The face superiority test: A novel method of studying face perception
Bibliographic record
Abstract
Aims/Purpose: There are several studies that compare written word and face perception. However, many draw conclusions upon different experimental paradigms, complicating direct comparison between these stimuli. We aim to create a novel paradigm studying face recognition that closely resembles the word‐superiority test. Methods: 40 subjects participated in the study. Each completed both a traditional word‐superiority test and our novel face‐superiority test. In the face‐superiority tests, patients were presented with either a familiar face (real), an unfamiliar face (pseudo‐), or a scrambled face (non‐) initially, then presented with a face feature in isolation and tasked to respond with whether the feature was present in the aforementioned face. Statistical analyses and bivariate correlation analyses were conducted to identify relationships in intra‐ and inter‐stimulus trials. Results: For both categories of stimuli, there were similar differences between non‐, pseudo‐, and real stimuli. Accuracy was lower for non‐stimuli compared to pseudo‐ and real stimuli, which in turn did not differ between each other. There was greater response latency for non‐stimuli compared to pseudo‐stimuli, which in turn was greater than real stimuli. Bivariate analyses revealed significant correlations between inter‐stimulus trials for reaction times. Conclusions: Our study was able to replicate a face superiority effect utilizing a similar methodology from the word‐superiority test. Additionally, we provide evidence that response latency follows similar patterns in the recognition of written words and faces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".