Semantics-weighted Lexical Surprisal Modeling of Naturalistic Functional MRI Time-Series during Spoken Narrative Listening

Probabilistic language models are increasingly used to provide neural representations of linguistic features under naturalistic settings. Word surprisal models can be applied to continuous fMRI recordings during task-free listening of narratives, to detect regions linked to language prediction and comprehension. Here, to this purpose, a novel semantics-weighted lexical surprisal is applied to naturalistic fMRI data. FMRI was performed at 3 Tesla in 31 subjects during task-free listening to a 12-minute audiobook played in both original and word-reversed (control) version. Lexical-only and semantics-weighted lexical surprisal models were estimated for the original and control word series. The two series were alternatively chosen to build the predictor of interest in the first-level general linear model and were compared in the second-level (group) analysis. The addition of the surprisal predictor to the stimulus-related predictors significantly improved the fitting of the neural signal. In average, the semantics-weighted model yielded lower surprisal values and, in some areas, better fitting of the fMRI data compared to the lexical-only model. The two models produced both overlapping and distinct activations: while lexical-only surprisal activated secondary auditory areas in the superior temporal gyri and the cerebellum, semantics-weighted surprisal additionally activated the left inferior frontal gyrus. These results confirm the usefulness of surprisal models in the naturalistic fMRI analysis of linguistic processes and suggest that the use of semantic information may increase the sensitivity of a probabilistic language model in higher-order language-related areas, with possible implications for future naturalistic fMRI studies of language under normal and (clinically or pharmacologically) modified conditions.