Applying the Model-Comparison Approach to Test Specific Research Hypotheses in Psychophysical Research Using the Palamedes Toolbox
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Bibliographic record
Abstract
In the social sciences it is common practice to test specific theoretically motivated research hypotheses using formal statistical procedures. Typically, students in these disciplines are trained in such methods starting at an early stage in their academic tenure. On the other hand, in psychophysical research, where parameter estimates are generally obtained using a maximum-likelihood (ML) criterion and data do not lend themselves well to the least-squares methods taught in introductory courses, it is relatively uncommon to see formal model comparisons performed. Rather, it is common practice to estimate the parameters of interest (e.g., detection thresholds) and their standard errors individually across the different experimental conditions and to 'eyeball' whether the observed pattern of parameter estimates supports or contradicts some proposed hypothesis. We believe that this is at least in part due to a lack of training in the proper methodology as well as a lack of available software to perform such model comparisons when ML estimators are used. We introduce here a relatively new toolbox of Matlab routines called Palamedes which allows users to perform sophisticated model comparisons. In Palamedes, we implement the model-comparison approach to hypothesis testing. This approach allows researchers considerable flexibility in targeting specific research hypotheses. We discuss in a non-technical manner how this method can be used to perform statistical model comparisons when ML estimators are used. With Palamedes we hope to make sophisticated statistical model comparisons available to researchers who may not have the statistical background or the programming skills to perform such model comparisons from scratch. Note that while Palamedes is specifically geared toward psychophysical data, the core ideas behind the model-comparison approach that our paper discusses generalize to any field in which statistical hypotheses are tested.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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