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Record W4406208667 · doi:10.1002/alz.090577

Evaluating the use of eye tracking tasks as language‐ and culturally‐neutral assessments of cognition in a multi‐ethnic cohort of older adults

2024· article· en· W4406208667 on OpenAlexaffabout
Rachel Yep, Alexander Nyman, Georgia Gopinath, Madeline Wood Alexander, Tulip Marawi, Silina Z. Boshmaf, Donald C. Brien, Brian C. Coe, Christopher B. Pople, Douglas P. Munoz, Jennifer D. Ryan, Sandra E. Black, Maged Goubran, Jennifer S. Rabin

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsBaycrest HospitalQueen's UniversityUniversity of TorontoSunnybrook Hospital
Fundersnot available
KeywordsEthnic groupCohortEye trackingCognitionTracking (education)PsychologyDevelopmental psychologyCognitive psychologyMedicineComputer scienceSociologyArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Canada and the United States are both aging and becoming increasingly diverse. Despite this demographic shift, non‐White racial/ethnic groups remain underrepresented in research on cognitive impairment and dementia. A major barrier to inclusivity is the lack of cognitive assessments that are valid in individuals with diverse language and cultural backgrounds. Eye tracking tasks can potentially overcome this barrier since they do not require verbal responses and use culturally neutral stimuli. Here, we tested this hypothesis by comparing performance on standard neuropsychological tests and eye tracking tasks across individuals from White and underrepresented ethnic groups (UEG). Method Participants were cognitively unimpaired adults enrolled in the Canadian Multi‐Ethnic Research on Aging (CAMERA) study. Participants completed a battery of standard neuropsychological tests and two well‐characterized eye tracking tasks: the interleaved pro/anti‐saccade task (IPAST), which assesses executive function, and the visual paired comparison task (VPCT), which assesses visual memory. Result We included 54 participants (mean age=68.68±6.91, 70% female) with a Clinical Dementia Rating global score of 0. Among them, 22% of participants self‐identified as White (n=12) and the remaining participants identified as being from an UEG (n=42; 37% East Asian, 37% South Asian, 4% Other). Linear regression models assessed whether ethnic background (White vs. UEG) was associated with task performance after adjusting for age, sex, and years of education. We found that ethnic background influenced performance on several standard neuropsychological tests, including the MoCA (β=0.26, p=0.03), phonemic fluency (β=0.29, p=0.02), category fluency (β=0.38, p=0.003), and Benson Complex Figure delay (β=0.23, p=0.08), such that the UEG group had lower scores. There were no group differences on Trails A and B or the Symbol Digit Modalities Test (p‐values>0.1). Importantly, ethnic background did not influence performance on either eye tracking task (IPAST: β=‐0.17, p=0.17; VPCT: β=0.02, p=0.83). Conclusion Our preliminary results suggest that ethnic background influences performance on several standard neuropsychological tests, but not performance on the IPAST or VPCT. With further validation in larger samples, these eye tracking tasks may provide a brief and language‐free way to screen and monitor cognitive impairment in diverse samples currently underrepresented in dementia research.

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)0.000
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 score0.825
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.100
GPT teacher head0.403
Teacher spread0.303 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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