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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".