The representation of stimulus conjunction in theories of associative learning: A context-dependent added-elements model.
Bibliographic record
Abstract
This article briefly reviews 3 theories concerning elemental and configural approaches to stimulus representation in associative learning and presents a new context-dependent added-elements model (C-AEM). This model takes an elemental approach to stimulus representation where individual stimuli are represented by single units and stimulus compounds activate both those units and configurational units corresponding to each conjunction of 2 or more stimuli. Activity across these units is scaled such that each stimulus always contributes the same amount of activity to the system whether it is presented in isolation or in compound; the configurational units "borrow" activity from representational units for individual stimuli (and from each other). This scaling is affected by the extent to which stimuli interact with each other perceptually. Hence, the model is conceptually similar to Wagner's (2003) replaced elements model but lacks features that explicitly code for the absence of stimuli (i.e., inhibited elements). Simulations of the model are reported for a range of generalization and discrimination learning tasks, conflicting results from which have previously been taken to provide support for either configural or elemental theories of learning. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".