Small errors may affect the process of learning in a fundamentally different way than large errors. For example, adapting reaching movements in response to a small perturbation produces generalization patterns that are different from large perturbations. Are distinct neural mechanisms engaged in response to large versus small errors? Here, we examined the motor learning process in patients with severe degeneration of the cerebellum. Consistent with earlier reports, we found that the patients were profoundly impaired in adapting their motor commands during reaching movements in response to large, sudden perturbations. However, when the same magnitude perturbation was imposed gradually over many trials, the patients showed marked improvements, uncovering a latent ability to learn from errors. On sudden removal of the perturbation, the patients exhibited aftereffects that persisted much longer than did those in healthy controls. That is, despite cerebellar damage, the brain maintained the ability to learn from small errors and the motor memory that resulted from this learning was strongly resistant to change. Of note was the fact that on completion of learning, the motor output of the cerebellar patients remained distinct from healthy controls in terms of its temporal characteristics. Therefore cerebellar degeneration impaired the ability to learn from large-magnitude errors, but had a lesser impact on learning from small errors. The neural basis of motor learning in response to small and large errors appears to be distinct.
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http://dx.doi.org/10.1152/jn.00822.2009 | DOI Listing |
Sci Rep
January 2025
Department of Psychology, Faculty of Psychology and Sport Science, Justus Liebig University, Otto-Behaghel-Str. 10F, 35394, Gießen, Germany.
Adapting movements to rapidly changing conditions is fundamental for interacting with our dynamic environment. This adaptability relies on internal models that predict and evaluate sensory outcomes to adjust motor commands. Even infants anticipate object properties for efficient grasping, suggesting the use of internal models.
View Article and Find Full Text PDFAm J Speech Lang Pathol
January 2025
School of Communication Sciences & Disorders, Elborn College, Western University, London, Ontario Canada.
Purpose: Cerebral palsy (CP) is the most prevalent motor disability affecting children. Many children with CP have significant speech difficulties and require augmentative and alternative communication (AAC) to participate in communication. Despite demonstrable benefits, the use of AAC systems among children with CP remains constrained, although research in Canada is lacking.
View Article and Find Full Text PDFChild Neuropsychol
January 2025
Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland.
Executive function (EF) impairments are prevalent in survivors of neonatal critical illness such as children born very preterm (VPT) or with complex congenital heart disease (cCHD). This paper aimed to describe EF profiles in school-aged children born VPT or with cCHD and in typically developing peers, to identify child-specific and family-environmental factors associated with these profiles and to explore links to everyday-life outcomes. Data from eight EF tests assessing working memory, inhibition, cognitive flexibility, switching, and planning in = 529 children aged between 7 and 16 years was subjected into a latent profile analysis.
View Article and Find Full Text PDFActa Paediatr
January 2025
Paediatric Neurology and Neurorehabilitation Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Aim: Young people with childhood-onset motor disabilities face unique challenges in understanding and managing their condition. This study explored how they learnt about their condition.
Method: A descriptive qualitative study was conducted in 2023-2024 at a Swiss paediatric neurorehabilitation unit.
Health Inf Sci Syst
December 2025
Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
This study introduces a novel classification method to distinguish children with autism from typically developing children. We recruited 50 school-age children in Taiwan, including 44 boys and 6 girls aged 6 to 12 years, and asked them to draw patterns from a visual-motor integration test to collect data and train deep learning classification models. Ensemble learning was adopted to significantly improve the classification accuracy to 0.
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