Signal Detection Measures Cannot Distinguish Perceptual Biases from Response Biases
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Bibliographic record
Abstract
A common conceptualization of signal detection theory (SDT) holds that if the effect of an experimental manipulation is truly perceptual, then it will necessarily be reflected in a change in d' rather than a change in the measure of response bias. Thus, if an experimental manipulation affects the measure of bias, but not d', then it is safe to conclude that the manipulation in question did not affect perception but instead affected the placement of the internal decision criterion. However, the opposite may be true: an effect on perception may affect measured bias while having no effect on d'. To illustrate this point, we expound how signal detection measures are calculated and show how all biases-including perceptual biases-can exert their effects on the criterion measure rather than on d'. While d' can provide evidence for a perceptual effect, an effect solely on the criterion measure can also arise from a perceptual effect. We further support this conclusion using simulations to demonstrate that the Müller-Lyer illusion, which is a classic visual illusion that creates a powerful perceptual effect on the apparent length of a line, influences the criterion measure without influencing d'. For discrimination experiments, SDT is effective at discriminating between sensitivity and bias but cannot by itself determine the underlying source of the bias, be it perceptual or response based.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.020 | 0.005 |
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