Iconic faces are not real faces: enhanced emotion detection and altered neural processing as faces become more iconic
Bibliographic record
Abstract
Iconic representations are ubiquitous; they fill children's cartoons, add humor to newspapers, and bring emotional tone to online communication. Yet, the communicative function they serve remains unaddressed by cognitive psychology. Here, we examined the hypothesis that iconic representations communicate emotional information more efficiently than their realistic counterparts. In Experiment 1, we manipulated low-level features of emotional faces to create five sets of stimuli that ranged from photorealistic to fully iconic. Participants identified emotions on briefly presented faces. Results showed that, at short presentation times, accuracy for identifying emotion on more "cartoonized" images was enhanced. In addition, increasing contrast and decreasing featural complexity benefited accuracy. In Experiment 2, we examined an event-related potential component, the P1, which is sensitive to low-level visual stimulus features. Lower levels of contrast and complexity within schematic stimuli were also associated with lower P1 amplitudes. These findings support the hypothesis that iconic representations differ from realistic images in their ability to communicate specific information, including emotion, quickly and efficiently, and that this effect is driven by changes in low-level visual features in the stimuli.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".