![]() ![]() ![]() 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. We found that both the individual-study peaks and the ALE clusters fell primarily within the language network and the ToM network. 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). anatomical locations, as is typically done) for three candidate brain networks-the language-selective network ( Fedorenko et al., 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. We then evaluated the locations of both the individual-study peaks and the clusters against probabilistic functional atlases (cf. Applying the activation likelihood estimation approach to the 825 activation peaks yielded six left-lateralized clusters. We identified 74 fMRI experiments (n=1,430 participants) from 2001-2021 that contrasted non-literal language comprehension with a literal control condition, spanning ten phenomena (e.g., metaphor, irony, indirect speech). Using a novel meta-analytic approach, we evaluate the contribution of linguistic, social-cognitive, and executive mechanisms to non-literal interpretation. However, the mechanisms that support non-literal inferences remain debated. Going beyond the literal meaning of language is key to communicative success. ![]()
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