Maria Laura Mencaroni

Combining psychophysics and Bayesian modelling, we characterized the computational principles by which the observer integrates multisensory signals during face-to-face communication. In the following, we first describe the Bayesian Causal Inference model from which we derive the Forced Fusion model as a special case (see [52] for further details). Second, we describe how action intention may affect multisensory processing.

Abstract Face-to-face communication relies on the seamless integration of multisensory signals, including voice, gaze, and head movements, to convey meaning effectively. This poses a fundamental computational challenge: optimally binding signals sharing the same communicative intention (e.g. looking at the addressee while speaking) and segregating unrelated signals (e.g. looking away while coughing), all within the rapid turn-taking dynamics of conversation.