'Negation-blind' N400 effect disappears when lexical priming is controlled
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| 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 | 0.000 |
| 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 it