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Record W2042392563 · doi:10.1037/a0017122

Learning to bypass the central bottleneck: Declining automaticity with advancing age.

2010· article· en· W2042392563 on OpenAlexfundno aff
François Maquestiaux, Maude Laguë-Beauvais, Eric Ruthruff, Alan A. Hartley, Louis Bherer

Bibliographic record

VenuePsychology and Aging · 2010
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutomaticityBottleneckPsychologyTask (project management)Cognitive psychologyCognitionYoung adultAudiologyDevelopmental psychologyNeuroscienceComputer science

Abstract

fetched live from OpenAlex

Does advancing age reduce the ability to bypass the central bottleneck through task automatization? To answer this question, the authors asked 12 older adults and 20 young adults to first learn to perform an auditory-vocal task (low vs. high pitch) in 6 single-task sessions. Their dual-task performance was then assessed with a psychological refractory period paradigm, in which the highly practiced auditory-vocal task was presented as Task 2, along with an unpracticed visual-manual Task 1. Converging evidence indicated qualitative differences in dual-task performance with age: Whereas the vast majority of young adults bypassed the bottleneck, at most 1 of the 12 older adults was able to do so. Older adults are either reluctant to bypass the bottleneck (as a matter of strategy) or have lost the ability to automatize task performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.373
Teacher spread0.358 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations54
Published2010
Admission routes1
Has abstractyes

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