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.