Evidence map›Paper›PMID 26441602›Full record

ArticleFrontiers in human neuroscience2015

Implicit learning of predictable sound sequences modulates human brain responses at different levels of the auditory hierarchy.

Françoise Lecaignard, Olivier Bertrand, Gérard Gimenez, Jérémie Mattout, Anne Caclin

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
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  7. Neurocomputational Underpinnings of Expected Surprise.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
    Article
  8. Article
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  12. Statistical Learning and Inference Is Impaired in the Nonclinical Continuum of Psychosis.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020
    Article
  13. Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020
    Article
  14. Brain dynamics for confidence-weighted learning.PLoS computational biology · 2020
    Article
  15. Article
  16. Article
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  19. Article
  20. Mismatch negativity to pitch pattern deviants in schizophrenia.The European journal of neuroscience · 2017
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Françoise LecaignardLyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS, UMR5292, Brain Dynamics and Cognition Team Lyon, France ; University Lyon 1 Lyon, France ; MEG Department, CERMEP Imaging Center Lyon, France.
Olivier BertrandLyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS, UMR5292, Brain Dynamics and Cognition Team Lyon, France ; University Lyon 1 Lyon, France.
Gérard GimenezMEG Department, CERMEP Imaging Center Lyon, France.
Jérémie MattoutLyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS, UMR5292, Brain Dynamics and Cognition Team Lyon, France ; University Lyon 1 Lyon, France.
Anne CaclinLyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS, UMR5292, Brain Dynamics and Cognition Team Lyon, France ; University Lyon 1 Lyon, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deviant stimuli, violating regularities in a sensory environment, elicit the mismatch negativity (MMN), largely described in the Event-Related Potential literature. While it is widely accepted that the MMN reflects more than basic change detection, a comprehensive description of mental processes modulating this response is still lacking. Within the framework of predictive coding, deviance processing is part of an inference process where prediction errors (the mismatch between incoming sensations and predictions established through experience) are minimized. In this view, the MMN is a measure of prediction error, which yields specific expectations regarding its modulations by various experimental factors. In particular, it predicts that the MMN should decrease as the occurrence of a deviance becomes more predictable. We conducted a passive oddball EEG study and manipulated the predictability of sound sequences by means of different temporal structures. Importantly, our design allows comparing mismatch responses elicited by predictable and unpredictable violations of a simple repetition rule and therefore departs from previous studies that investigate violations of different time-scale regularities. We observed a decrease of the MMN with predictability and interestingly, a similar effect at earlier latencies, within 70 ms after deviance onset. Following these pre-attentive responses, a reduced P3a was measured in the case of predictable deviants. We conclude that early and late deviance responses reflect prediction errors, triggering belief updating within the auditory hierarchy. Beside, in this passive study, such perceptual inference appears to be modulated by higher-level implicit learning of sequence statistical structures. Our findings argue for a hierarchical model of auditory processing where predictive coding enables implicit extraction of environmental regularities.

Indexed as

auditory regularityearly deviance responseEEGmismatch negativityP3apredictive coding

Identifiers

PMID26441602
PMCPMC4584941

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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.