Our remarkable ability to infer complex cause-effect relationships is thought to distinguish humans from all other species. Despite that causal inferencing pervades human cognition, it remains unclear whether this fundamental cognitive ability is supported by a unified, domain-general mechanism or multiple domain-specific mechanisms. Both the language and logical reasoning systems have been described as possible unified substrates of causal inferencing. The current study uses neuroimaging to offer insight into this debate. We specifically focus on the culturally universal and highly motivationally relevant case of inferring illness causes. Participants read causal and noncausal vignettes about illness and mechanical failure while undergoing fMRI. We find that inferring the causes of illness selectively activates the brain's ‘animacy network,' particularly the precuneus. By contrast, a domain-general (i.e., ‘content-invariant') preference for causal inferencing did not emerge, including in the language and logical reasoning networks. Together, this evidence suggests that domain-specific mechanisms enable causal inferencing.

Going beyond the literal meaning of language is key to communicative success. However, the mechanisms that support non-literal inferences remain debated. Using a novel meta-analytic approach, we evaluate the contribution of linguistic, social-cognitive, and executive mechanisms to non-literal interpretation. We identified 74 fMRI experiments (n = 1,430 participants) from 2001 to 2021 that contrasted non-literal language comprehension with a literal control condition, spanning ten phenomena (e.g., metaphor, irony, indirect speech). Applying the activation likelihood estimation approach to the 825 activation peaks yielded six left-lateralized clusters. We then evaluated the locations of both the individual-study peaks and the clusters against probabilistic functional atlases (cf. anatomical locations, as is typically done) for three candidate brain networks-the language-selective network (Fedorenko, Behr, & Kanwisher, 2011), which supports language processing, the Theory of Mind (ToM) network (Saxe & Kanwisher, 2003), which supports social inferences, and the domain-general Multiple-Demand (MD) network (Duncan, 2010), which supports executive control. These atlases were created by overlaying individual activation maps of participants who performed robust and extensively validated localizer tasks that selectively target each network in question (n = 806 for language; n = 198 for ToM; n = 691 for MD). We found that both the individual-study peaks and the ALE clusters fell primarily within the language network and the ToM network. These results suggest that non-literal processing is supported by both i) mechanisms that process literal linguistic meaning, and ii) mechanisms that support general social inference. They thus undermine a strong divide between literal and non-literal aspects of language and challenge the claim that non-literal processing requires additional executive resources.