'Negation-blind' N400 effect disappears when lexical priming is controlled
Bibliographic record
Abstract
Previous ERP studies showed that false affirmative sentences elicited a larger N400 than their true versions, but they found the reverse pattern when the sentences were of negative form as if N400 was blind to negation. This negation-blind N400 pattern arguably constituted evidence for two-step accounts of negation processing: When processing negative sentences, a comprehender first computes an internal proposition and then considers the negation. However, the prior studies were confounded by a lexical priming relation between subject and object. Therefore, it was an open question whether or not the observed ERP pattern really reflected the two-step process. To tackle this question, we conducted an ERP experiment, using size-comparison statements where subjects and objects are semantically unrelated. This design allowed us to remove the priming confound. We predicted that if the previous negation-blind N400 pattern is unrelated to lexical priming, it would be replicated; if not, it would disappear. The result was consistent with the second prediction. This suggests that the previously observed negation-blind N400 pattern does not necessarily constitute evidence for two-step accounts of negation 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".