A Dynamic Neural Gradient Model of Two-Item and Intermediate Transposition
Bibliographic record
Abstract
Transposition is a tendency for organisms to generalize relationships between stimuli in situations where training does not objectively reward relationships over absolute, static associations. Transposition has most commonly been explained as either conceptual understanding of relationships (Köhler, 1938) as nonconceptual effects of neural memory gradients (as in Spence's stimulus discrimination theory, 1937 ). Most behavioral evidence can be explained by the gradient account, but a key finding unexplained by gradients is intermediate transposition, where a central (of three) stimulus, "relationally correct response," is generalized from training to test. Here, we introduce a dynamic neural field (DNF) model that captures intermediate transposition effects while using neural mechanisms closely resembling those of Spence's original proposal. The DNF model operates on dynamic rather than linear neural relationships, but it still functions by way of gradient interactions, and it does not invoke relational conceptual understanding in order to explain transposition behaviors. In addition to intermediate transposition, the DNF model also replicates the predictions of stimulus discrimination theory with respect to basic two-stimulus transposition. Effects of wider test item spacing were additionally captured. Overall, the DNF model captures a wider range of effects in transposition than stimulus discrimination theory, uses more fully specified neural mechanics, and integrates transposition into a wider modeling effort across cognitive tasks and phenomena. At the same time, the model features a similar low-level focus and emphasis on gradient interactions as Spence's, serving as a conceptual continuation and updating of Spence's work in the field of transposition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".