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New VR devices, such as the Meta (Oculus) Quest, have expanded the possibilities for viewing movies. The purpose of this study is to compare the emotional and cognitive impacts of VR and traditional 2D movies. In this study, sixty volunteers were divided into two groups and presented a movie (Gala) in 2D or VR format. We employed a multimodal method to assess the cognitive and emotional effects of the film both during and after watching. Our technique combined self-reports, interviews, questionnaires, and objective heart rate and EEG brain activity data. After\u00a0quantitative and qualitative evaluation, it was discovered, that regardless of media, there was a substantial influence of the movie on the emotional state of the participant\u2019s mood. Moreover, compared to the traditional 2D-movie, the VR movie led to more consistent and robust positive effect on all aspects of self-rated affect. The difference in self-reported mood was corroborated by reduced EEG amplitudes in the beta frequency band, indicating higher levels of positive affectivity, which was only observed for the VR movie. Lastly, the VR movie also leads to overall higher self-rated immersion and engagement than the 2D version. Our results highlight the potential of VR movies to engage and emotionally affect audiences beyond traditional cinema. Moreover, our study highlights the value of using a multidisciplinary method for analysing audience impacts.<\/jats:p>","DOI":"10.1007\/s10055-023-00864-2","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T21:02:06Z","timestamp":1696280526000},"page":"3181-3190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Evaluating the viewer experience of interactive virtual reality movies"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1594-3779","authenticated-orcid":false,"given":"Rudy","family":"Carpio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Baumann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Birt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"issue":"02","key":"864_CR1","doi-asserted-by":"publisher","first-page":"52","DOI":"10.38094\/jastt20291","volume":"2","author":"SMSA Abdullah","year":"2021","unstructured":"Abdullah SMSA, Ameen SYA, Sadeeq MA, Zeebaree S (2021) Multimodal emotion recognition using deep learning. 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