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Record W2058353339 · doi:10.1167/8.6.932

Older adults just can't look away: Age-related changes in saccadic trajectory curvature

2010· article· en· W2058353339 on OpenAlexaff
Jay Pratt, Naseem Al-Aidroos, M. Karen Campbell, Lynn Hasher

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSaccadic maskingGazePsychologyEye movementVisual fieldCognitive psychologyAudiologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

There is considerable evidence that the ability to inhibit irrelevant information declines as people grow older. In the present research we investigated whether such an inhibitory deficit extends to the systems responsible for the control of saccadic eye movements. Participants made saccades to a visual target that appeared concurrently with a distractor, and saccadic latencies and trajectories were recorded. Consistent with earlier findings, younger adult's early-onset saccades curved towards the distractor (as the distractor competed with the target for response selection) while late-onset saccades curved away from the distractor (as the distractor location became inhibited over time). In contrast, older adults' saccades always curved towards the distractor, indicating they were unable to inhibit the distractor in the same manner as the younger adults. This demonstrates that, as people age, they lose the ability to prevent their eyes from being drawn to irrelevant visual distractors. As the location of gaze plays a dominant role in determining what information in the visual field is processed, this finding has important implications for changes in the efficiency of visual processing with age.

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)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.026
GPT teacher head0.322
Teacher spread0.296 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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