Bibliographic record
Abstract
It is well established that two predictor cues (A and B) of a common outcome interact in that the judgement of the relationship between each cue and the outcome is influenced by the pairing history of the other cue with that outcome. For example, when the contingency of A with an outcome is weaker than the contingency of B with that outcome, the rating of the predictiveness of A is reduced relative to a situation where only A is paired with the outcome. One explanation of such cue interaction effects is provided by the conditional deltaP account. Spellman (1996b) derived a counterintuitive prediction of the conditional deltaP account where cue interaction should not occur under certain conditions even though a relatively poor predictor of an outcome is paired with a relatively good predictor of that outcome. However, Spellman (1996b) did not provide data to evaluate this prediction. In the present paper, we report the relevant data and show that they are consistent with the conditional deltaP account. A competing account of cue interaction is provided by the Rescorla-Wagner (RW) model. We derive the predictions of the RW model for the conditions specified by Spellman (1996b), and show that at asymptote the predictions of the RW model are identical to those of the conditional deltaP account.
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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 | 0.004 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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".