Detecting emotions is easier in less realistic faces.
Bibliographic record
Abstract
In investigations of emotional expression, schematic faces are attractive stimuli because they can be precisely controlled. However, these stimuli are then often treated as comparable to real faces, which could be problematic. Previous research has shown that schematic faces are processed less holistically than naturalistic faces (Prazak, E.R., & Burgund, E.D., 2014) and that the deficits autistic patients have in emotion detection may not extend to cartoon faces (Rosset, D.B., Rondan, C., Da Fonseca, D., Santos, A., Assouline, B., & Deruelle, C., 2008). These examples suggest that in some cases, cartoon faces are processed differently. We hypothesized that higher levels of schematization increase communicability of emotion. Here we tested participants in a discrimination task with emotional faces presented with varying degrees of schematization: in addition to unmanipulated greyscale photos, we applied rotoscoping software to generate two heavy outlined “cartoon” versions of the same photographs with high vs. low contrast. To vary featural complexity, simple cartoon faces underwent the same treatment to create two schematic stimulus types. The resulting stimulus set contained five types of faces - three photo-based and two schematic - which non-linearly spanned a range from photos to simple cartoons. In each trial, participants were presented with a rapidly presented face (17, 33, 50, and 66 ms) and instructed to identify which emotion was present. At 17 ms, expressions in photos were detected above chance. However, for every stimulus type moving away from photos towards cartoons, accuracy increased. That is, at shorter presentation times, as contrast increased and featural complexity decreased, discrimination became more successful. These results suggest that, as faces are represented less realistically, their informational content is more easily accessed. Moreover, our data also suggest that both contrast and featural complexity influence how easy it is to detect emotions in an image. Meeting abstract presented at VSS 2015
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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 | 0.007 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".