The effect of unimodal affective priming on dichotic emotion recognition
Bibliographic record
Abstract
The present report concerns two experiments extending to unimodal priming the cross-modal priming effects observed with auditory emotions by Harding and Voyer [(2016). Laterality effects in cross-modal affective priming. Laterality: Asymmetries of Body, Brain and Cognition, 21, 585-605]. Experiment 1 used binaural targets to establish the presence of the priming effect and Experiment 2 used dichotically presented targets to examine auditory asymmetries. In Experiment 1, 82 university students completed a task in which binaural targets consisting of one of 4 English words inflected in one of 4 emotional tones were preceded by binaural primes consisting of one of 4 Mandarin words pronounced in the same (congruent) or different (incongruent) emotional tones. Trials where the prime emotion was congruent with the target emotion showed faster responses and higher accuracy in identifying the target emotion. In Experiment 2, 60 undergraduate students participated and the target was presented dichotically instead of binaurally. Primes congruent with the left ear produced a large left ear advantage, whereas right congruent primes produced a right ear advantage. These results indicate that unimodal priming produces stronger effects than those observed under cross-modal priming. The findings suggest that priming should likely be considered a strong top-down influence on laterality effects.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".