Evidence map›Paper›PMID 41614374›Full record

ArticlePsychophysiology2026

The Relationship Between Task-Related Aperiodic EEG Activity, Neural Inefficiency and Verbal Working Memory in Younger and Older Adults.

Sabrina Sghirripa, Alannah Graziano, Mitchell Goldsworthy

Abstract read
In one paragraph

Article in Psychophysiology, 2026. 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. Article
  2. 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

3 authors.

Sabrina SghirripaAustralian Institute for Machine Learning, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, South Australia, Australia.ORCID https://orcid.org/0000-0003-0275-1476
Alannah GrazianoHopwood Centre of Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
Mitchell GoldsworthyLifespan Human Neurophysiology, School of Biomedicine, The University of Adelaide, Adelaide, South Australia, Australia.

Funding

Australian Research Council FT230100658
6 · The paper itself

Abstract

Working memory (WM) decline in aging may be related to increases in "neural noise", potentially reflected in the EEG aperiodic exponent. We reanalyzed previously published data to investigate age-related differences in the aperiodic exponent during verbal WM and its relationship with neural inefficiency. EEG was recorded from 24 younger (18-35 years) and 30 older adults (50-86 years) during a modified Sternberg task with 1-letter, 3-letter, and 5-letter load conditions. Younger adults consistently demonstrated steeper aperiodic slopes than older adults. Unexpectedly, both age groups showed decreased (i.e., flattened) aperiodic exponents during retention relative to fixation, with minimal load-dependent effects. Notably, the relationship between task-related exponent changes and WM performance was complex and dependent on the exponent at fixation, particularly in older adults. Finally, flatter exponents during fixation and late retention were associated with greater neural inefficiency during stimulus processing, reflected by increased P3b amplitudes without corresponding WM performance improvements. These findings suggest that flatter exponents are associated with less efficient neural processing and that older adults flexibly modulate their aperiodic exponent during retention to support WM performance.

Indexed as

AgingMemory, Short-TermAdolescentAdultAgedAged, 80 and overElectroencephalographyFemaleHumansMaleMiddle AgedYoung Adultcognitive agingEEGneural efficiencyworking memory

Identifiers

PMID41614374
PMCPMC12856785

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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.