Evidence map›Paper›PMID 42878409›Full record

ArticleAnnals of the New York Academy of Sciences2026

Neural Oscillatory Signatures of Predictive Processing in Visual Statistical Learning.

Martina Pasqualetti, Jakob C B Schwenk, Andrea Alamia

Abstract read
In one paragraph

Article in Annals of the New York Academy of Sciences, 2026. 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

3 authors.

Martina PasqualettiCentre De Recherche Cerveau Et Cognition (CerCo), CNRS, Université De Toulouse, Toulouse, France.
Jakob C B SchwenkCentre De Recherche Cerveau Et Cognition (CerCo), CNRS, Université De Toulouse, Toulouse, France.
Andrea AlamiaCentre De Recherche Cerveau Et Cognition (CerCo), CNRS, Université De Toulouse, Toulouse, France.

Funding

European UnionEuropean Union's Horizon 2020 101075930
6 · The paper itself

Abstract

Several studies suggest that neural oscillations play a role in cognition, including predictive processing. Recent models propose that backward alpha-band traveling waves reflect top-down predictions, while forward waves may reflect bottom-up prediction errors. We tested this hypothesis using a visual statistical learning task in which participants (N = 31) detected target shapes presented with varying levels of predictability. Electrophysiological activity and pupillometry were recorded throughout the task. Behavioral results showed faster responses for predictable targets, and pupil dilation increased for unexpected shapes. Evoked response potential analyses revealed predictability effects on the P300 component across occipital and frontal electrodes, along with modulation of alpha oscillations. However, traveling wave analyses did not show a clear effect of predictability between conditions. Instead, wave patterns showed a modest association with individual differences in cognitive strategy, with stronger top-down alpha-band waves in participants who relied more on statistical regularities. Overall, these findings challenge the view that alpha-band traveling waves implement predictive coding during visual perception, suggesting instead that they may reflect more general cognitive strategies rather than specific predictive processes.

Indexed as

BrainLearningVisual PerceptionAdultAlpha RhythmCognitionElectroencephalographyFemaleHumansMalePhotic StimulationYoung Adultalpha bandbrain oscillationspredictive codingstatistical learningtraveling waves

Identifiers

PMID42878409
PMCPMC13670989

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.