Humans' ability to adapt and learn relies on reflecting on past performance. These experiences form latent representations called internal states that induce movement variability that improves how we interact with our environment. Our study uncovered temporal dynamics and neural substrates of two states from ten subjects implanted with intracranial depth electrodes while they performed a goal-directed motor task with physical perturbations. We identified two internal states using state-space models: one tracking past errors and the other past perturbations. These states influenced reaction times and speed errors, revealing how subjects strategize from trial history. Using local field potentials from over 100 brain regions, we found large-scale brain networks such as the dorsal attention and default mode network modulate visuospatial attention based on recent performance and environmental feedback. Notably, these networks were more prominent in higher-performing subjects, emphasizing their role in improving motor performance by regulating movement variability through internal states.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Breault, Macauley Smith ; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA. breault@mit.edu ; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. breault@mit.edu
Sacré, Pierre ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Robotique intelligente
Fitzgerald, Zachary B; Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
Gale, John T; DIXI Neurolab, Inc., Oxford, MI, USA
Cullen, Kathleen E ; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
González-Martínez, Jorge A; Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
Sarma, Sridevi V; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
Language :
English
Title :
Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks.
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