Conducting online visual psychophysics experiments: A replication assessment of two face processing studies
Bibliographic record
Abstract
In vision sciences, researchers rigorously control the testing environment and the physical properties of stimuli, making it challenging to conduct visual perception experiments online. However, online research offers key advantages, including access to larger and more diverse participant samples, helping to address the problem of underpowered studies and to enhance the generalizability of results. In face recognition research, increasing diversity is essential, especially considering evidence that cultural and geographical factors influence basic visual face processing. The present study tested a new online platform, Pack & Go from VPixx Technologies, that supports experiments written in MATLAB and Python. Two face recognition experiments based on a data-driven psychophysical method involving real-time stimulus manipulation and relying on functions from the Psychtoolbox were tested. In Experiment 1, the visual information used for face recognition was compared across four conditions that gradually reduced experimental control over the testing environment and stimulus properties. In Experiment 2, the association between face recognition abilities and information utilization was measured online and compared to lab-based results. In both experiments, results obtained in the lab and online were highly similar, demonstrating the potential of online research for vision science.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".