N400 processes inhibit inappropriately activated representations: Adding a piece of evidence from a high-repetition design
Bibliographic record
Abstract
The N400 event-related potential could index the activation/integration of representations corresponding to the stimulus or, on the contrary, the inhibition of representations that have been inappropriately activated. To test this alternative, series of 3 words were visually presented to subjects in a relatively rapid succession in order to prevent any disengagement of attention. In one block, participants had to judge whether the meaning of the 1st word was related to that of the 3rd. Representations activated by the 2nd word were thus inappropriate and had to be ignored. In another block, these representations were task appropriate as subjects were asked to decide whether the meaning of the 2nd word was related to that of the 3rd. The new technique of massive repetitions was used in order to obtain early peaking and short lasting N400 effects that would be easier to distinguish from effects on the contingent negative variations (CNVs) triggered by the expectancy of 3rd words. The ERPs elicited by 2nd words were more negative in the N400 time window when their meanings were task inappropriate than when these meanings had to be used. These differences were maximal at the latency of the peak of the N400 deflection rather than at the latency of the maximum of the late positive complex or at that of the CNV. They appeared to be greater at centro-parietal sites and slightly larger over the right than over the left hemiscalp. The results thus bring further support to the idea that N400 processes are of an inhibitory nature.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 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".