Mixed emotions: Holistic and analytic perception of facial expressions
Bibliographic record
Abstract
It is well established that recognition of facial identity relies more on Holistic Processing (HP) of the entire face than analytic processing of its constituent parts. HP of faces has been measured in experiments where participants selectively attend to the top or bottom half of faces in a same-different judgment task and either the alignment of face halves or the congruency of information across face halves are manipulated (e.g., the composite face effect, Young, Hellawell, & Hay, 1987). In contrast to identity, it is unclear whether the identification of facial expressions is holistic or analytic, as studies to date have produced conflicting results. In part this may be due to a lack of appropriate baseline measures. To measure processing of emotional expressions, we created two sets of composite faces in which top and bottom face halves displayed incongruent (e.g., angry top/happy bottom) or congruent (e.g., happy top/happy bottom) expressions and two baseline sets where expression halves were paired with halves of neutral expression, or presented in isolation. In Experiment 1, participants were asked to report the expression in the cued half of the face and ignore information in the uncued half. Relative to baseline conditions, it was found that in an incongruent expression, conflicting information in the uncued half interfered with speed and accuracy of identifying the cued half. However, in a congruent face, the uncued portion had no effect on speed and accuracy. A manipulation of the exposure duration in Experiment 2 revealed that while stimuli were equivalently identified at brief exposures, this pattern of holistic interference without facilitation emerged by 60 ms. Collectively, the results suggest that holistic face processes are engaged given both a) conflicting or ambiguous facial expression information and b) a minimum amount of processing. In contrast, unambiguous or briefly viewed facial expressions are processed analytically.
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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 | 0.003 |
| 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 | 0.001 |
| 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".