Fluid Abilities and Rule Learning: Patterning and Biconditional Discriminations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous experience with discrimination problems that can only be solved by learning about stimulus configurations enhances performance on new configural discriminations. Some of these effects can be explained by a shift toward increased configural processing (learning about combinations of cues rather than about individual elements), or by a tendency to generalize a learned rule to a new training set. We investigated whether fluid abilities influence the extent that previous experience with configural discriminations improves performance on subsequent discriminations. In Experiments 1 and 2 we used patterning discriminations that could be solved by applying a simple rule, whereas in Experiment 3 we used biconditional discriminations that could not be solved using a rule. Fluid abilities predicted the improvement on the second training set in all experiments, including Experiment 3 in which rule-based generalization could not explain the improvement on the second discrimination. This supports the idea that fluid abilities contribute to performance by inducing a shift toward configural processing rather than rule-based generalization. However, fluid abilities also predicted performance on a rule-based transfer test in Experiment 2. Taken together, these results suggest that fluid abilities contribute to both a flexible shift toward configural processing and to rule-based generalization.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it