Knowing Its Gender Without Knowing Its Name: Differential Access to Lexical Information in a Jargonaphasic Patient
Bibliographic record
Abstract
According to recent models of word production, when we name a picture, we first retrieve the meaning of the object, and then we independently retrieve the written or sound form of the word corresponding to the picture. In languages like French, in which words have a gender, theoretical models disagree with respect to the moment at which this information is retrieved. The lemma model (Levelt et al., 1999) posits that we access this information before the sound or written form of the word is retrieved. In contrast, the"Independent network"(IN) model (Caramazza, 1997) model posits that we access gender after retrieval of either the sound or written form of the word. This paper reports a single-case study of an aphasic patient, BA, who showed deficits affecting spoken and written production in the presence of largely preserved comprehension abilities. Experimental testing indicated that she presented with a deficit functionally localized in the access to lexical representations. Results in picture naming and in gender identification also revealed that BA identified the gender above chance level, whether she produced a correct response, a phonemic error, or a neologism. In contrast, when she was unable to produce a spoken or written response, she could not identify the gender. This pattern of performance is consistent with the lemma model in which access to lexical syntax is required before access to phonological form can take place.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".