{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T06:18:25Z","timestamp":1783923505331,"version":"3.55.0"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Secretar\u00eda de Ciencia, Humanidades, Tecnolog\u00eda e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["CF-2023-G-107"],"award-info":[{"award-number":["CF-2023-G-107"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neural Circuits"],"abstract":"<jats:p>\n                    Visuomotor interception requires predicting a moving target\u2019s future state to compensate for sensorimotor delays. Predictive control has been studied mainly under constant-speed motion. How observers adapt when the temporal structure of target speed (\n                    <jats:italic>v<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>T<\/jats:italic>\n                    <\/jats:sub>\n                    ) varies remains unclear. We examined interception across\n                    <jats:italic>v<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>T<\/jats:italic>\n                    <\/jats:sub>\n                    profiles within the same individuals: constant, linear-ramp, and nonlinear (sinusoidal and semicircular). Participants intercepted a moving target while eye and hand movements were recorded; on a subset of trials, visual feedback was removed after an observation period to force reliance on internal estimates. Behavior stayed strongly dependent on\n                    <jats:italic>v<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>T<\/jats:italic>\n                    <\/jats:sub>\n                    even under transient visual loss, but this dependence was constrained by how\n                    <jats:italic>v<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>T<\/jats:italic>\n                    <\/jats:sub>\n                    evolved over time. Removing feedback increased spatial error and gaze\u2013target distance, with modest effects at constant and linear\n                    <jats:italic>v<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>T<\/jats:italic>\n                    <\/jats:sub>\n                    and larger errors at nonlinear\n                    <jats:italic>v<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>T<\/jats:italic>\n                    <\/jats:sub>\n                    . Speed matching stayed stable, indicating that local velocity estimates are preserved but insufficient to prevent cumulative spatial error under more complex dynamics. Oculomotor measures were more sensitive than manual control, suggesting partially dissociable effectors. A within-cycle matched-velocity test on the linear-ramp data revealed acceleration\u2013deceleration differences at every velocity bin and metric; the representation is velocity-dominated but not velocity-only, and it exploits higher-order kinematic information at least partially. Exploratory pupillometry showed stable pupil size during full visibility and a reduction late in masked trials, with outcome-dependent modulation independent of gaze position at target disappearance. Predictive interception thus relies on velocity-dominated representations that preserve sensitivity to higher-order kinematic information, enabling robust performance across simple motion profiles but imposing limits when target dynamics become nonlinear.\n                  <\/jats:p>","DOI":"10.3389\/fncir.2026.1848603","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T06:01:41Z","timestamp":1783922501000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive interception across speed profiles"],"prefix":"10.3389","volume":"20","author":[{"given":"Inmaculada","family":"M\u00e1rquez","sequence":"first","affiliation":[{"name":"Laboratorio De Plasticidad Cortical y Aprendizaje Perceptual, Instituto De Neurociencias, Universidad De Guadalajara","place":["Jalisco, Mexico"]},{"name":"Departamento De Psicolog\u00eda, Centro Universitario De La Ci\u00e9nega, Universidad De GuadaLajara","place":["Ocotl\u00e1n, Mexico"]},{"name":"Laboratorio De Neurofisiolog\u00eda, Departamento De Bioingenier\u00eda Traslacional, Centro Universitario De Ciencias Exactas e Ingenier\u00edas","place":["Jalisco, Mexico"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Trevi\u00f1o","sequence":"additional","affiliation":[{"name":"Laboratorio De Plasticidad Cortical y Aprendizaje Perceptual, Instituto De Neurociencias, Universidad De Guadalajara","place":["Jalisco, Mexico"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e47502","DOI":"10.1371\/journal.pone.0047502","article-title":"Smooth pursuit and visual occlusion: Active inference and oculomotor control in schizophrenia.","volume":"7","author":"Adams","year":"2012","journal-title":"PLoS One"},{"key":"B2","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1152\/jn.00110.2023","article-title":"Sensory prediction error drives subconscious motor learning outside of the laboratory.","volume":"130","author":"Albert","year":"2023","journal-title":"J. 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