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Record W3108170777 · doi:10.3389/fnins.2020.605267

Age-Related Deficits in Binocular Vision Are Associated With Poorer Inhibitory Control in Healthy Older Adults

2020· article· en· W3108170777 on OpenAlexafffund
Grace Lin, Raghda Al Ani, Ewa Niechwiej‐Szwedo

Bibliographic record

VenueFrontiers in Neuroscience · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStereoscopic acuityStereopsisAudiologyPsychologyContrast (vision)Binocular visionDepth perceptionSensory systemCognitionPopulationVisual acuityMedicinePerceptionOphthalmologyNeuroscienceArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

A robust association between reduced visual acuity and cognitive function in older adults has been revealed in large population studies. The aim of this work was to assess the relation between stereoacuity, a key aspect of binocular vision, and inhibitory control, an important component of executive functions. Inhibition was tested using the antisaccade task in older adults with normal or reduced stereopsis (study 1), and in young adults with transiently reduced stereopsis (study 2). Older adults with reduced stereopsis made significantly more errors on the antisaccade task in comparison to those with normal stereopsis. Specifically, there was a significant correlation between stereoacuity and antisaccade errors ( r = 0.27, p = 0.019). In contrast, there were no significant differences in antisaccade errors between the normal and reduced stereopsis conditions in the young group. Altogether, results suggest that the association between poorer stereopsis and lower inhibitory control in older adults might arise due to central nervous system impairment that affects the processing of binocular disparity and antisaccades. These results add to a growing body of literature, which highlights the interdependence of sensory and cognitive decline in older adults.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.620
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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 teacher head, 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

Citations9
Published2020
Admission routes2
Has abstractyes

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