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In this study, we used an fMRI\u2010compatible trackball with naturalistic stimuli to record dynamic ratings of perceived risk and speed and investigated the degree to which different brain regions were able to decode these. We presented participants with first\u2010person perspective videos of cars racing on the same course. These videos varied in terms of subjectively perceived speed and risk profiles, as determined during a behavioral pilot. During the fMRI experiment, participants used the trackball to dynamically rate subjective risk in a first and speed in a second session and assessed overall risk and speed after watching each video. A standard multivariate correlation analysis based on these ratings revealed sparse decodability in visual areas only for the risk ratings. In contrast, the dynamic rating\u2010based correlation analysis uncovered frontal, visual, and temporal region activation for subjective risk and dorsal visual stream and temporal region activation for subjectively perceived speed. Interestingly, further analyses showed that the brain regions for decoding risk changed over time, whereas those for decoding speed remained constant. Overall, our results demonstrate the advantages of time\u2010resolved decoding to help our understanding of the dynamic networks associated with decoding risk and speed perception in realistic driving scenarios.<\/jats:p>","DOI":"10.1002\/hbm.26652","type":"journal-article","created":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T02:46:09Z","timestamp":1710557169000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Decoding the dynamic perception of risk and speed using naturalistic stimuli: A multivariate, whole\u2010brain analysis"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9391-3938","authenticated-orcid":false,"given":"Uijong","family":"Ju","sequence":"first","affiliation":[{"name":"Department of Information Display Kyung Hee University  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2604-9115","authenticated-orcid":false,"given":"Christian","family":"Wallraven","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Engineering Korea University  South Korea"},{"name":"Department of Artificial Intelligence Korea University  South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2020.10.029"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24592-8_12"},{"key":"e_1_2_10_4_1","unstructured":"Beel J. &Langer S.(2015).A comparison of offline evaluations online evaluations and user studies in the context of research\u2010paper recommender systems. 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