The influence of processing style on face perception
Bibliographic record
Abstract
A wealth of evidence suggests that face processing typically involves global (holistic) analysis but that, under some circumstances (e.g., when viewing inverted or fragmented faces), a feature-based analysis is undertaken (e.g., Farah et al., 1995). This approach may also be used when viewers process incongruent (McGurk) audiovisual stimuli; under these circumstances, viewers tend to focus disproportionately on the mouth (Paré et al., 2003). The purpose of the present experiments was to see if individual differences in processing style predict performance on tasks in which feature-based analysis of faces is likely to occur. Processing style was assessed with the Group Embedded Figures Test (GEFT, Witkin et al., 1971); high scores on this test indicate a local processing bias, while low scores indicate a global processing bias (Ellis, 1996). In the first experiment, participants completed the GEFT and a face matching task in which they were required to match a target face to one of two choice faces. The choice faces were always in the same orientation as the target, but could be shown from the same or a different viewpoint. Local processors tended to be more accurate than global processors at matching inverted (but not upright) faces; they were also more accurate at matching a target face to a choice face differing in viewing angle by 90 degrees. In the full sample, GEFT scores were positively correlated (r = .36, p = .046) with accuracy scores for matching inverted faces differing in viewing angle by 90 degrees. In the second experiment, we examined the relationship between processing style and the strength of the McGurk effect. In some conditions, local processors showed a larger McGurk effect than global processors. Together, these results lend support to the idea that individual differences in processing style affect performance with certain types of face 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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".