The influence of horizontal structure on face identification as revealed by noise masking
Bibliographic record
Abstract
Dakin and Watt (J Vis., 2009, 9(4):2, 1-10) suggested that face identity is conveyed primarily by the horizontal structure in a face. We evaluated this hypothesis using upright and inverted faces masked with orientation filtered Gaussian noise. Observers completed a 10-AFC identification task that used faces that varied slightly in viewpoint. Face stimuli were presented in horizontal and vertical noise, and in a noiseless baseline condition. Both face and noise orientation were varied within subjects, with face orientation blocked and counter-balanced across two sessions and noise orientation varying within each session. We measured 71% correct RMS contrast thresholds for each condition and then converted the thresholds into masking ratios defined as the logarithm of the ratio of the masked and unmasked thresholds. There was a significant effect of noise orientation for upright faces (F(1,11)=5.162, p<0.05), with horizontal noise producing more masking than vertical. However, this effect did not appear for inverted faces. In a second experiment, we found that the pattern of masking did not change significantly with the RMS contrast of the masking noise (F(2,4)=1.013, P>0.4). Finally, we simulated the performance of Dakin and Watt's so-called barcode observer for our experimental conditions, and found that the predictions of the model were consistent with the masking data obtained with upright faces. Together, these data suggest that observers may indeed identify faces preferentially using the horizontal structure in the stimulus.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".