Loneliness, personality, and attention to AI-generated images depicting social threat

An eye-tracking study

Journal Article (2025)
Author(s)

Jenna Pfeifer (TU Delft - Medical Instruments & Bio-Inspired Technology)

Joost C.F. Winter (TU Delft - Human-Robot Interaction)

Dimitra Dodou (TU Delft - Medical Instruments & Bio-Inspired Technology)

Y. B. Eisma (TU Delft - Human-Robot Interaction)

Research Group
Medical Instruments & Bio-Inspired Technology
DOI related publication
https://doi.org/10.1016/j.paid.2025.113415
More Info
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Publication Year
2025
Language
English
Research Group
Medical Instruments & Bio-Inspired Technology
Volume number
247
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Abstract

Attention bias towards social threat has been linked to loneliness and anxiety, though findings are mixed and concerns about measurement reliability persist. This study examined whether state and trait loneliness, along with personality, self-esteem, social anxiety, and life satisfaction, are associated with attention bias towards social threat images (indicating rejection or exclusion) in young adults (N = 241). AI-generated images were used to enhance control over stimulus content and category distinctions. Participants completed an eye-tracking free-viewing task comprising 40 image matrices (four images per matrix, displayed for 6000 ms). We then computed attention bias (dwell time percentage, total fixation duration percentage, and fixation count percentage) and initial orientation of attention (first fixation percentage). The attention bias measures showed adequate-to-good internal consistency (α = 0.61–0.86). No significant associations emerged between loneliness and attention to socially threatening stimuli, suggesting that heightened vigilance to social threat may not be a feature of loneliness in non-clinical young adults. However, it was found that females exhibited greater attention to social positive images, and baseline pupil diameter was associated with social anxiety. Future research should assess whether loneliness-specific attention bias is a replicable phenomenon, ideally by using an extreme-sampling approach with very lonely individuals.