Human motor learning involves cognitive strategies in addition to implicit adaptation. Differences in systems-level neurophysiology between strategy-based and implicit learning remain poorly understood. We asked how the P3 event-related potential, an electroencephalography signal known to increase during early motor learning, relates to strategy-based learning and implicit adaptation. We re-analysed data from two experiments, in which participants (n = 64) reached towards a visual target, with online visual feedback replacing vision of their moving hand. We induced learning by rotating the visual feedback. In the first experiment, feedback rotations were turned on during pairs of two consecutive trials, interspersed between non-rotated trials. In one condition, feedback was rotated relative to the actual movement, allowing participants to develop a re-aiming strategy on the second trial of each pair, while it was rotated relative to the target in the other condition, rendering re-aiming futile. P3 amplitude increased in the first rotated trial in both conditions, but this increase was more pronounced in the re-aiming condition. In the second experiment, a constant visuomotor rotation was turned on for many consecutive trials. We instructed one group beforehand how to re-aim successfully, while the other group had to develop a strategy by themselves. P3 amplitude increased during early adaptation only in the latter group. These findings collectively suggest that in the context of motor learning, the P3 ERP is associated with a need to develop, or adjust, a cognitive strategy.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/ejn.16476 | DOI Listing |
Cell Rep
January 2025
Institut Interdisciplinaire de Neurosciences (IINS), University Bordeaux, CNRS, IINS, UMR 5297, 33000 Bordeaux, France; Centre Broca Nouvelle-Aquitaine, 146, rue Léo-Saignat, 33076 Bordeaux, France. Electronic address:
Optimal decision-making depends on interconnected frontal brain regions, enabling animals to adapt decisions based on internal states, experiences, and contexts. The secondary motor cortex (M2) is key in adaptive behaviors in expert rodents, particularly in encoding decision values guiding complex probabilistic tasks. However, its role in deterministic tasks during initial learning remains uncertain.
View Article and Find Full Text PDFMaternal stress during pregnancy, or prenatal stress, is a risk factor for neurodevelopmental disorders in offspring, including autism spectrum disorder (ASD). In ASD, dorsal striatum displays abnormalities correlating with symptom severity, but there is a gap in knowledge about dorsal striatal cellular and molecular mechanisms that may contribute. Using a mouse model, we investigated how prenatal stress impacted striatal-dependent behavior in adult offspring.
View Article and Find Full Text PDFThe degeneration of midbrain dopamine (DA) neurons disrupts the neural control of natural behavior, such as walking, posture, and gait in Parkinson's disease. While some aspects of motor symptoms can be managed by dopamine replacement therapies, others respond poorly. Recent advancements in machine learning-based technologies offer opportunities for unbiased segmentation and quantification of natural behavior in both healthy and diseased states.
View Article and Find Full Text PDFPhysiol Behav
January 2025
Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. Electronic address:
C1q/TNF-related protein 14 (CTRP14), also known as C1q-like 1 (C1QL1), is a synaptic protein predominantly expressed in the brain. It plays a critical role in the formation and maintenance of the climbing fiber-Purkinje cell synapses, ensuring that only one single winning climbing fiber from the inferior olivary neuron synapses with the proximal dendrites of Purkinje cells during the early postnatal period. Loss of CTRP14/C1QL1 results in incomplete elimination of supernumerary climbing fibers, leading to multiple persistent climbing fibers synapsing with the Purkinje cells.
View Article and Find Full Text PDFFront Neuroinform
December 2024
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Introduction: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.
Methods: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!