Prediction across sensory modalities: A neurocomputational model of the McGurk effect

Abstract

The McGurk effect is a textbook illustration of the automaticity with which the human brain integrates audio-visual speech. It shows that even incongruent audiovisual (AV) speech stimuli can be combined into percepts that correspond neither to the auditory nor to the visual input, but to a mix of both. Typically, when presented with, e.g., visual /aga/ and acoustic /aba/ we perceive an illusory /ada/. In the inverse situation, however, when acoustic /aga/ is paired with visual /aba/, we perceive a combination of both stimuli, i.e., /abga/ or /agba/. Here we assessed the role of dynamic cross-modal predictions in the outcome of AV speech integration using a computational model that processes continuous audiovisual speech sensory inputs in a predictive coding framework. The model involves three processing levels: sensory units, units that encode the dynamics of stimuli, and multimodal recognition/identity units. The model exhibits a dynamic prediction behavior because evidence about speech tokens can be asynchronous across sensory modality, allowing for updating the activity of the recognition units from one modality while sending top–down predictions to the other modality. We explored the model's response to congruent and incongruent AV stimuli and found that, in the two-dimensional feature space spanned by the speech second formant and lip aperture, fusion stimuli are located in the neighborhood of congruent /ada/, which therefore provides a valid match. Conversely, stimuli that lead to combination percepts do not have a unique valid neighbor. In that case, acoustic and visual cues are both highly salient and generate conflicting predictions in the other modality that cannot be fused, forcing the elaboration of a combinatorial solution. We propose that dynamic predictive mechanisms play a decisive role in the dichotomous perception of incongruent audiovisual inputs.