Identifying Early Signs of Cognitive Deficits Using a Novel Eye-Tracking Protocol: Distinctions between Subjective Concerns and Healthy Controls.
Notice bibliographique
Résumé
The dataset consists of behavioural and cognitive performance measures collected as part of a longitudinal study investigating visual recognition memory and related neurocognitive variables in older adults. Data were acquired at two time points approximately 12 months apart (Time 1 and Time 2). Participants were categorised into two groups based on self-reported cognitive status: Healthy Control (HC) and Subjective Cognitive Concern (SCC). All behavioural data were obtained under controlled laboratory conditions using standardized procedures.Eye-tracking–based Visual Paired Comparison (VPC) performance was assessed at both time points using a structured viewing paradigm that presents familiar and novel stimuli following either a 20-second or 2-minute delay interval. For each delay condition, the percentage of viewing time directed toward the novel stimulus was computed as the primary outcome measure. Eye-tracking data were processed using built-in software algorithms that automatically identify fixations and calculate fixation duration percentages. Cognitive performance was also measured via standardized tests, including the Montreal Cognitive Assessment (MoCA), following established administration and scoring guidelines.The dataset contains 47 participants, each represented by a unique numerical identifier. Tabular data are provided in CSV format. Each row corresponds to a single participant, and columns represent:Group (1 = HC, 2 = SCC)Timepoint (1 or 2)VPC_20sec (percentage of fixation on the novel stimulus at the 20-second delay)VPC_2min (percentage of fixation at the 2-minute delay)MoCA scoreAll percentage-based variables are expressed as continuous numerical values. Cognitive test scores follow the standard scoring ranges defined by the respective instruments. Missing values are present in a small number of entries where participants did not complete a given measure at one of the time points. Missingness is non-systematic and primarily due to participant withdrawal or technical issues during eye-tracking acquisition (e.g., loss of calibration). No imputation has been applied; missing values are coded as blank cells in the CSV files.Internal consistency checks were performed to identify outliers, unexpected score ranges, and anomalous values. No measurement errors beyond expected human performance variability were detected. Eye-tracking error ranges align with typical commercial infrared-based systems, which maintain sub-degree spatial accuracy; however, as only percentage fixation measures are reported, hardware error is not directly expressed in the tabular data.The dataset is stored in comma-separated value (CSV) format, ensuring broad compatibility across statistical and data-analysis platforms such as R, Python, MATLAB, SPSS, and Excel.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».