Learning of action through adaptive combination of motor primitives. Thoroughman, K. & Shadmehr, R Nature, 407(6805):742-7, 2000. doi abstract bibtex Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects' adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.
@Article{Thoroughman2000,
author = {KA Thoroughman and R Shadmehr},
journal = {Nature},
title = {Learning of action through adaptive combination of motor primitives.},
year = {2000},
number = {6805},
pages = {742-7},
volume = {407},
abstract = {Understanding how the brain constructs movements remains a fundamental
challenge in neuroscience. The brain may control complex movements
through flexible combination of motor primitives, where each primitive
is an element of computation in the sensorimotor map that transforms
desired limb trajectories into motor commands. Theoretical studies
have shown that a system's ability to learn action depends on the
shape of its primitives. Using a time-series analysis of error patterns,
here we show that humans learn the dynamics of reaching movements
through a flexible combination of primitives that have gaussian-like
tuning functions encoding hand velocity. The wide tuning of the inferred
primitives predicts limitations on the brain's ability to represent
viscous dynamics. We find close agreement between the predicted limitations
and the subjects' adaptation to new force fields. The mathematical
properties of the derived primitives resemble the tuning curves of
Purkinje cells in the cerebellum. The activity of these cells may
encode primitives that underlie the learning of dynamics.},
doi = {10.1038/35037588},
keywords = {Computing Methodologies, Human, Language, Learning, Mental Processes, Models, Theoretical, Stochastic Processes, Support, U.S. Gov't, Non-P.H.S., Cognition, Linguistics, Neural Networks (Computer), Practice (Psychology), Non-U.S. Gov't, Memory, Psychological, Task Performance and Analysis, Time Factors, Visual Perception, Adult, Attention, Discrimination Learning, Female, Male, Short-Term, Mental Recall, Orientation, Pattern Recognition, Visual, Perceptual Masking, Reading, Concept Formation, Form Perception, Animals, Corpus Striatum, Shrews, P.H.S., Visual Cortex, Visual Pathways, Acoustic Stimulation, Auditory Cortex, Auditory Perception, Cochlea, Ear, Gerbillinae, Glycine, Hearing, Neurons, Space Perception, Strychnine, Adolescent, Decision Making, Reaction Time, Astrocytoma, Brain Mapping, Brain Neoplasms, Cerebral Cortex, Electric Stimulation, Electrophysiology, Epilepsy, Temporal Lobe, Evoked Potentials, Frontal Lobe, Noise, Parietal Lobe, Scalp, Child, Language Development, Psycholinguistics, Brain, Perception, Speech, Vocalization, Animal, Discrimination (Psychology), Hippocampus, Rats, Calcium, Chelating Agents, Excitatory Postsynaptic Potentials, Glutamic Acid, Guanosine Diphosphate, In Vitro, Neuronal Plasticity, Pyramidal Cells, Receptors, AMPA, Metabotropic Glutamate, N-Methyl-D-Aspartate, Somatosensory Cortex, Synapses, Synaptic Transmission, Thionucleotides, Action Potentials, Calcium Channels, L-Type, Electric Conductivity, Entorhinal Cortex, Neurological, Long-Evans, Infant, Mathematics, Statistics, Probability Learning, Problem Solving, Psychophysics, Association Learning, Child Psychology, Habituation (Psychophysiology), Probability Theory, Analysis of Variance, Semantics, Symbolism, Behavior, Eye Movements, Macaca mulatta, Prefrontal Cortex, Cats, Dogs, Haplorhini, Photic Stimulation, Electroencephalography, Nervous System Physiology, Darkness, Grasshoppers, Light, Membrane Potentials, Neural Inhibition, Afferent, Picrotoxin, Vision, Deoxyglucose, Injections, Microspheres, Neural Pathways, Rhodamines, Choice Behavior, Speech Perception, Verbal Learning, Dominance, Cerebral, Fixation, Ocular, Language Tests, Random Allocation, Comparative Study, Saguinus, Sound Spectrography, Species Specificity, Audiometry, Auditory Threshold, Calibration, Data Interpretation, Statistical, Anesthesia, General, Electrodes, Implanted, Pitch Perception, Sound Localization, Paired-Associate Learning, Serial Learning, Auditory, Age Factors, Motion Perception, Brain Injuries, Computer Simulation, Blindness, Psychomotor Performance, Color Perception, Signal Detection (Psychology), Judgment, ROC Curve, Regression Analysis, Music, Probability, Arm, Cerebrovascular Disorders, Hemiplegia, Movement, Muscle, Skeletal, Myoclonus, Robotics, Magnetoencephalography, Phonetics, Software, Speech Production Measurement, Epilepsies, Partial, Laterality, Stereotaxic Techniques, Germany, Speech Acoustics, Verbal Behavior, Child Development, Instinct, Brain Stem, Coma, Diagnosis, Differential, Hearing Disorders, Hearing Loss, Central, Neuroma, Acoustic, Dendrites, Down-Regulation, Patch-Clamp Techniques, Wistar, Up-Regulation, Aged, Aphasia, Middle Aged, Cones (Retina), Primates, Retina, Retinal Ganglion Cells, Tympanic Membrane, Cell Communication, Extremities, Biological, Motor Activity, Rana catesbeiana, Spinal Cord, Central Nervous System, Motion, Motor Cortex, Intelligence, Macaca fascicularis, Adoption, Critical Period (Psychology), France, Korea, Magnetic Resonance Imaging, Multilingualism, Auditory Pathways, Cochlear Nerve, Loudness Perception, Neural Conduction, Sensory Thresholds, Sound, Language Disorders, Preschool, Generalization (Psychology), Vocabulary, Biophysics, Nerve Net, Potassium Channels, Sodium Channels, Cues, Differential Threshold, Arousal, Newborn, Sucking Behavior, Ferrets, Microelectrodes, Gestalt Theory, Mathematical Computing, Perceptual Closure, Vestibulocochlear Nerve, Brain Damage, Chronic, Regional Blood Flow, Thinking, Tomography, Emission-Computed, Case-Control Studies, Multivariate Analysis, Artificial Intelligence, Depth Perception, 11048700},
}
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Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects' adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. 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The brain may control complex movements\n\tthrough flexible combination of motor primitives, where each primitive\n\tis an element of computation in the sensorimotor map that transforms\n\tdesired limb trajectories into motor commands. Theoretical studies\n\thave shown that a system's ability to learn action depends on the\n\tshape of its primitives. Using a time-series analysis of error patterns,\n\there we show that humans learn the dynamics of reaching movements\n\tthrough a flexible combination of primitives that have gaussian-like\n\ttuning functions encoding hand velocity. The wide tuning of the inferred\n\tprimitives predicts limitations on the brain's ability to represent\n\tviscous dynamics. We find close agreement between the predicted limitations\n\tand the subjects' adaptation to new force fields. The mathematical\n\tproperties of the derived primitives resemble the tuning curves of\n\tPurkinje cells in the cerebellum. The activity of these cells may\n\tencode primitives that underlie the learning of dynamics.},\n doi = {10.1038/35037588},\n keywords = {Computing Methodologies, Human, Language, Learning, Mental Processes, Models, Theoretical, Stochastic Processes, Support, U.S. Gov't, Non-P.H.S., Cognition, Linguistics, Neural Networks (Computer), Practice (Psychology), Non-U.S. Gov't, Memory, Psychological, Task Performance and Analysis, Time Factors, Visual Perception, Adult, Attention, Discrimination Learning, Female, Male, Short-Term, Mental Recall, Orientation, Pattern Recognition, Visual, Perceptual Masking, Reading, Concept Formation, Form Perception, Animals, Corpus Striatum, Shrews, P.H.S., Visual Cortex, Visual Pathways, Acoustic Stimulation, Auditory Cortex, Auditory Perception, Cochlea, Ear, Gerbillinae, Glycine, Hearing, Neurons, Space Perception, Strychnine, Adolescent, Decision Making, Reaction Time, Astrocytoma, Brain Mapping, Brain Neoplasms, Cerebral Cortex, Electric Stimulation, 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