Evidence map›Paper›PMID 40124513›Full record

ArticleiScience2025

Predictable sequential structure augments auditory sensitivity at threshold.

Nadège Marin, Grégory Gérenton, Hadrien Jean, Nihaad Paraouty, Nicolas Wallaert, Diane S Lazard, Keith B Doelling, Luc H Arnal

Abstract read
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Nadège MarinUniversité Paris Cité, Institut Pasteur, AP-HP, INSERM, CNRS, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, 75012 Paris, France.
Grégory GérentonUniversité Paris Cité, Institut Pasteur, AP-HP, INSERM, CNRS, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, 75012 Paris, France.
Hadrien JeanMy Medical Assistant SAS, 51100 Reims, France.
Nihaad ParaoutyMy Medical Assistant SAS, 51100 Reims, France.
Nicolas WallaertMy Medical Assistant SAS, 51100 Reims, France.
Diane S LazardUniversité Paris Cité, Institut Pasteur, AP-HP, INSERM, CNRS, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, 75012 Paris, France.
Keith B DoellingUniversité Paris Cité, Institut Pasteur, AP-HP, INSERM, CNRS, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, 75012 Paris, France.
Luc H ArnalUniversité Paris Cité, Institut Pasteur, AP-HP, INSERM, CNRS, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, 75012 Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human hearing is highly sensitive and allows us to detect acoustic events at low levels. However, sensitivity is not only a function of the integrity of cochlear transduction mechanisms but is also constrained by central processes such as attention and expectation. While the effects of distraction and attentional orienting are generally acknowledged, the extent to which probabilistic expectations influence sensitivity at threshold is not clear. Classical audiometric tests, commonly used to assess hearing thresholds, do not distinguish between bottom-up sensitivity and top-down processes. In this study, we aim to decipher the influence of various types of expectations on hearing thresholds and how this information can be used to improve the assessment of hearing sensitivity. Our results raise important questions regarding the conventional assessment of hearing thresholds, both in fundamental research and in audiological clinical assessment.

Indexed as

AudiologySensory neuroscience

Identifiers

PMID40124513
PMCPMC11927747

What OpenQuestion holds

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Registered trials

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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.