Non-literal language processing is jointly supported by the language and theory of mind networks: Evidence from a novel meta-analytic fMRI approach

Miriam Hauptman; Idan Blank; Evelina Fedorenko

doi:10.1016/j.cortex.2023.01.013

Non-literal language processing is jointly supported by the language and theory of mind networks: Evidence from a novel meta-analytic fMRI approach

Cortex. 2023 May:162:96-114. doi: 10.1016/j.cortex.2023.01.013. Epub 2023 Mar 9.

Authors

Miriam Hauptman¹, Idan Blank², Evelina Fedorenko³

Affiliations

¹ Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA. Electronic address: mhauptm1@jhu.edu.
² Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Psychology, UCLA, Los Angeles, CA 90095, USA; Department of Linguistics, UCLA, Los Angeles, CA 90095, USA.
³ Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Program in Speech and Hearing in Bioscience and Technology, Harvard University, Boston, MA 02114, USA. Electronic address: evelina9@mit.edu.

Abstract

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.

Keywords: Language network; Lateralization; Meta-analysis; Non-literal language; Probabilistic atlas; Theory of mind; fMRI.

Publication types

Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Brain / diagnostic imaging
Brain / physiology
Brain Mapping / methods
Comprehension / physiology
Humans
Language
Magnetic Resonance Imaging*
Theory of Mind* / physiology

Abstract

Publication types

MeSH terms

Grants and funding