Evidence map›Paper›PMID 40953003›Full record

ReviewAnnals of the New York Academy of Sciences2025

Temporal Predictions in Music and Language: The Case of Autism Spectrum Disorder.

Maude Denis, Chiara Mazzocconi, David Da Fonseca, Daniele Schön

Abstract readReview
In one paragraph

Review in Annals of the New York Academy of Sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

4 authors.

Maude DenisInserm, INS, Institut de Neurosciences des Systèmes, Aix Marseille University, Marseille, France.ORCID 0009-0002-7261-0222
Chiara MazzocconiInserm, INS, Institut de Neurosciences des Systèmes, Aix Marseille University, Marseille, France.
David Da FonsecaCNRS, CRPN, Aix Marseille University, Marseille, France.
Daniele SchönInserm, INS, Institut de Neurosciences des Systèmes, Aix Marseille University, Marseille, France.

Funding

A*MIDEX AMX-19-IET-19004ANR-21-CE28-0010 (DS) ANR-21-CE28-0010ILCB CONV-0002NeuroMarseille ANR-17-EURE-0029
6 · The paper itself

Abstract

The predictive coding theory of autism suggests that individuals with autism may show atypicalities in how predictions are formed or updated. This may in turn affect how they process temporal information. While predictive coding has been widely applied to the study of language, including conversation, and music, including musical interactions, relatively few studies have explored the intersection of these domains in autism. Even fewer have focused on the role of temporal predictions in both language and music. This review examines studies that investigate temporal processing and predictive mechanisms in both music and language in individuals with autism spectrum disorder (ASD). Understanding these shared temporal mechanisms is crucial for providing a more comprehensive view of the underlying cognitive processes and difficulties in ASD. Furthermore, exploring the relationship between music and language from a temporal prediction perspective offers valuable insights into more ecologically valid and interactive settings, such as conversation and music-making. Such research not only improves our understanding of autism but also has important implications for therapeutic interventions, particularly those leveraging rhythmic training to enhance social communication and coordination skills.

Indexed as

Autism Spectrum DisorderLanguageMusicAuditory PerceptionHumansautism spectrum disorderconversational alignmentpredictive codingrhythmic synchronization

Identifiers

PMID40953003
PMCPMC12576881

What OpenQuestion holds

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LicenceCC BY
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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.