The role of cognitive load in automatic integration of emotional information from face and body
Bibliographic record
Abstract
We perceive emotions daily through facial expressions, often accompanied by a body posture that provide additional emotional context. Congruent facial and bodily expressions (conveying the same emotion) enhance emotional recognition compared to incongruent ones, suggesting interaction between these channels. Although behavioral evidence suggests that this integration occur automatic, its underlying neural mechanisms remains unclear. This study investigated the automaticity of facial and bodily expressions integration by manipulating cognitive load. Twenty-eight participants completed an emotion recognition task with congruent or incongruent facial and bodily expressions while performing a memory task under low or high cognitive load. EEG recordings captured brain activity, and emotion recognition accuracy and reaction times were measured. Results revealed that congruent expressions improved recognition, with bodily expressions exerting a stronger influence on facial expression recognition than vice versa. Early neural responses (P100, N100, P250, N250) were stronger during facial expression focus, while later responses reflected attention to body expressions. Bayesian analyses provided strong evidence for the absence of significant interaction between congruence and cognitive load, supporting the automaticity of integration. These findings suggest that emotional expressions are integrated automatically, independent of cognitive resources, and emphasize the differential influence of bodily expressions over facial expressions in shaping emotional perception.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".