{"ok":true,"nota":{"id":"d0af6201e1ebcac6","slug":"un-modelo-de-computadora-predice-que-tan-rapido-decide-una-persona-si--d0af62","titulo":"Un modelo de computadora predice qué tan rápido decide una persona si ve movimiento hacia un lado o hacia el o","resumen":"Científicos crearon un modelo que combina la anatomía del cerebro de cada participante con su actividad eléctrica. Lo probaron con 17 personas que tenían que decidir la dirección de un movimiento en pantalla.","fuente":"biorxiv","fuenteNombre":"bioRxiv · preprints","color":"#be2628","autor":"Wang, C., Yang, G., Wang, S., Luo, H.","url":"https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1?rss=1","imagen":"/hoy/portada/d0af6201e1ebcac6","tema":"ciencia","subtemas":["modelos computacionales","toma de decisiones","eeg","neurociencia","tiempo de reacción"],"entidades":["bioRxiv"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-22T00:00:00.000Z","indexTs":"2026-10-22T16:38:44.280Z","hash":"8f3ed3db4cf3a964cfffa135418fdd776d58bc3b20a0f2c072dad76a3654cc6a","tsa":{"time":"Oct 22 16:39:43 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"motor","registro":"hallazgo","porQueImporta":"El modelo logró reproducir tanto las señales cerebrales como los tiempos de reacción de cada persona, algo que hasta ahora se hacía por separado. Esto abre la posibilidad de explicar por qué unas personas deciden más rápido que otras a partir de cómo está armado su cerebro. Es un avance de laboratorio con pocas personas, así que todavía está lejos de usarse en la vida diaria o en diagnósticos.","quienFirma":"Universidad de Ginebra","confianza":"preprint","consulta":{"pregunta":"¿Te parece que entender el cerebro con modelos de computadora vale la pena?"}},"sello":{"hash":"8f3ed3db4cf3a964cfffa135418fdd776d58bc3b20a0f2c072dad76a3654cc6a","sello":"d493a8bea3819bafd6b2c68604fc31d15e99a87b04ebcb3742cb2bb6d0c78d4c","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6575204,"aleatorio":"ac0f4a6ce509202540c9641abb3a70f7f06eeb36f68a34e1bc3e430369a775da","tiempo":"2026-10-22T16:39:00.000Z"},"nist":{"cadena":2,"pulso":1994801,"salida":"123765DCC46130497358D1E6393BF76FC82D205E2F260DEF171C4887B733C1209F31A193DDD877AB50C2E9F79D0A125A26712AD900F817275AC0E40E5C1C39BC","tiempo":"2026-10-22T16:38:00.000Z"},"noAntes":"2026-10-22T16:39:00.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 22 16:39:43 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"A brain-behavior generative model links individual brain dynamics to perceptual decisions","descripcion":"Perceptual decision speed varies across individuals and trials. Explaining this variability requires linking individual brain anatomy, neural dynamics, and behavior, yet whole-brain models typically reproduce brain signals without behavior, whereas behavioral models lack anatomical constraints. Here we introduce a brain-behavior generative model in which whole-brain dynamics on each participant's structural connectome generate both single-trial electroencephalography (EEG) and reaction times. Fitted to 17 participants performing motion discrimination, the models reproduced individual event-related potentials and reaction-time distributions. Joint fitting enabled the models to predict whether","pubDate":"2026-10-22","url":"https://www.biorxiv.org/content/10.64898/2026.10.16.759120v1?rss=1"},"indexado":"2026-10-22T16:38:44.280Z"}}