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Record W7154604045 · doi:10.48448/4x26-k695

Age-related changes in cognitive flexibility: fMRI meta‐analysis

2025· other· W7154604045 on OpenAlexaff
Cognitive Science Society 2025, Marie Arsalidou, Zhanna Chuikova, Andrei Faber, Andrei Filatov, Andriy Myachykov, Yury Shtyrov

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsYork University
Fundersnot available
KeywordsCognitionCognitive flexibilityCognitive agingCognitive declineFlexibility (engineering)Neural activityAgeingNeural correlates of consciousnessTask switching

Abstract

fetched live from OpenAlex

To examine neural mechanisms underlying cognitive flexibility changes with ageing, we synthesized findings from 87 fMRI studies, comprising 120 experiments with 2308 adult participants distributed across young, middle-age, and older groups. Our meta-analysis was focused on rule-retrieval and rule-discovery processes, assessed with Task-Switching Paradigm and Wisconsin Card Sorting Test, respectively. Activation Likelihood Estimation analyses revealed age-related decreases in brain activation related to general switching ability, particularly in posterior regions, alongside an anterior shift in older adults, consistent with the Posterior-Anterior Shift in Aging (PASA) model. Rule-retrieval tasks consistently engaged left-lateralized frontoparietal regions across all age groups, with middle-age adults additionally recruiting the right cerebellum and medial-frontal gyrus. For rule-discovery tasks, age-related decline was observed in bilateral frontoparietal regions, while older adults also showed unique activation in the left inferior-frontal gyrus. These findings highlight differential ageing trajectories for rule retrieval and rule discovery, potentially reflecting compensatory neural mechanisms and dedifferentiation processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Bibliometrics, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.004
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0290.079
Science and technology studies0.0010.019
Scholarly communication0.0010.001
Open science0.0050.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0720.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.368
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designMeta-analysis
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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