ArticleeLife2019
Brain signatures of a multiscale process of sequence learning in humans.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
42 citing papers in PubMed, 120 citations in OpenAlex.
- Compressive learning scaffolds higher-order network structure to enhance human knowledge acquisition.Nature communications · 2026Article
- Cortical deviance detection represents a canonical difference signal.bioRxiv : the preprint server for biology · 2026Article
- The Effect of Previously Encountered Sensory Information on Neural Representations of Predictability: Evidence From Human EEG.The European journal of neuroscience · 2025Article
- The Somatosensory Mismatch Negativity.The European journal of neuroscience · 2025Review
- The Mismatch Negativity Compared: EEG, SQUID-MEG, and NovelHuman brain mapping · 2025Article
- Oscillatory brain indexes of auditory-induced predictions about contingences during vocalizations.Experimental brain research · 2025Article
- Brain complexity represents uncertainty in sequence learning and corroborates habituation deficit in Parkinson disease patients.Scientific reports · 2025Article
- Dogs as a model to study the emergence of concept manipulation skills for language-readiness.Biologia futura · 2025Review
- Direct brain recordings reveal implicit encoding of structure in random auditory streams.Scientific reports · 2025Article
- Beta oscillations predict the envelope sharpness in a rhythmic beat sequence.Scientific reports · 2025Article
- Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia.Clinical EEG and neuroscience · 2025Review
- Parallel mechanisms signal a hierarchy of sequence structure violations in the auditory cortex.eLife · 2024Article
- Predictive learning shapes the representational geometry of the human brain.Nature communications · 2024Article
- Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome.Human brain mapping · 2024Article
- Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Fast adaptation to rule switching using neuronal surprise.PLoS computational biology · 2024Article
- Multiple Concurrent Predictions Inform Prediction Error in the Human Auditory Pathway.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- A multimodal cortical network of sensory expectation violation revealed by fMRI.Human brain mapping · 2023Article
- Article
- Probabilistic integration of preceding responses explains response bias in perceptual decision making.iScience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Extracting the temporal structure of sequences of events is crucial for perception, decision-making, and language processing. Here, we investigate the mechanisms by which the brain acquires knowledge of sequences and the possibility that successive brain responses reflect the progressive extraction of sequence statistics at different timescales. We measured brain activity using magnetoencephalography in humans exposed to auditory sequences with various statistical regularities, and we modeled this activity as theoretical surprise levels using several learning models. Successive brain waves related to different types of statistical inferences. Early post-stimulus brain waves denoted a sensitivity to a simple statistic, the frequency of items estimated over a long timescale (habituation). Mid-latency and late brain waves conformed qualitatively and quantitatively to the computational properties of a more complex inference: the learning of recent transition probabilities. Our findings thus support the existence of multiple computational systems for sequence processing involving statistical inferences at multiple scales.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.