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Previous studies have shown that face-voice matching accuracy is more consistently above chance for dynamic (i.e. speaking) faces than for static faces. This suggests that dynamic information can play an important role in informing matching decisions. We initially asked whether this advantage for dynamic stimuli is due to shared information across modalities that is encoded in articulatory mouth movements. Participants completed a sequential face-voice matching task with (1) static images of faces, (2) dynamic videos of faces, (3) dynamic videos where only the mouth was visible, and (4) dynamic videos where the mouth was occluded, in a well-controlled stimulus set. Surprisingly, after accounting for random variation in the data due to design choices, accuracy for all four conditions was at chance. Crucially, however, exploratory analyses revealed that participants were not responding randomly, with different patterns of response biases being apparent for different conditions. Our findings suggest that face-voice identity matching may not be possible with above-chance accuracy but that analyses of response biases can shed light upon how people attempt face-voice matching. We discuss these findings with reference to the differential functional roles for faces and voices recently proposed for multimodal person perception.

Explaining face-voice matching decisions: The contribution of mouth movements, stimulus effects and response biases