Modality-specific and modality-independent neural representations work in concert in predictive processes during sequence learning

Research output: Contribution to journalResearch articleContributedpeer-review


  • Teodóra Vékony - , Lyon Neuroscience Research Center (CRNL) (Author)
  • Ádám Takács - , Department of Child and Adolescent Psychiatry and Psychotherapy, Department of Child and Adolescent Psychiatry and Psychotherapy (Author)
  • Felipe Pedraza - , Lyon Neuroscience Research Center (CRNL), Université Lumière Lyon 2 (Author)
  • Frederic Haesebaert - , Lyon Neuroscience Research Center (CRNL) (Author)
  • Barbara Tillmann - , Lyon Neuroscience Research Center (CRNL), Université de Bourgogne (Author)
  • Imola Mihalecz - , Lyon Neuroscience Research Center (CRNL) (Author)
  • Romane Phelipon - , Lyon Neuroscience Research Center (CRNL) (Author)
  • Christian Beste - , Department of Child and Adolescent Psychiatry and Psychotherapy, Department of Child and Adolescent Psychiatry and Psychotherapy (Author)
  • Dezso Nemeth - , Lyon Neuroscience Research Center (CRNL), Eotvos Lorand University, Research Centre for Natural Sciences (Author)


Probabilistic sequence learning supports the development of skills and enables predictive processing. It remains contentious whether visuomotor sequence learning is driven by the representation of the visual sequence (perceptual coding) or by the representation of the response sequence (motor coding). Neurotypical adults performed a visuomotor sequence learning task. Learning occurred incidentally as it was evidenced by faster responses to high-probability than to low-probability targets. To uncover the neurophysiology of the learning process, we conducted both univariate analyses and multivariate pattern analyses (MVPAs) on the temporally decomposed EEG signal. Univariate analyses showed that sequence learning modulated the amplitudes of the motor code of the decomposed signal but not in the perceptual and perceptual-motor signals. However, MVPA revealed that all 3 codes of the decomposed EEG contribute to the neurophysiological representation of the learnt probabilities. Source localization revealed the involvement of a wider network of frontal and parietal activations that were distinctive across coding levels. These findings suggest that perceptual and motor coding both contribute to the learning of sequential regularities rather than to a neither–nor distinction. Moreover, modality-specific encoding worked in concert with modality-independent representations, which suggests that probabilistic sequence learning is nonunitary and encompasses a set of encoding principles.


Original languageEnglish
Pages (from-to)7783-7796
Number of pages14
JournalCerebral cortex
Issue number12
Publication statusPublished - 8 Jun 2023

External IDs

PubMed 36944531
ORCID /0000-0002-2989-9561/work/146788800
WOS 000950958200001



  • electroencephalography, multivariate pattern analysis, sequence learning, statistical learning, temporal decomposition, Multivariate pattern analysis, Statistical learning, Sequence learning, Electroencephalography, Temporal decomposition, Learning/physiology, Probability