Processing of Grammatical Gender in French: an Individual Differences Study
Bibliographic record
Abstract
Past studies of grammatical gender have shown that native speakers encounter processing difficulties when encountering a form that does not agree in gender with previous words. However, the specific behavioral and neural responses to these difficulties have not been replicated across studies of the same type. This is in part due to different experimental designs and statistical analyses, but a crucial factor may be the lack of control between nouns of masculine and feminine gender in stimuli creation. Masculine and feminine gender show distinct distributional asymmetries and collapsing them into one condition diminishes the explanatory power of any study examining grammatical gender. We used reading times in a self-paced reading experiment to examine whether masculine and feminine gender violations differentially affect processing speeds. Fifty French speakers read sentences that were well-formed or contained a mismatch in gender between determiner and noun, half of which were masculine and half feminine. Following Beatty-Martínez et al. (2021), we added individual difference measures to determine how participant-specific factors modulate processing. Participants also completed a category verbal fluency task and the AX-CPT, a measure of cognitive control. They found that ERP components were modulated by these components for Spanish speakers and the modulation differed between masculine noun and feminine noun violations. We hypothesized that reading times would be similarly affected in French, a closely related language with the same gender categories. However, no conditions or interactions reached statistical significance. It is unclear whether this is due to the experimental manipulation or lack of control for participants’ language background, as we had a high number of bilingual and multilingual participants. Regardless, elements of the procedure may provide insight on how to design future experiments that lay a groundwork in understanding the most basic elements of gender processing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".