Associations and Dissociations: An Investigation of Lexical Access Deficits in Agrammatism and Anomia
Notice bibliographique
Résumé
No AccessPerspectives on Neurophysiology and Neurogenic Speech and Language DisordersArticle1 Dec 2005Associations and Dissociations: An Investigation of Lexical Access Deficits in Agrammatism and Anomia Jean K. Gordon Jean K. Gordon University of Iowa, Iowa City Google Scholar More articles by this author https://doi.org/10.1044/nnsld15.4.19 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In References Berndt, R. S., Haendiges, A. N., Mitchum, C. C., & Sandson, J. (1997). Verb retrieval in aphasia: 2. Relationship to sentence processing.Brain and Language, 56, 107–137. Google Scholar Berndt, R. S., Mitchum, C. C., Haendiges, A. N., & Sandson, J. (1997). Verb retrieval in aphasia: 1. Characterizing single word impairments.Brain and Language, 56, 68–106. CrossrefGoogle Scholar Berndt, R. S., Wayland, S., Rochon, E., Saffran, E., & Schwartz, M. (2000). Quantitative production analysis: A training manual for the analysis of aphasic sentence production. Hove, UK: Psychology Press. Google Scholar Boyle, M., & Coelho, C. A. (1995). Application of semantic feature analysis as a treatment for aphasic dysnomia.American Journal of Speech-Language Pathology, 4, 94–98. LinkGoogle Scholar Breedin, S. D., Saffran, E. M., & Schwartz, M. F. (1998). Semantic factors in verb retrieval: An effect of complexity.Brain and Language, 63, 1–31. Google Scholar Byng, S. (1988). Sentence processing deficits: Theory and therapy.Cognitive Neuropsychology, 5, 629–676. CrossrefGoogle Scholar Coelho, C. A., McHugh, R. E., & Boyle, M. (2000). Semantic feature analysis as a treatment for aphasic dysnomia: A replication.Aphasiology, 14 (2), 133–142. CrossrefGoogle Scholar Conley, A., & Coelho, C. A. (2003). Treatment of word retrieval impairment in chronic Broca’s aphasia.Aphasiology, 17 (3), 203–211. Google Scholar Fink, R.B. (2001). Mapping treatment: An approach to treating sentence level impairments in agrammatism.Special Interest Division 2 Neurophysiology and Neurogenic Speech and Language Disorders, 11 (3), 14–23. AbstractGoogle Scholar Goodglass, H. (1993). Understanding aphasia. Boston: Academic Press. Google Scholar Goodglass, H., Kaplan, E., & Barresi, B. (2001). The assessment of aphasia and related disorders (3rd ed.). Philadelphia: Lippincott, Williams & Wilkins. Google Scholar Gordon, J. K. (2000). Aphasic speech errors: Spontaneous and elicited contexts. Unpublished doctoral dissertation, McGill University, Montréal, Québec. Google Scholar Gordon, J. K. (in press). A quantitative production analysis of picture description.Aphasiology. Google Scholar Gordon, J. K., & Dell, G. S. (2003). Learning to divide the labor: An account of deficits in light and heavy verb production.Cognitive Science, 27, 1–40. CrossrefGoogle Scholar Hesketh, A., & Bishop, D. V. M. (1996). Agrammatism and adaptation theory.Aphasiology, 10 (1), 49–80. Google Scholar Kim, M. (2004). Verb production in fluent aphasia: A preliminary report.Perspectives on Neurophysiology and Neurogenic Speech and Language Disorders, 14 (4), 24–27. Google Scholar Kim, M., & Thompson, C. K. (2000). Patterns of comprehension and production of nouns and verbs in agrammatism: jImplications for lexical organization.Brain and Language, 74, 1–25. Google Scholar Kim, M., & Thompson, C. K. (2004). Verb deficits in Alzheimer’s disease and agrammatism: Implications for lexical organization.Brain and Language, 88(1), 1–20. Google Scholar Kohn, S. E., Lorch, M. P., & Pearson, D. M. (1989). Verb finding in aphasia.Cortex, 25, 57–69. Google Scholar Miceli, G., Silveri, M. C., Villa, G., & Caramazza, A. (1984). On the basis for the agrammatic’s difficulty in producing main verbs.Cortex, 20, 207–220. Google Scholar Nicholas, L. E., & Brookshire, R. H. (1993). A system for quantifying the informativeness and efficiency of the connected speech of adults with aphasia.Journal of Speech & Hearing Research, 36, 338–350. AbstractGoogle Scholar Rochon, E., Saffran, E. M., Berndt, R. S., & Schwartz, M. F. (2000). Quantitative analysis of aphasic sentence production: Further development and new data.Brain and Language, 72, 193–218. CrossrefGoogle Scholar Saffran, E. M., Berndt, R. S., & Schwartz, M. F. (1989). The quantitative analysis of agrammatic production: Procedure and data.Brain and Language, 37, 440–479. CrossrefGoogle Scholar Schwartz, M. F., Saffran, E. M., Fink, R. B., Myers, J. L., & Martin, N. (1994). Mapping therapy: A treatment programme for agrammatism.Aphasiology, 8 (1), 19–54. CrossrefGoogle Scholar Williams, S. E., & Canter, G. J. (1987). Action-naming performance in four syndromes of aphasia.Brain and Language, 32, 124–136. CrossrefGoogle Scholar Zingeser, L. B., & Berndt, R. S. (1990). Retrieval of nouns and verbs in agrammatism and anomia.Brain and Language, 39 (1), 14–32. Google Scholar Additional Resources FiguresReferencesRelatedDetailsCited ByJournal of Speech, Language, and Hearing Research51:1 (S259-S275)1 Feb 2008Translational Research in Aphasia: From Neuroscience to NeurorehabilitationAnastasia M. Raymer, Pelagie Beeson, Audrey Holland, Diane Kendall, Lynn M. Maher, Nadine Martin, Laura Murray, Miranda Rose, Cynthia K. Thompson, Lyn Turkstra, Lori Altmann, Mary Boyle, Tim Conway, William Hula, Kevin Kearns, Brenda Rapp, Nina Simmons-Mackie and Leslie J. Gonzalez Rothi Volume 15Issue 4December 2005Pages: 19-23 Get Permissions Add to your Mendeley library History Published in issue: Dec 1, 2005 Metrics Downloaded 13 times Topicsasha-topicsasha-article-typesasha-sigsCopyright & Permissions© 2005 American Speech-Language-Hearing AssociationPDF DownloadLoading ...
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 | 0,000 | 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 | 0,001 |
| 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) | 0,000 | 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 ».