Pupil size and eye movements differently index effort in both younger and older adults
Notice bibliographique
Résumé
Abstract The assessment of mental effort is increasingly relevant in neuro-cognitive and lifespan domains. Pupillometry, the measure of the pupil size, is often used to assess effort but has disadvantages. Analysis of eye movements may provide an alternative, but research has been limited to easy and difficult task demands in younger adults. An effort measure must be sensitive to the whole effort profile, including ‘giving up’ effort investment, and capture effort in different age groups. The current study comprised three experiments in which younger (N=66) and older adults (N=44) listened to speech masked by background babble at different signal-to-noise ratios associated with easy, difficult, and impossible speech comprehension. We expected individuals to invest little effort for easy and impossible speech (‘giving up’) but to exert effort for difficult speech. Indeed, pupil size was largest for difficult, but lower for easy and impossible speech. In contrast, gaze dispersion decreased with increasing speech masking in both age groups. Critically, gaze dispersion during difficult speech returned to levels similar to easy speech after sentence offset, when acoustic stimulation was similar across conditions, whereas gaze dispersion during impossible speech continued to be reduced. These findings show that a reduction in eye movements is not a byproduct of acoustic factors, but instead suggest that neuro-cognitive processes, different from arousal-related systems regulating the pupil size, drive reduced eye movements during high task demands. The current data thus show that effort in one sensory domain (audition) differentially impacts distinct functional properties in another sensory domain (vision). Significance statement The assessment of mental effort is increasingly relevant in many domains. Here, we investigated the sensitivity of a novel measure of effort - the spatial dispersion of eye movements - and compared it to pupillometry, which is a more common effort-assessment tool. Individuals listened to speech masked by background babble at levels associated with easy, difficult, and impossible speech comprehension. Pupil size was largest for difficult, but lower for easy and impossible speech conditions (giving up listening). In contrast, the spatial dispersion of eye movements decreased with increasing speech masking, but this effect was independent of acoustic factors. Instead, the current results suggest that neuro-cognitive processes, different from arousal-related systems regulating the pupil size, drive reduced eye movements during high task demands.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».