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

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. Confirmatory and exploratory analyses reveal compositional representations in the left inferior frontal gyrus (BA45), even when the task did not require semantic access. These results suggest that BA45 represents combinatorial information automatically across task demands, and further characterize composition as the (symmetric) intersection of constituent features. Additionally, a cluster of compositional representations emerges in the left middle superior temporal sulcus, while semantic, but not compositional, representations are observed in the left angular gyrus. Overall, our work clarifies which brain regions represent semantic information compositionally across contexts and tasks, and qualifies which operations best describe composition.