Item order matters in a function learning task.
Bibliographic record
Abstract
In a function learning task, participants are taught the relationship between 2 variables, a predictor (e.g., the dosage of a drug) and a criterion (e.g., its effect on mood). Of particular interest in this article is the question of what information does a participant use to generate a response for test examples that fall outside the training region-so-called, extrapolation items. In this article, we test whether the presentation of training items has an impact on the pattern of responses for items requiring participants to extrapolate, and examine, whether the 2 dominant accounts of function learning (Population of Linear Experts [POLE]: Kalish, Lewandowsky, & Kruschke, 2004; and Extrapolation Association Model [EXAM]: DeLosh, Busemeyer, & McDaniel, 1997) can account for this effect. The results show that a manipulation of trial-to-trial changes in the relative magnitudes of the predictor and criterion does influence subsequent extrapolation, and neither POLE, nor EXAM, was able to account for this effect in their current forms. We demonstrate that a model that encodes information about the trial-to-trial changes in the predictor and criterion, and which subsequently uses this information to adjust the retrieved value of the criterion, can account for the effect.
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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 | 0.011 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".