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Enregistrement W2395056813

Comprehension cueing strategies in elderly: a window into cognitive decline?

2013· article· en· W2395056813 sur OpenAlexaboutno aff
Vanja Kljajević, Viviana Fratini, Aitziber Etxaniz, Elena Urdaneta, José Javier Yanguas

Notice bibliographique

RevueCognitive Science · 2013
Typearticle
Langueen
DomaineNeuroscience
ThématiqueNeurobiology of Language and Bilingualism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCognitionComprehensionCognitive declinePsychologyUtteranceSentenceCognitive psychologyAudiologyVocabularyMontreal Cognitive AssessmentAnalysis of varianceDevelopmental psychologyLinguisticsCognitive impairmentMedicineDementiaStatisticsComputer scienceArtificial intelligenceMathematics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Comprehension cueing strategies in elderly: a window into cognitive decline? Abstract Language abilities gradually decline as we age, but the mechanisms of this decline are not well understood. The present study investigated comprehension of subject vs. object who and which direct questions (DQs), embedded questions (EQs) and relative clauses (RCs) in 39 cognitively healthy native speakers of Spanish. The elderly participants (n = 21) were further classified according to their scores on a general cognitive test, Montreal Cognitive Assessment (MoCA), into a group with low MoCA scores, LM (n = 10), and a group with normal MoCA scores, NM (n = 11). A mixed-model, repeated-measures analysis of variance (ANOVA) showed that the elderly participants achieved significantly worse accuracy and speed than the young participants (Y) in all tasks. Accuracy was significantly lower and reaction times significantly longer in the LM group compared to the NM group in DQs and RCs. Accuracy in comprehension of EQs was also worse in LM compared to NM, with no significant difference in RTs between the two groups. The results are explained within the competition model and reliance on a language-specific cueing strategy. Reliance on cueing strategies in sentence comprehension may be an effective indicator of cognitive decline associated with aging. Keywords: comprehension; wh-dependencies; aging. Introduction Cognitive aging is typically associated with a decline in speed of processing and deterioration of memory and attention (Salthouse, 2009). Language abilities also gradually decline as we age, which is reflected in decreased vocabulary, smaller mean number of clauses per utterance, simplified syntactic structure of produced sentences, reliance on optimization strategies when choosing referring expressions as well as difficulty in comprehension of complex sentences (Kemper, Thompson & Marquis, 2001; Grossman, Cooke, De Vita, Chen, Moore et al., 2002; Hendriks, Englert, Wubs & Hoeks, 2008). Older adults’ language comprehension decline appears to be due not to sensory, but cognitive demands of spoken language, with complex syntax slowing down the comprehension even when sentence understanding is accurate (Tun, Benichov & Wingfield, 2010). Research on English has shown that comprehension of structures that require a syntactic operation of movement and involve a longer gap between a moved element and its trace (t), such as object relative clauses (e.g., The cat i that the dog chased t i is black), is impaired in elderly adults, while comprehension of subject relative clauses, in which this gap is smaller (e.g., The cat i that t i chased the dog is black), is spared (e.g., Zurif, Swinney, Prather, Wingfield & Brownell, 1995; Stine- Morrow, Ryan & Leonard, 2000). One explanation of this finding is that the object relative clauses require allocation of more working memory (WM) resources than subject relative clauses, and WM limitation is one of key features of cognitive aging (Zurif et al., 1995; Caplan & Waters, 1999; Stine-Morrow et al., 2000; Grossman, Cooke, De Vita, Alsop, Detre et al., 2002). Furthermore, neuroimaging research has shown that when processing complex sentences, healthy seniors compared to young participants show reduced activation in the core language areas (e.g., inferior frontal regions), while showing additional activation of some areas that are not considered the “core” sentence processing network as well as difference in the coherence of connectivity of the involved brain areas (Peelle, Troiani, Wingfield, & Grossman, 2010; Tyler, Shafto, Randall, Wright, Marslen-Wilson et al., 2010). Activation of the brain regions that are not typically involved in language processing has been interpreted as an indicator of compensatory processes (Grossman et al., 2002; Wingfield & Grossman, 2006; Tyler et al., 2010). Better understanding of the earliest changes in typical cognitive aging is also an important step towards better understanding of the Alzheimer’s disease (AD) continuum. Structural and metabolic changes in AD brain occur long before cognitive symptoms become apparent (Dubois et al., 2007, 2010; Sperling et al., 2011). Crucially, even small metabolic and structural alterations in the brain may affect the dynamics enabling cognitive function (Buckner, Snyder, Shannon, LaRossa, Sachs, et al., 2005). Thus, it is important to understand the brain’s ability to engage alternate networks and rely on cognitive strategies compensating for a deteriorating cognitive function. One goal of the present study was to determine whether elderly native speakers of Spanish rely on compensatory strategies in sentence comprehension. We chose to study comprehension of wh-structures (i.e., structures formed by wh-words, such as what, who, which, etc.): direct and embedded questions introduced by interrogative pronouns que (“what, which”) and quien (“who”) and relative clauses introduced by que. Like in English, the distance between a moved element and its gap is longer in object than in subject wh-structures, as shown in (1-2): (1) ?Quien i t i comio una naranja? (2) ?A quien i mordio j el perrito t j t i ? However, in Spanish preposition a marks object wh- questions and therefore it could serve as a processing cue. Since it appears before the moved wh-word, it signals an object structure, allowing the parser to assign a temporary thematic role before encountering the gap. Thus, reliance on this cue would facilitate comprehension of object structures, resulting in their good comprehension, even though they are syntactically more difficult than subject structures and require more WM resources.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,056
Score d'incertitude au seuil0,932

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,003
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,034
Tête enseignante GPT0,329
Écart entre enseignants0,295 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2013
Routes d'admission1
Résumé présentoui

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