Automatic Change Detection of Human Attractiveness: Comparing Visual and Auditory Perception
Year: 2025
Type: article
Abstract: BACKGROUND/OBJECTIVES: Change detection of social cues across individuals plays an important role in human interaction. METHODS: Here we investigated the automatic change detection of facial and vocal attractiveness in 19 female participants by recording event-related potentials (ERPs). We adopted a 'deviant-standard-reverse' oddball paradigm where high- or low-attractive items were embedded as deviants in a sequence of opposite attractive standard stimuli. RESULTS: Both high- and low-attractive faces and voices elicited mismatch negativities (MMNs). Furthermore, low-attractive versus high-attractive items induced larger mismatch negativities in the voice condition but larger P3 amplitudes in the face condition. CONCLUSIONS: These data indicate that attractiveness can be automatically detected but that differences exist between facial and vocal attractiveness processing. Generally, change detection seems to work better for unattractive than attractive information, possibly in line with a negativity bias.
Source: Brain Sciences
Authors: Meng Liu, Jin Gao, Werner Sommer, Weijun Li
Institutions: Liaoning Normal University, Humboldt-Universität zu Berlin
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,654
Awards: G7206525070, G7670041312
OpenAlex ID: W4416298673
