A timely reminder about stimulus display times and other presentation parameters on CRTs and newer technologies.
Bibliographic record
Abstract
Scientific experimentation requires specification and control of independent variables with accurate measurement of dependent variables. In Vision Sciences (here broadly including experimental psychology, cognitive neuroscience, psychophysics, and clinical vision), proper specification and control of stimulus rendering (already a thorny issue) may become more problematic as several newer display technologies replace cathode ray tubes (CRTs) in the lab. The present paper alerts researchers to spatiotemporal differences in display technologies and how these might affect various types of experiments. Parallels are drawn to similar challenges and solutions that arose during the change from cabinet-style tachistoscopes to computer driven CRT tachistoscopes. Technical papers outlining various strengths and limitations of several classes of display devices are introduced as a resource for the reader wanting to select appropriate displays for different presentation requirements. These papers emphasise the need to measure rather than assume display characteristics because manufacturers' specifications and software reports/settings may not correspond with actual performance. This is consistent with the call by several Vision Science and Psychological Science bodies to increase replications and increase detail in Method sections. Finally, several recent tachistoscope-based experiments, which focused on the same question but were implemented with different technologies, are compared for illustrative purposes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.017 |
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".