{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:53:30Z","timestamp":1793321610246,"version":"4.5.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":73,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Turing AI Fellowship","award":["EP\/W002965\/1"],"award-info":[{"award-number":["EP\/W002965\/1"]}]},{"name":"Leverhulme Trust grant"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The growing prevalence of realistic AI-generated videos on media platforms increasingly blurs the line between fact and fiction, eroding public trust. Understanding how people watch AI-generated videos offers a human-centered perspective for improving AI detection and guiding advancements in video generation. However, existing studies have not investigated human gaze behavior in response to AI-generated videos of physical scenes. Here, we collect and analyze the eye movements from 40 participants during video understanding and AI detection tasks involving a mix of real-world and AI-generated videos. We find that given the high realism of AI-generated videos, gaze behavior is driven less by the video\u2019s\n                    <jats:italic>actual<\/jats:italic>\n                    authenticity and more by the viewer\u2019s\n                    <jats:italic>perception<\/jats:italic>\n                    of its authenticity. Our results demonstrate that the mere awareness of potential AI generation may alter media consumption from passive viewing into an active search for anomalies.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-55437-5","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T23:34:47Z","timestamp":1780702487000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Perceived authenticity drives gaze behavior when watching AI-generated videos of physical scenes"],"prefix":"10.1038","volume":"16","author":[{"given":"Danqing","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine M.","family":"Collins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangzhe","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayush","family":"Tewari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miri","family":"Zilka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"key":"55437_CR1","unstructured":"OpenAI: Sora is here. https:\/\/openai.com\/index\/sora-is-here\/. 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Towards accurate generative models of video: A new metric & challenges. arXiv preprint arXiv:1812.01717 (2018)."},{"key":"55437_CR8","unstructured":"Radford, A. et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning 8748\u20138763 (2021) (PmLR)."},{"key":"55437_CR9","doi-asserted-by":"publisher","first-page":"4449","DOI":"10.1109\/TIP.2021.3072221","volume":"30","author":"Z Tu","year":"2021","unstructured":"Tu, Z., Wang, Y., Birkbeck, N., Adsumilli, B. & Bovik, A. C. Ugc-vqa: Benchmarking blind video quality assessment for user generated content. IEEE Trans. Image Process. 30, 4449\u20134464 (2021).","journal-title":"IEEE Trans. Image Process."},{"key":"55437_CR10","doi-asserted-by":"crossref","unstructured":"Huang, Z., He, Y., Yu, J., Zhang, F., Si, C., Jiang, Y., Zhang, Y., Wu, T., Jin, Q., Chanpaisit, N., et al. Vbench: Comprehensive benchmark suite for video generative models. 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In Proceedings of the Annual Meeting of the Cognitive Science Society Vol. 42 (2020)."},{"key":"55437_CR17","unstructured":"Tanaka, Y., Inuzuka, M. & Hirayama, R. Utilizing eye-tracking to explain variation in response to inconsistent message onbelief change in false rumor. In Proceedings of the Annual Meeting of the Cognitive Science Society Vol. 41 (2019)."},{"key":"55437_CR18","doi-asserted-by":"crossref","unstructured":"Ilyas, C. M. A. Reading the readers mind through eye tracking: Can ai generated texts match human authors? In Proceedings of the 2025 Symposium on Eye Tracking Research and Applications 1\u20137 (2025).","DOI":"10.1145\/3715669.3726846"},{"key":"55437_CR19","doi-asserted-by":"crossref","unstructured":"Shi, D. et al. Crtypist: Simulating touchscreen typing behavior via computational rationality. 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