a speed-accuracy-tradeoff. In addition, we found that conservative responding increased under time pressure. Surprisingly, we found that two other persistent reasoning errors - Markov violations and failures to explain away - appear insensitive to the time pressure we employed. These observations seemed related to the confidence that participants expressed in their causal judgment: Conservatism was associated with low confidence, whereas Markov violations and failures to explain were not. These findings shed doubt on existing explanations of causal reasoning that predict an association between time pressure and all reasoning errors. Our findings suggest that these errors should not be attributed to a single cognitive mechanism and emphasize the need for an understanding of causal judgements as the result of multiple processes.

Causal cognition is a core aspect of how we deal with the world; however, existing psychological theories tend not to target intuitive causal engagement that is done in daily life. To fill this gap, we propose an Ecological-Enactive (E-E) affordance-based account of situated causal engagement, that is, causal judgments and perceptions. We develop this account to improve our understanding of this way of dealing with the world, which includes making progress on the causal selection problem, and to extend the scope of embodied cognitive science to causal cognition. We characterize identifying causes as selectively attending to the relevant ecological information to engage with relevant affordances, where these affordances are dependent on individual abilities. Based on this we construe causal engagement as based on a learned skill. Moreover, we argue that to understand judgments of causation as we make them in our daily lives, we need to see them as situated in sociocultural practices. Practices are about doing, and so this view helps us understand why people make these judgments so ubiquitously: to get things done, to provide an effective path to intervening in the world. Ultimately this view on causal engagement allows us to account for individual differences in causal perceptions, judgments, and selections by appealing to differences in learned skills and sociocultural practices.

One consistent finding in the causal reasoning literature is that causal judgments are rather variable. In particular, distributions of probabilistic causal judgments tend not to be normal and are often not centered on the normative response. As an explanation for these response distributions, we propose that people engage in ‘mutation sampling’ when confronted with a causal query and integrate this information with prior information about that query. The Mutation Sampler model (Davis & Rehder, 2020) posits that we approximate probabilities using a sampling process, explaining the average responses of participants on a wide variety of tasks. Careful analysis, however, shows that its predicted response distributions do not match empirical distributions. We develop the Bayesian Mutation Sampler (BMS) which extends the original model by incorporating the use of generic prior distributions. We fit the BMS to experimental data and find that, in addition to average responses, the BMS explains multiple distributional phenomena including the moderate conservatism of the bulk of responses, the lack of extreme responses, and spikes of responses at 50%.