Text Verification and Verb Factivity: An ERP Investigation
Notice bibliographique
Résumé
Text Verification and Verb Factivity: An ERP Investigation Todd R. Ferretti Murray Singer Department of Psychology, Wilfrid Laurier University Waterloo, Ontario, Canada Department of Psychology, University of Manitoba Winnipeg, Manitoba, Canada Courtney Patterson Department of Psychology, Wilfrid Laurier University Waterloo, Ontario, Canada epochs that extended 100 ms before the critical word in the Keywords: verb meaning; discourse processing; event- target sentence (i.e., truck) to 1000 ms post stimulus onset. related brain potentials (ERP). Introduction Results and Discussion Singer (2006; JML) has recently investigated text verification processes when people read passages similar to (1). These passages included sentences that varied in their truth with reference to antecedent text (i.e., truck (true) / bus (false)), factivity of the main verb (comprehended (factive) / implied (nonfactive)), and negation (was (affirmative) / wasn’t (negative)). The results demonstrated that in the Late Positivity Complex (LPC) region (600-1000 ms post stimulus onset), amplitudes were more positive for true, factive sentences than for false, factive sentences. However, truth had no influence on nonfactive verbs. Similarly, in the P2 region (200-300 ms), amplitudes varied in the same way as a function of truth and factivity. Despite these clear differences in early and late components, in the N400 region (300-500 ms), amplitudes varied only as a function of truth (see Figure 1). These findings are consistent with Singer’s (2006) reading time data, and provide insight into how the brain processes information about truth of discourse constituents in conjunction with the factivity associated with verbs. In particular, the interaction in the P2 results suggest the brain is most prepared to process the visual features of the targets words in true, factive sentences, and the least prepared for the targets in false, factive sentences. The same pattern of findings in the LPC data also suggests that people had the least difficulty integrating the targets into the discourse in the true, factive condition, and the most difficulty for the targets in the false, factive condition. Dan had been driving all night in order to get home for Thanksgiving. Before long, Dan drove past a truck/bus which was stopped with a flat tire. He couldn’t help but laugh because its spare tire must have been underneath everything and suitcases and boxes were strewn everywhere. Later, while Dan was sitting in a diner, drinking some coffee, a policeman came in and started a conversation with him. He implied/comprehended that the vehicle with the flat was/wasn’t a truck. Singer found that reading times varied systematically with truth, factivity, and negation. For factive, but not nonfactive verbs, reading times for false, affirmative sentences were read more slowly than true, affirmative sentences. Alternatively, false, negative sentences were read more slowly than true, negative sentences, but this was only true for nonfactive verbs. These results are consistent with Singer’s proposal that readers verify discourse constituents against the referents that they passively cue during reading. In the present research, we extend these results by providing converging neurocognitive evidence for these reading processes by employing ERP methodology. The main focus of this research was on affirmative sentences. Method µV 0.0 Materials Stimuli consisted of 32 target passages and 21 filler passages. The target passages were identical to (1) with the exception that we only used affirmative target sentences. Words in the target sentences were presented one a time for a duration of 300 ms and an SOA of 500 ms. ms Figure 1: Results at a central - parietal electrode located on the midline. Solid = true / factive, dots = false / factive, dash = true / nonfactive, dash + dot = false /nonfactive. Acknowledgments EEG Recording Parameters This research was supported by a CFI grant to the first author, and by separate NSERC discovery grants awarded to the first and second authors. EEG was recorded from 64 electrodes from 48 participants. Impedances were kept below 5KΩ. ERPs were computed in
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 | 0,003 |
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 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 ».