{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:05:03Z","timestamp":1790229903206,"version":"4.1.0"},"reference-count":74,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:p>\n                    Earth\u2019s gravity fundamentally shapes human behavior. The brain encodes this force as an\n                    <jats:italic>internal model of gravity<\/jats:italic>\n                    , enabling the prediction and interpretation of gravitational effects during perception and action. Understanding how this model adapts to altered gravity is critical for predicting human performance in spaceflight. We present a computational framework for modelling neurophysiological adaptation across diverse gravitational environments. The framework has two components trained on open-access data from altered-gravity studies, particularly parabolic flights. The first component (CorticalG) employs a lightweight multilayer perceptron neural network to predict gravity-dependent changes in EEG frequency bands, estimating cortical state under different gravitational loads. The second component (PhysioG) uses independent Gaussian process models to capture broader physiological responses, including heart rate variability, electrodermal activity, and motor control. To complement the quantitative modelling, we simulated subjective experience across gravitational environments using the Large Language Model (LLM) Claude 3.5 Sonnet. Physiological outputs prompted the model to generate narratives describing alertness, bodily awareness, and cognitive state across zero gravity, partial gravity of the Moon and Mars, and hypergravity. This framework provides a novel approach for investigating human adaptation to spaceflight. It offers a predictive tool to assess performance and resilience, supporting the design of future space exploration missions.\n                  <\/jats:p>","DOI":"10.3389\/fpsyg.2026.1735872","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:44:52Z","timestamp":1790228692000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Gravity-awareness: a deep learning and LLM framework for predicting human adaptation to altered gravitational environments"],"prefix":"10.3389","volume":"17","author":[{"given":"Bakytzhan","family":"Alibekov","sequence":"first","affiliation":[{"name":"School of IT & Engineering, Kazakh-British Technical University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alina","family":"Gutoreva","sequence":"additional","affiliation":[{"name":"School of IT & Engineering, Kazakh-British Technical University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisa Raffaella","family":"Ferr\u00e8","sequence":"additional","affiliation":[{"name":"School of Psychological Sciences, Birkbeck University of London","place":["London, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref1","first-page":"337","author":"Aher","year":"2023"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e1010275","DOI":"10.1371\/journal.pcbi.1010275","article-title":"Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference","volume":"18","author":"Amin","year":"2022","journal-title":"PLoS Comput. 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