Continuous psychophysics for two-variable experiments; A new “Bayesian participant” approach
Why this work is in the frame
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Bibliographic record
Abstract
Interest in continuous psychophysical approaches as a means of collecting data quickly under natural conditions is growing. Such approaches require stimuli to be changed randomly on a continuous basis so that participants can not guess future stimulus states. Participants are generally tasked with responding continuously using a continuum of response options. These features introduce variability in the data that is not present in traditional trial-based experiments. Given the unique weaknesses and strengths of continuous psychophysical approaches, we propose that they are well suited to quickly mapping out relationships between above-threshold stimulus variables such as the perceived direction of a moving target as a function of the direction of the background against which the target is moving. We show that modelling the participant in such a two-variable experiment using a novel "Bayesian Participant" model facilitates the conversion of the noisy continuous data into a less-noisy form that resembles data from an equivalent trial-based experiment. We also show that adaptation can result from longer-than-usual stimulus exposure times during continuous experiments, even to features that the participant is not aware of. Methods for mitigating the effects of adaptation are discussed.
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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 | 0.000 | 0.000 |
| 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 it