Effects of Dispositional Affect on the N400: Language Processing and Socially Situated Context
Bibliographic record
Abstract
We examined whether the N400 Event-Related Potential (ERP) component would be modulated by dispositional affect during sentence processing. In this study, 33 participants read sentences manipulated by direct object type (congruent vs. incongruent) and object determiner type (definite vs. demonstrative). We were particularly interested in sentences of the form: (i) The connoisseur tasted thewineon the tour vs. (ii) The connoisseur tasted the #roof… We expected that processing incongruent direct objects (#roof) vs. congruent objects (wine) would elicit N400 effects. Previous ERP language experiments have shown that participants in (induced) positive and negative moods were differentially sensitive to semantic anomaly, resulting in different N400 effects. Presently, we ask whether individual dispositional affect scores (as measured by the Positive and Negative Affect Schedule; PANAS) would modulate N400 effects as shown previously. Namely, previous results showed larger N400 effects associated with happy moods and attenuated amplitudes associated with sad moods. Results revealed significant N400 effects, driven by the #roof vs. the wine, where larger amplitude differences were found for individuals showing smaller negative affect (NA) scores, thus partially replicating previous findings. We discuss our results in terms of theories of local (lexical) inhibition, such that low NA promotes stronger lexico-semantic links in sentences. Finally, our results support accounts of language processing that include social and biological characteristics of individuals during real-time sentence comprehension.
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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 | 0.000 | 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".