Imagers and mentalizers: capturing individual variation in metaphor interpretation via intersubject representational dissimilarity

When girls are pearls, does it mean that they are beautiful or that they are pleasant? Not only are metaphors open to different interpretations but also these interpretations might vary across individuals, even with the same cultural context. However, the literature lacks a description of which patterns of interpretation emerge across individuals and which factors might drive them. Here, we investigated the role of multimodality, intended as the contribution of different dimensions of experience-based information, to explain individual variability in metaphor interpretation. We analyzed participants’ interpretations in a metaphor verbalization task according to a series of semantic features of words (affective, cognitive, and sensory) that mirror different cognitive mechanisms. With an innovative method that combines i) Natural Language Processing (NLP), ii) a multivariate statistical technique that derives Intersubject Representational Dissimilarity Matrixes (IS-RDMs), and iii) a data-driven clustering method, we were able to identify two groups of participants. One cluster, which we named mentalizers, exhibited greater use of cognitive and affective terms (e.g. the girls-pearls metaphors was explained as indicating that girls are pleasant), while the other cluster, which we named imagers, capitalized more on words expressing sensory-based features (e.g., girls were described as beautiful). Our study showed that a data-driven approach can capture different metaphor interpretative profiles from word-level semantic features and that differences are driven by the sensorimotor vs. sociocognitive dimensions. This suggest that there are multiple alternative routes to derive metaphorical meaning, involving different modality systems in the multimodal network for metaphor.