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Record W4413107343 · doi:10.1073/pnas.2505879122

Decoding memory function through naturalistic gaze patterns

2025· article· en· W4413107343 on OpenAlexafffund
Jordana S. Wynn, Anisha Khosla, Arber Kacollja, Elaheh Shahmiri, Nicole D. Anderson, Kelly Shen, Jennifer D. Ryan

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsVector InstituteUniversity of TorontoSimon Fraser UniversityBaycrest HospitalUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsGazeEncoding (memory)PsychologyCognitive psychologyTask (project management)CognitionNeuroscience

Abstract

fetched live from OpenAlex

Eye movements are closely linked to encoding and retrieval processes, with changes in viewing behavior reflecting age- and pathology-related memory decline. In the current study, we leveraged this relationship to explore possible gaze-based indicators of memory function. Across two task-free viewing experiments, we investigated changes in naturalistic viewing behavior across five participant groups spanning a broad spectrum of memory function, from healthy young adults to amnesic cases. We show that memory decline is associated with an underlying reduction in explorative, adaptive, and differentiated visual sampling of the environment. Our results provide compelling evidence that naturalistic gaze patterns can serve as a sensitive marker of cognitive decline.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.024
GPT teacher head0.303
Teacher spread0.279 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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