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Record W2145081194 · doi:10.1080/03610731003613425

Aging and Vigilance: Who Has the Inhibition Deficit?

2010· article· en· W2145081194 on OpenAlexafffund
Kristina Brache, Charles T. Scialfa, Carl Hudson

Bibliographic record

VenueExperimental Aging Research · 2010
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsVigilance (psychology)CognitionPsychologyAudiologyResponse inhibitionPoison controlDevelopmental psychologyCognitive psychologyMedicineMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

The present study compared 18 younger (M = 21.00 years) and 17 older adults (M = 64.29 years) in a modified vigilance task that required the inhibition of a routinized response. The task was a 50-min simulation of industrial inspection, wherein observers were presented with simple displays labeled "good" and "bad" parts. General linear modeling indicated that younger adults showed a doubling of inhibition failures over time (from 19% to 43%); older adults' inhibition failures held constant at approximately 17.5%. In both age groups, those who responded most quickly were also most error-prone. A control experiment, using the traditional vigilance task requiring a response to infrequent "bad" parts, found only small age differences in accuracy and these also favored older adults. This research suggests that younger adults may demonstrate larger inhibition failures when the routinized responses on simple tasks must be suppressed. There are several implications for theory, industrial design, and cognitive assessment.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.104
GPT teacher head0.480
Teacher spread0.376 · 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

Citations30
Published2010
Admission routes2
Has abstractyes

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