The relative psychometric function: A general analysis framework for relating psychological processes.
Bibliographic record
Abstract
Psychophysics seeks to quantitatively characterize relationships between objective properties of the world and subjective properties of perception. However, traditional approaches investigate psychophysical dependencies of perception on stimulus properties on a case by case basis rather than seeking to identify quantitative relationships among these psychological processes themselves. This latter goal is particularly important when the processes in question likely depend on each other in some way, such as is the case for subjective experience and task performance: typically, stronger physical stimuli lead to better performance and stronger subjective experiences of clarity, vividness, or confidence. But is the relationship between performance and subjective experience fixed, or can it vary, e.g. by task or attentional demands? Such questions are key for better understanding psychological processes in general, and subjective experience in particular. Here, we develop and showcase a new psychophysical method designed to answer such questions: relative psychometric function (RPF) analysis, which characterizes the nonlinear psychometric relationships between psychological processes and how these relationships change under different circumstances (e.g. experimental manipulations). We demonstrate the advantages of RPF analysis using a sample dataset in which human subjects discriminated random dot kinematogram stimuli which varied in dot motion coherence and overall dot density (dots per visual degree), and rated confidence. RPF analysis revealed systematic changes in the relationship between performance and two subjective measures (confidence and metacognitive sensitivity) due to dot density and task design choices. While these empirical results are intriguing in their own right, they also show how RPF analysis can reveal changes in quantitative relationships between any two psychological measures: performance, vividness, clarity, reaction time, confidence, and more. To encourage the scientific community to use RPF analysis on their data, we also present our open-source RPF toolbox.
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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 | 0.027 | 0.093 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".