{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:00:46Z","timestamp":1772164846353,"version":"3.50.1"},"reference-count":126,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":280,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000275","name":"Leverhulme","doi-asserted-by":"publisher","award":["PLP-2018-152"],"award-info":[{"award-number":["PLP-2018-152"]}],"id":[{"id":"10.13039\/501100000275","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Observers\u2019 ability to estimate levels of movement synchrony, such as in Olympic diving or rowing, is highly variable and, in part, constrained by personality traits and enjoyment of the movements. Embodiment also appears to play a crucial role, whereby stronger beliefs that one\u2019s body can complete physical tasks predict more accurate estimation of synchrony levels. However, to demonstrate the relevance of embodiment per se, direct evidence that physical embodiment (i.e., bodily knowledge of specific movements) alters synchrony estimation is required. Here, we assessed the behavioural and cortical underpinnings of synchrony estimation in conditions with and without physical embodiment. Forty-three participants completed personality trait measures, then completed a synchrony estimation task. Participants copied upper-body movement sequences (acquiring physical embodiment), then viewed dyads performing the same sequence or an unknown sequence and estimated the level of synchrony, while we recorded cortical activation using functional near-infrared spectroscopy (fNIRS). Participants underestimated synchrony levels, showing greater underestimation for unknown movements than performed movements. For performed movements, but not unknown movements, the degree of estimation error was associated with activation of left inferior frontal gyrus and superior temporal gyrus, and right motor cortex and inferior parietal lobule. Participants\u2019 estimation error was associated with body competence and autistic trait scores, as well as measures of enjoyment and movement predictability (replicating previous work). This work demonstrates that physical embodiment improves lay observers\u2019 ability to discern levels of movement synchrony and has practical implications across a wide variety of artistic and athletic domains.<\/jats:p>","DOI":"10.1162\/imag.a.962","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T22:02:17Z","timestamp":1759960937000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":0,"title":["Individual abilities to estimate levels of movement synchrony predict action observation network activation"],"prefix":"10.1162","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0981-3353","authenticated-orcid":false,"given":"Ryssa","family":"Moffat","sequence":"first","affiliation":[{"name":"Social Brain Sciences Lab, ETH Zurich, Zurich, Switzerland"},{"name":"School of Psychological Sciences, Macquarie University, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1671-5698","authenticated-orcid":false,"given":"Emily S.","family":"Cross","sequence":"additional","affiliation":[{"name":"Social Brain Sciences Lab, ETH Zurich, Zurich, Switzerland"},{"name":"School of Psychological Sciences, Macquarie University, Sydney, Australia"},{"name":"MARCS Institute, Western Sydney University, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"2025102415561050200_IMAG.a.962-b200","doi-asserted-by":"crossref","DOI":"10.3389\/fninf.2014.00014","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","author":"Abraham","year":"2014","journal-title":"Frontiers in Neuroinformatics"},{"issue":"12","key":"2025102415561050200_IMAG.a.962-b1","doi-asserted-by":"crossref","first-page":"8355","DOI":"10.1111\/ejn.14866","article-title":"Mobile brain\/body Imaging in dance: A dynamic transdisciplinary field for applied research","volume":"54","author":"Barnstaple","year":"2021","journal-title":"European Journal of Neuroscience"},{"issue":"21","key":"2025102415561050200_IMAG.a.962-b2","doi-asserted-by":"crossref","first-page":"e2372242025","DOI":"10.1523\/JNEUROSCI.2372-24.2025","article-title":"EEG of the dancing brain: Decoding sensory, motor, and social processes during dyadic dance","volume":"45","author":"Bigand","year":"2025","journal-title":"The Journal of Neuroscience"},{"key":"2025102415561050200_IMAG.a.962-b3","author":"Borchers","year":"2022","journal-title":"pracma: Practical numerical math functions"},{"key":"2025102415561050200_IMAG.a.962-b4","article-title":"The OpenCV Library","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s Journal of Software Tools"},{"issue":"2","key":"2025102415561050200_IMAG.a.962-b5","doi-asserted-by":"crossref","first-page":"025005","DOI":"10.1117\/1.NPh.2.2.025005","article-title":"How short is short? Optimum source\u2013detector distance for short-separation channels in functional near-infrared spectroscopy","volume":"2","author":"Brigadoi","year":"2015","journal-title":"Neurophotonics"},{"key":"2025102415561050200_IMAG.a.962-b6","unstructured":"Broadwell, P., & Tangherlini, T. R. (2021). Comparative K-Pop choreography analysis through deep-learning pose estimation across a large video corpus. 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