When Perceptual Representations Defer to Grammar: Conflicting Linguistic and Perceptual Cues in Cantonese Classifiers
Notice bibliographique
Résumé
A growing body of evidence suggests semantic representations for linguistic expressions are grounded in perception, and that these representations guide online sentence processing. However, semantic distinctions that superficially seem grounded in perception are sometimes partially or wholly grammaticalized. To date, these cases have not been investigated in studies of real-time comprehension. We consider the case of shape classifiers in Cantonese Chinese - prenominal expressions that arguably encode size/shape characteristics of their associated nouns, but whose occurrence with nouns is determined by grammar. E.g., the classifier tiu typically precedes nouns denoting long-narrow-flexible things (e.g., ropes, snakes...). However, tiu occurs with the noun for [goldfish] even though goldfishes are not prototypically long-narrow-flexible. Conversely, although a stocking is normally long-narrow-flexible, this noun cannot occur with tiu. Thus, grammatical rules governing classifier-noun pairings can conflict with perceptually-based meanings of classifiers. An offline test of Cantonese speakers' intuitions showed they considered classifier-noun pairings to be rule-based 97.6% of the time, rather than being dependent on prototypical size/shape features of noun referents. However, perceptual information encoded by classifiers might be computed unconsciously during online comprehension, somewhat like the processing of fictive motion [1]. To assess this possibility, we employed an eye-tracking methodology known to be sensitive to perceptual representations associated with words [2]. Cantonese listeners followed spoken instructions containing a classifier-noun pairing that named a target object (e.g., tiu [snake]). Displays also contained a item that either (i) matched both the classifier's grammatical and perceptual parameters (e.g., [rope]), (ii) matched the grammatical but not the canonical perceptual parameters (e.g., [goldfish]); or (iii) matched the perceptual but not the grammatical parameters (e.g., [stocking]). Of interest was how often the competitor attracted fixations prior to eventual fixation on the target. Our findings indicated that only grammatically-legitimate competitors attracted attention as the noun phrase unfolded; the match/mismatch with classifier-denoted perceptual parameters had no effect. Thus, perceptual information encoded by classifiers did not seem to be computed during online interpretation. A second eye-tracking experiment tested whether perceptually-grounded meanings might become more apparent when target objects do not possess the canonical perceptual features conveyed by their associated classifier (e.g., a goldfish for tiu), potentially highlighting the relevance of perceptual features. Competitor pictures either matched the perceptual but not the grammatical parameters of the classifier (e.g., [stocking]), or matched both the perceptual and the grammatical parameters (e.g., [snake]). As before, the latter type of competitor attracted fixations during the initial stages of processing. However, unlike the earlier experiment, grammatically illegitimate competitors matching the classifier's perceptual parameters were now observed to attract attention. Overall, the results show perceptual features of classifiers are computed at a relatively shallow level during comprehension, and that these computations are normally dominated by rule-like grammatical constraints. Implications for theories claiming a perceptual/embodied basis for linguistic representations will be discussed. References Richardson & Matlock (2005). The integration of figurative language and static depictions. Cognition, 102, 129-138. Huettig & Altmann (2007). Visual-shape competition and control of eye fixation during the processing of words. Visual Cognition, 15, 985-1018.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».