Brain potentials reveal differential processing of masculine and feminine grammatical gender in native Spanish speakers
Bibliographic record
Abstract
Studies of Spanish grammatical gender have shown that native speakers exploit gender cues in determiners to facilitate speech processing and are sensitive to gender mismatches. However, past research has not considered attested distributional asymmetries between masculine and feminine gender, collapsing performance on trials with one or the other gender into a single analysis. We use event-related potentials to investigate whether masculine and feminine grammatical gender elicit qualitatively different brain responses. Forty monolingual Spanish speakers read sentences that were well-formed or contained determiner-noun gender violations. Half of the nouns were masculine and the other half were feminine. Consistent with previous research, brain responses varied along a continuum between LAN- and P600-dominant effects for both gender categories. However, results showed that individuals' ERP response dominance (LAN/P600) systematically differed across the two genders: participants who showed a LAN-dominant response to masculine-noun violations were more likely to show a P600 effect in response to feminine-noun violations. Correlations with individual difference measures further revealed that responses to masculine-noun violations were modulated by performance on the AX-CPT, a measure of cognitive control, whereas responses to feminine-noun violations were modulated by lexical knowledge, as indexed by verbal fluency. Together, the results demonstrate that even when processing features of language that belong to the same "natural class," native speakers can exhibit patterns of brain activity attuned to distributional patterns of language use. The inherent variability in native speaker processing is, therefore, an important factor when explaining purported deviations from the "native norm" reported in other types of populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".