The psychophysics of contingency assessment.
Bibliographic record
Abstract
The authors previously described a procedure that permits rapid, multiple within-participant evaluations of contingency assessment (the "streamed-trial" procedure, M. J. C. Crump, S. D. Hannah, L. G. Allan, & L. K. Hord, 2007). In the present experiments, they used the streamed-trial procedure, combined with the method of constant stimuli and a binary classification response, to assess the psychophysics of contingency assessment. This strategy provides a methodology for evaluating whether variations in contingency assessment reflect changes in the participant's sensitivity to the contingency or changes in the participant's response bias (or decision criterion). The sign of the contingency (positive or negative), outcome density, and imposition of an explicit payoff structure had little influence on sensitivity to contingencies but did influence the decision criterion. The authors discuss how a psychophysical analysis can provide a better understanding of findings in the literature such as mood and age effects on contingency assessment. They also discuss the relation between a psychophysical approach and an associative account of contingency assessment.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".