Supplementary data for the paper: 'Detecting Midjourney-generated images: An eye-tracking study'

DOI:10.4121/96b5eb59-b9a4-4094-8c47-04c14d57af1a.v1
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DOI: 10.4121/96b5eb59-b9a4-4094-8c47-04c14d57af1a

Datacite citation style

de Winter, Joost; Pfeifer, Jenna; Dodou, Dimitra; Eisma, Yke Bauke (2025): Supplementary data for the paper: 'Detecting Midjourney-generated images: An eye-tracking study'. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/96b5eb59-b9a4-4094-8c47-04c14d57af1a.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This study investigated human performance in identifying AI-generated images. In a speeded forced-choice task, 255 participants viewed paired images (one real, one AI-generated by Midjourney) of standard or futuristic cars and buildings and had to identify the AI-generated one, while eye movements were recorded using an eye-tracker. Results revealed a powerful ‘futurism-as-artificiality’ heuristic. Specifically, participants performed poorly (55% correct) when an AI-generated standard image was paired with a real futuristic image Conversely, accuracy was high (91% correct) when the AI-generated futuristic image was paired with a real standard image. Participants' gaze landed first on the AI-generated image more often when it depicted a futuristic design than when it depicted a standard one. The demonstrated heuristic presents a double-edged sword for information veracity: it may lead to the uncritical acceptance of AI-generated misinformation that appears conventional, while simultaneously causing real forward-thinking designs to be dismissed as fake.

History

  • 2025-07-14 first online, published, posted

Publisher

4TU.ResearchData

Format

readme/txt; scripts/m; data/mat; data/xlsx; images/png; video/mp4; supplementary methods/.pdf; experiment_paradigm/SR Research Experiment Builder files

Organizations

TU Delft, Faculty of Mechanical Engineering, Department of Cognitive Robotics

DATA

Files (3)