Faces and Voices Processing in Human and Primate Brains: Rhythmic and Multimodal Mechanisms Underlying the Evolution and Development of Speech

While influential works since the 1970s have widely assumed that imitation is an innate skill in both human and non-human primate neonates, recent empirical studies and meta-analyses have challenged this view, indicating other forms of reward-based learning as relevant factors in the development of social behavior. The visual input translation into matching motor output that underlies imitation abilities instead seems to develop along with social interactions and sensorimotor experience during infancy and childhood. Recently, a new visual stream has been identified in both human and non-human primate brains, updating the dual visual stream model. This third pathway is thought to be specialized for dynamics aspects of social perceptions such as eye-gaze, facial expression and crucially for audio-visual integration of speech. Here, we review empirical studies addressing an understudied but crucial aspect of speech and communication, namely the processing of visual orofacial cues (i.e., the perception of a speaker's lips and tongue movements) and its integration with vocal auditory cues. Along this review, we offer new insights from our understanding of speech as the product of evolution and development of a rhythmic and multimodal organization of sensorimotor brain networks, supporting volitional motor control of the upper vocal tract and audio-visual voices-faces integration.

Matching Unfamiliar Voices to Static and Dynamic Faces: No Evidence for a Dynamic Face Advantage in a Simultaneous Presentation Paradigm

Previous research has demonstrated that humans are able to match unfamiliar voices to corresponding faces and vice versa. It has been suggested that this matching ability might be based on common underlying factors that have a characteristic impact on both faces and voices. Some researchers have additionally assumed that dynamic facial information might be especially relevant to successfully match faces to voices. In the present study, static and dynamic face-voice matching ability was compared in a simultaneous presentation paradigm. Additionally, a procedure (matching additionally supported by incidental association learning) was implemented which allowed for reliably excluding participants that did not pay sufficient attention to the task. A comparison of performance between static and dynamic face-voice matching suggested a lack of substantial differences in matching ability, suggesting that dynamic (as opposed to mere static) facial information does not contribute meaningfully to face-voice matching performance. Importantly, this conclusion was not merely derived from the lack of a statistically significant group difference in matching performance (which could principally be explained by assuming low statistical power), but from a Bayesian analysis as well as from an analysis of the 95% confidence interval (CI) of the actual effect size. The extreme border of this CI suggested a maximally plausible dynamic face advantage of less than four percentage points, which was considered way too low to indicate any theoretically meaningful dynamic face advantage. Implications regarding the underlying mechanisms of face-voice matching are discussed.