Does face inversion qualitatively change face processing: An eye movement study using a face change detection task
Bibliographic record
Abstract
Understanding the Face Inversion Effect is important for the study of face processing. Some researchers believe that the processing of inverted faces is qualitatively different from the processing of upright faces because inversion leads to a disproportionate performance decrement on the processing of different kinds of face information. Other researchers believe that the difference is quantitative because the processing of all kinds of facial information is less efficient due to the change in orientation and thus, the performance decrement is not disproportionate. To address the Qualitative and Quantitative debate, the current study employed a response-contingent, change detection paradigm to study eye movement during the processing of upright and inverted faces. In this study, configural and featural information were parametrically and independently manipulated in the eye and mouth region of the face. The manipulations for configural information involved changing the interocular distance between the eyes or the distance between the mouth and the nose. The manipulations for featural information involved changing the size of the eyes or the size of the mouth. The main results showed that change detection was more difficult in inverted than upright faces. Specifically, performance declined when the manipulated change occurred in the mouth region, despite the greater efforts allocated to the mouth region. Moreover, compared to upright faces where fixations were concentrated on the eyes and nose regions, inversion produced a higher concentration of fixations on the nose and mouth regions. Finally, change detection performance was better when the last fixation prior to response was located on the region of change, and the relationship between last fixation location and accuracy was stronger for inverted than upright faces. These findings reinforce the connection between eye movements and face processing strategies, and suggest that face inversion produces a qualitative disruption of looking behavior in the mouth region.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".