Eye Movement Differences when Recognising and Learning Moving and Static Faces

Natalie Butcher, Rachel J. Bennetts, Laura Sexton, Andrei Barbanta, Karen Lander

Research output: Contribution to journalArticlepeer-review

5 Downloads (Pure)

Abstract

Seeing a face in motion can help subsequent face recognition. Several explanations have been proposed for this ‘motion advantage’, but other factors that might play a role have received less attention. For example, facial movement might enhance recognition by attracting attention to the internal facial features thereby facilitating identification. However, there is no direct evidence that motion increases attention to regions of the face that facilitate identification (i.e., internal features) compared to static faces. We tested this hypothesis by recording participants eye movements whilst they completed famous face recognition (Experiment 1, N = 32), and face-learning (Experiment 2, N = 60, Experiment 3, N = 68), tasks with presentation style manipulated (moving or static). Across all three experiments, a motion advantage was found, and participants directed a higher proportion of fixations to the internal features (i.e., eyes, nose, and mouth) of moving faces vs. static. Conversely, the proportion of fixations to the internal non-feature area (i.e., cheeks, forehead, chin), and external area (Experiment 3) was significantly reduced for moving compared to static faces (all ps < 0.05). Results suggest that during both familiar and unfamiliar face recognition, facial motion is associated with increased attention to internal facial features, but only during familiar face recognition is the magnitude of the motion advantage significantly related functionally to the proportion of fixations directed to the internal features.
Original languageEnglish
JournalQuarterly Journal of Experimental Psychology
Early online date14 May 2024
Publication statusE-pub ahead of print - 14 May 2024

Fingerprint

Dive into the research topics of 'Eye Movement Differences when Recognising and Learning Moving and Static Faces'. Together they form a unique fingerprint.

Cite this