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Record W2038097704 · doi:10.1167/10.14.5

Spatiotemporal properties of apparent motion perception and aging

2010· article· en· W2038097704 on OpenAlexafffund
Eugenie Roudaia, Patrick Bennett, Allison B. Sekuler, Karin S. Pilz

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork UniversityMcMaster University
FundersCanada Research ChairsCanadian Institutes of Health ResearchAlexander von Humboldt-Stiftung
KeywordsRangingPerceptionDisplacement (psychology)Motion perceptionSpatial frequencyStimulus (psychology)PsychologyOpticsCartographyAudiologyPhysicsGeodesyGeographyMedicineCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

We used a random-dot two-frame apparent motion paradigm to investigate whether age-related declines in motion perception are caused by deficits in integrating spatial information, temporal information, or both. Two random-dot patterns were presented sequentially on a black screen, separated by a blank inter-stimulus interval ranging from 0.01 s to 0.240 s. From the first to the second pattern, all the dots were shifted to the left or right by an equal displacement ranging from 0.03 deg to 1.64 deg. The spatiotemporal range yielding good direction discrimination performance was greatly reduced with age. For ISIs longer than 0.04 s, older subjects performed less accurately than younger subjects across a wide range of spatial displacements. Older subjects also showed poorer performance for large spatial displacements across a wide range of ISIs. Age-related differences in performance were also found with small displacements; however, these were largely accounted for by age-related declines in visual acuity. Overall, the results show that the maximum temporal interval and maximum spatial displacement over which two frames can be integrated are reduced in older 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 integrity0.0000.000
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 head0.057
GPT teacher head0.333
Teacher spread0.276 · 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

Citations52
Published2010
Admission routes2
Has abstractyes

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