Lateralized Affective Word Priming and Gender Effect
Bibliographic record
Abstract
Affective priming research suggests that processing of affective words is a quick and short lived process. Using the divided visual field (DVF) paradigm, investigations of the lateralization of affective word processing have yielded inconsistent results. However, research on semantic processing of words generally suggests that the left hemisphere (LH) is the location where rapid processing occurs. We investigated the processing of affective (emotional) words using a combination of the DVF and affective priming paradigms, and four stimulus onset asynchronies (SOAs)—0, 150, 300, and 750 ms. The priming pattern yielded by males (n=32) showed quick priming (at 0-ms SOA) of affective words in the LH; there was slower right hemisphere (RH) priming of affective words (at 750-ms SOA). In females (n=28), both hemispheres were associated with quick priming of affective words (at 300-ms SOA in the LH and at 150-ms SOA in the RH). Results demonstrate the capability of both cerebral hemispheres in the processing of words with affective meaning, along with leading role of the left hemisphere in this process. This is similar to the results of semantic research that suggest access to word meanings occurs in both hemispheres, but different mechanisms might be involved. While the LH seems to prime affective words quickly regardless of gender, gender differences are likely in the RH in that affective word processing probably occurs slowly in males but rapidly in females. This gender difference may result from increased sensitivity to the emotional feature of affective words in females.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".