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Record W2071360023 · doi:10.1080/13825580802411758

Perception of Temporal Order: The Effects of Age, Sex, and Cognitive Factors

2008· article· en· W2071360023 on OpenAlexaff
Pamela Ulbrich, Jan Churan, Martina Fink, Marc Wittmann

Bibliographic record

VenueAging Neuropsychology and Cognition · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersBundesministerium für Bildung und Forschung
KeywordsPerceptionCognitionPsychologyCognitive psychologyVisual perceptionModalitiesTime perceptionAuditory perceptionOrder (exchange)Neuroscience

Abstract

fetched live from OpenAlex

The present paper investigates the effects of age, sex, and cognitive factors on temporal-order perception. Nine temporal-order tasks were employed using two and four stimuli presented in the auditory and visual modalities. Significantly increased temporal-order thresholds (TOT) in the elderly were found for almost all tasks, while sex differences were only observed for two tasks. Multiple regression analyses show that the performance on most temporal-order tasks can be predicted by cognitive factors, such as speed of fluid reasoning, short-term memory, and attention. However, age was a significant predictor of TOT in three tasks using visual stimuli. We conclude (1) that age-related differences can often be attributed to cognitive factors involved in temporal-order perception, and (2) that the concept of temporal-order perception is more complex than implied by the current models.

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)0.005
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0020.000

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 head0.031
GPT teacher head0.292
Teacher spread0.261 · 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

Citations63
Published2008
Admission routes1
Has abstractyes

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