Effects of size, fixation location, and inversion on face identification
Bibliographic record
Abstract
One possible explanation for the face inversion effect (FIE) is that inversion swaps the eye and mouth locations relative to fixation, and attention typically is directed to the top of a stimulus for faces. As the eye region is the most informative for face discrimination, automatically attending to the upper-half of a face would cause observers to use less diagnostic regions for inverted faces. Consistent with this hypothesis, cueing attention to the eyes modulates the FIE measured both behaviourally (Hills et al., JEP:HPP 2011) and with EEG (de Lissa et al., Neuropsychologia 2014). However, past studies used old/new recognition or gender discrimination tasks rather than identification tasks, and they did not consider the effects of stimulus size. The size manipulation is interesting in light of a recent suggestion that specialized face processing is engaged only by large stimuli (Yang et al., J Vis 2014). To address these issues, we measured accuracy and ERPs in a 6-AFC identification task that varied fixation location (center, left eye, right eye, mouth), orientation (upright or inverted), and face width (3.2 or 8.1 deg). Behavioural results showed significant main effects of: i) face size (higher accuracy for large faces), ii) fixation location (lower accuracy for mouth fixations), and iii) orientation (lower accuracy for inverted faces). However, we observed no fixation x orientation interaction, thus fixation location did not modulate the FIE. The size x orientation interaction also was not significant, which is inconsistent with the suggestion that small and large faces differentially recruit face-specific mechanisms. Finally, we found a significant N170 latency FIE that, consistent with previous studies, was larger with eye fixations. Together, these results clarify the roles of size and fixation in identification tasks, and further implicate the eyes in both behavioural and electrophysiological markers of face processing. 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".