Compositionality in the semantic network: a model-driven representational similarity analysis.

ABSTRACT: Semantic composition allows us to construct complex meanings (e.g., "dog house", "house dog") from simpler constituents ("dog", "house"). Neuroimaging studies have often relied on high-level contrasts (e.g., meaningful > non-meaningful phrases) to identify brain regions sensitive to composition. However, such an approach is less apt at addressing how composition is carried out, namely what functions best characterize constituents integration. Here, we rely on simple computational models to explicitly characterize alternative compositional operations, and use representational similarity analysis to compare models to target regions of interest. We re-analyze fMRI data aggregated from four published studies (N = 85), all employing two-word combinations but differing in task requirements. Conf

SUBMITTER: Ciapparelli M 

PROVIDER: S-EPMC12421894 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature