Evidence map›Paper›PMID 35501360›Full record

ArticleNature human behaviour2022

Rational arbitration between statistics and rules in human sequence processing.

Maxime Maheu, Florent Meyniel, Stanislas Dehaene

Abstract read
PubMed Publisher
In one paragraph

Article in Nature human behaviour, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Sensory integration, temporal prediction, and rule discovery reflect interdependent inference processes.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  6. Lexical Surprisal Shapes the Time Course of Syntactic Structure Building.Neurobiology of language (Cambridge, Mass.) · 2024
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  15. Observational
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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

3 authors.

Maxime MaheuCognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Centre, Gif-sur-Yvette, France. maheu.mp@gmail.com.ORCID 0000-0002-6851-4927
Florent MeynielCognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Centre, Gif-sur-Yvette, France.ORCID 0000-0002-6992-678X
Stanislas DehaeneCognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Centre, Gif-sur-Yvette, France.ORCID 0000-0002-7418-8275

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence in humans of a dissociation between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.

Indexed as

LearningNegotiatingBayes TheoremCognitive ScienceHumansProbability

Identifiers

PMID35501360

What OpenQuestion holds

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Registered trials

None linked

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.