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Record W2124182611 · doi:10.3200/jmbr.38.5.373-382

Growing Older Does Not Always Mean Moving Slower: Examining Aging and the Saccadic Motor System

2006· article· en· W2124182611 on OpenAlexafffund
Jay Pratt, Michael D. Dodd, Timothy N. Welsh

Bibliographic record

VenueJournal of Motor Behavior · 2006
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of CalgaryUniversity of British ColumbiaUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsSaccadic maskingSaccadic eye movementPsychologyAudiologyKinematicsSaccadePhysical medicine and rehabilitationEye movementNeuroscienceMedicinePhysics

Abstract

fetched live from OpenAlex

Although humans typically move more slowly as they age, one exception may be the saccadic motor system. To fully determine whether the execution of saccades is affected by age, the authors examined detailed kinematics of vertical and horizontal saccades across a range of saccadic amplitudes (4 degrees, 8 degrees, and 12 degrees). Ten younger and 20 older adults participated in each experiment. Whereas in the 1st experiment, the authors assessed volitionally generated saccades, in the 2nd experiment, they evaluated reflexively generated saccades. The results of those experiments showed that the saccadic motor system is relatively impervious to the effects of aging; in fact, the differences between vertical and horizontal saccades were more evident than were differences between saccades produced by younger and older adults. The authors discuss possible reasons for that relative resistance to aging.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.028
GPT teacher head0.264
Teacher spread0.236 · 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

Citations58
Published2006
Admission routes2
Has abstractyes

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