The Effects of Horizontal Bias Training on Face Identification
Bibliographic record
Abstract
Horizontally oriented structure drives the accuracy of face identification: Individuals who rely more on faces’ horizontal structure relative to vertical structure (horizontal bias) perform better on facial identity discrimination tasks (Pachai et al., 2013); superior identification performance for familiar faces is linked to stronger horizontal bias (Pachai et al., 2017); and training on inverted faces leads to increased horizontal bias (Pachai et al., 2019). Although it is now established that training on faces can enhance horizontal bias, here we ask whether targeted perceptual training of horizontal structure can enhance face identification, and the extent of transfer of any learning effects. This study utilized a ten-alternative forced choice task implemented over four consecutive days. On days one and four, participants underwent baseline testing of two face sets with varying horizontal and vertical bandwidth filters; while on days two and three, participants underwent training of horizontal structure of only one of the two selected face sets and only horizontal bandwidth filters. Further, participants were separated into two training groups: one with additional uninformative vertical context added to the horizontally filtered faces, and the other with only horizontally filtered information. Preliminary data showed that horizontal training led to a decreased face identification thresholds within the trained face set. Both context present and context absent conditions led to slight transfer of learning to the untrained horizontally filtered face set. However, transfer to vertically filtered faces was seen only in the training conditions in which uninformative vertical context was included. These results provide useful insights for the development of training programs to enhance face perception.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".