Evidence map›Paper›PMID 41123592›Full record

ArticleeLife2025

Sequential temporal anticipation characterized by neural power modulation and in recurrent neural networks.

Xiangbin Teng, Ru-Yuan Zhang

Abstract read
In one paragraph

Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Xiangbin TengDepartment of Psychology, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0001-5360-4957
Ru-Yuan ZhangBrain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine and School of Psychology, Shanghai, China.ORCID https://orcid.org/0000-0002-0654-715X

Funding

Chinese University of Hong Kong 4937113National Natural Science Foundation of China 32441102Shanghai Municipal Education Commission 2024AIZD014
6 · The paper itself

Abstract

Relevant prospective moments arise intermittently, while most of the time is filled with irrelevant events, or noise, that constantly bombard our sensory systems. Thus, anticipating a few key moments necessitates disregarding what lies between the present and the future - the noise. Here, through examining how the brain and recurrent neural networks (RNNs) anticipate a sequence of prospective moments without relying on any external timing cues, we provided a reinterpretation of temporal anticipation. We first designed a 'premembering' task, where we marked three temporal locations in white noise and asked human listeners to detect a tone at one of these points. Using power modulation analyses, we investigated the memory-guided anticipatory processes in trials involving only flat noise. Our research revealed a unique neural-power modulation pattern for sequential temporal anticipation: neural power within the alpha-beta band range fluctuates over time, accurately identifying the marked locations on a sub-second scale and correlating with tone detection performance. To understand the functional roles of these neural modulations, we utilized RNNs optimized for the behavioral task. The hidden dynamics of the RNNs mirrored the neural modulations, and additional analyses and perturbations on the RNNs indicated that the neural power modulations in the alpha-beta band resulted from selective suppression of irrelevant noise periods and heightened sensitivity to anticipated temporal locations. Our behavioral, neural, and modeling findings collectively indicate that sequential temporal anticipation involves a process of dynamic gain control: anticipating a few significant moments involves actively disregarding irrelevant events that frequently occur.

Indexed as

Anticipation, PsychologicalAuditory PerceptionBrainNerve NetTime PerceptionAdultFemaleHumansMaleRecurrent Neural NetworksYoung Adultalpha-beta powerdynamic gain controlhumanneurosciencetemporal attentiontemporal prediction

Identifiers

PMID41123592
PMCPMC12543324

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.