Evidence map›Paper›PMID 41437281›Full record

SynthesisMolecular autism2025

A systematic review of statistical learning in autism spectrum disorder.

Rebecca R Bell, Hannah R Thomas, Jenny R Saffran, Inge-Marie Eigsti

Abstract readSystematic Review
In one paragraph

Synthesis in Molecular autism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Rebecca R BellDepartment of Psychological Sciences, University of Connecticut, 406 Babbidge Road, Storrs, CT, 06269, USA. rebecca.canale@uconn.edu.
Hannah R ThomasDepartment of Psychological Sciences, University of Connecticut, 406 Babbidge Road, Storrs, CT, 06269, USA.
Jenny R SaffranDepartment of Psychology, University of Wisconsin-Madison, 1202 W Johnson St, Madison, WI, 53706, USA.
Inge-Marie EigstiDepartment of Psychological Sciences, University of Connecticut, 406 Babbidge Road, Storrs, CT, 06269, USA. inge-marie.eigsti@uconn.edu.

Funding

Optimal Outcomes in ASD: Adult Functioning, Predictors, and MechanismsR01MH112687 · NIMH · UNIVERSITY OF CONNECTICUT STORRS · PI EIGSTI, INGE-MARIE, FEIN, DEBORAH A. · 2018 to 2022
$3.4M
Training in the Cognitive Neuroscience of CommunicationT32DC017703 · NIDCD · UNIVERSITY OF CONNECTICUT STORRS · PI Inge-Marie Eigsti, Emily B Myers · 2019 to 2026
$2.3M
Modeling Individual Differences in the Temporal Dynamics of Spoken Word Recognition in Autistic Young AdultsF31DC022187 · NIDCD · UNIVERSITY OF CONNECTICUT STORRS · PI Rebecca Bell · 2024 to 2026
$101k
National Institutes of Health, United States NIDCD-1F31DC022187-01National Institutes of Health, United States NIMH-1R01MH112687-01A1NIDCD NIH HHS F31 DC022187NIDCD NIH HHS T32 DC017703NIMH NIH HHS R01 MH112687
6 · The paper itself

Abstract

Statistical learning, the ability to detect and extract statistical regularities from the environment, has been proposed as a key mechanism underlying language, social, and cognitive development. Numerous studies have examined statistical learning abilities in autistic individuals to test the hypothesis that differences contribute to the behavioral presentation of autism spectrum disorder (ASD). Findings have been inconsistent, with variations in methodology, sensory modality, and participant characteristics complicating the interpretation of results. The current study presents a systematic review of statistical, implicit, and procedural learning studies in autism, considering how statistical learning abilities vary across (a) modality (e.g., auditory versus visual), (b) methodology (e.g., behavioral versus neuroimaging), and (c) task design, and considering the influence of language and cognitive abilities. Results across 37 studies in visual and auditory modalities indicate few behavioral differences in statistical learning abilities (with the exception of slowed reaction times in autism), and that learning may benefit from extended exposure and explicit cues. In contrast, neuroimaging findings reveal substantial variability in the neural mechanisms implicated in these tasks, with evidence suggesting compensatory cognitive processing in some autistic samples. Individual differences in language, cognitive abilities, and autism-related traits strongly influence statistical learning outcomes. Significant gaps remain, particularly in the inclusion of minimally verbal individuals and those with intellectual disabilities. Methodological heterogeneity, skewed gender and sociodemographic sample characteristics, and inconsistent neural findings highlight the need for more standardized approaches in future research. Understanding the mechanisms of statistical learning in autism has critical implications for language and cognitive interventions, emphasizing the importance of individualized support strategies.

Indexed as

Autism Spectrum DisorderLearningCognitionHumansAuditory statistical learningEEGFunctional MRIImplicit learningPredictionProcedural learningReaction timeVisual statistical learning

Identifiers

PMID41437281
PMCPMC12805773

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.