Optimal viewing positions for upright and inverted face recognition
Bibliographic record
Abstract
Upright faces are easier to recognise than inverted faces. Eye-tracking studies have shown that the same pattern of ocular fixations across the stimulus are obtained with inverted and upright faces, suggesting that the inversion effect cannot be explained by a difference in the features fixated as a function of orientation (Williams & Henderson, 2007; but see Barton, Radcliffe, Cherkasova, Edelman & Intriligator, 2006). One possibility, however, is that the areas fixated with inverted faces are not optimal for recognition, in contrast to fixations with upright faces. Here, we tested this hypothesis using the optimal viewing position paradigm. Five participants were first familiarized with the stimulus set, made of the faces of five female and five male famous actors. First, the exposure duration needed by each participant to identify upright faces centered at fixation with an accuracy of 90% was determined using QUEST (Watson & Pelli, 1983). Then, upright or inverted faces were displayed for this duration (less than 100 ms for all subjects) at random positions within a distance of 7.8 deg of visual angle horizontally and 11.7 deg of visual angle vertically relative to fixation. A mask made of the average of the ten faces in the stimulus set was displayed immediately after target offset. Participants were asked to identify the target face. Each participant completed 3,000 trials for each orientation. We then determined correct response probabilities as a function of viewing position. The results show, for example, that the optimal viewing area is smaller for inverted than for upright faces. Implications of these results for the face inversion effect will be discussed (e.g., Sekuler, Gaspar, Gold, & Bennett, 2004; Willenbockel et al., 2008).
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".