Is absolute identification always relative? Comment on Stewart, Brown, and Chater (2005).
Bibliographic record
Abstract
N. Stewart, G. D. A. Brown, and N. Chater's relative judgment model includes three core assumptions that enable it to predict accurately the vast majority of "classical" phenomena in absolute identification choices, but not the time taken to make them, including sequential effects, such as assimilation and contrast. These core assumptions, coupled with the parameter values used in the above-mentioned article, lead to the prediction that identification accuracy is low when a large stimulus on 1 trial is followed by a small stimulus on the next trial and vice versa. Data do not support this prediction. The authors identify a set of parameters that allow the model to better fit the data, but problems remain when the data are analyzed with a version of the discrimination measure (d') from signal detection theory. The fundamental problem is that the model fits data on average but at the expense of making incorrect predictions in detail.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.044 | 0.036 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".