Matching novel face and voice identity using static and dynamic facial images

Research suggests that both static and dynamic faces share identity information with voices. However, face-voice matching studies offer contradictory results. Accurate face-voice matching is consistently above chance when facial stimuli are dynamic, but not when facial stimuli are static. This thesis aims to account for previous inconsistencies, comparing accuracy across a variety of two-alternative forced-choice (2AFC) procedures to isolate the features that support accuracy. In addition, the thesis provides a clearer and more complete picture of face-voice matching ability than that available in the existing literature. Samedifferent procedures are used to address original research questions relating to response bias and the delay between face and voice presentation. The overall findings indicate that faces and voices offer concordant source identity information. When faces and voices are presented close together in time, matching accuracy is consistently above chance level using both dynamic and static facial stimuli. Previous contradictory findings across studies can be accounted for by procedural differences and the characteristics of specific stimulus sets. Multilevel modelling analyses show that some people look and sound more similar than others. The results also indicate that when there is only a short (~1 second) interval between faces and voices, people exhibit a bias to assume that they belong to the same person. The findings presented in this thesis have theoretical and applied relevance. They highlight the value of considering person perception from a multimodal point of view, and are consistent with evidence for the existence of early perceptual integrative mechanisms between face and voice processing pathways. The results also offer insights into how people successfully navigate complex social situations featuring a number of novel speakers.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Nottingham Trent University

Year dc:date.issued

2016

Author dc:creator

• Smith, HMJ

Language dc:language

en