A study of the effect of auditory prime type on emotional facial expression recognition
Bibliographic record
Abstract
Background: In this study, we investigated the influence of two types of emotional auditory primes - vocalizations and pseudoutterances - on the ability to judge a subsequently presented emotional facial expression in an event-related potential (ERP) study using the facial-affect decision task. We hypothesized that accuracy would be greater for congruent trials than for incongruent trials. This is due to the possibility that a congruent prime would allow the listener to implicitly identify the particular emotion of the face more effectively. We also hypothesized that the normal priming effect would be observed in the N400 for both prime types, i.e. a greater negativity for incongruent trials than for congruent trials. Methods: Emotional primes (vocalization or pseudoutterance) were presented to participants who were then asked to make a judgment regarding whether or not a facial expression conveyed an emotion. Behavioural data on participant accuracy and experimental electroencephalogram (EEG) data were collected and subsequently analyzed for six participants. Results: Behavioural results showed that participants were more accurate in judging faces when primed with vocalizations than pseudoutterances. ERP results revealed that a normal priming effect was observed for vocalizations in the 150 msec - 250 msec temporal window – where greater negativities were produced during incongruent trials than during congruent trials – whereas the reverse effect was observed for pseudoutterances. Few participants were tested (n = 7). Hence, this study is a pilot study preceding a further study conducted with a greater sample size (n = 25) and slight modifications in the methodology (such as the duration of auditory primes.) Conclusions: Vocalizations showed the expected priming effect of greater negativities for incongruent trials than for congruent trials, while pseudoutterances unexpectedly showed the opposite effect. These results suggest that vocalizations may provide more prosodic information in a shorter time and thereby generate the expected congruency effect.
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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 | 0.006 |
| 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) | 0.003 | 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".