Associations and Dissociations: An Investigation of Lexical Access Deficits in Agrammatism and Anomia
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Abstract
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). 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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 ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".