Functional connectivity during facial and vocal emotion recognition: Preliminary evidence for dissociations in developmental change by nonverbal modality
Bibliographic record
Abstract
The developmental trajectory of emotion recognition (ER) skills is thought to vary by nonverbal modality, with vocal ER becoming mature later than facial ER. To investigate potential neural mechanisms contributing to this dissociation at a behavioural level, the current study examined whether youth's neural functional connectivity during vocal and facial ER tasks showed differential developmental change across time. Youth ages 8-19 (n = 41) completed facial and vocal ER tasks while undergoing functional magnetic resonance imaging, at two timepoints (1 year apart; n = 36 for behavioural data, n = 28 for neural data). Partial least squares analyses revealed that functional connectivity during ER is both distinguishable by modality (with different patterns of connectivity for facial vs. vocal ER) and across time-with changes in connectivity being particularly pronounced for vocal ER. ER accuracy was greater for faces than voices, and positively associated with age; although task performance did not change appreciably across a 1-year period, changes in latent functional connectivity patterns across time predicted participants' ER accuracy at Time 2. Taken together, these results suggest that vocal and facial ER are supported by distinguishable neural correlates that may undergo different developmental trajectories. Our findings are also preliminary evidence that changes in network integration may support the development of ER skills in childhood and adolescence.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".