A Cognitively Plausible Visual Working Memory Model
Bibliographic record
Abstract
Visual working memory (VWM) plays a fundamental role in cognitive processes, such as perception, attention, and reasoning. However, existing approaches to modelling VWM are not integrated into cognitive architectures and lack interpretability with respect to their parameters. To address this limitation, we propose a novel VWM model based on the well-established Semantic Pointer Architecture (SPA). In contrast to previous works, our model is the first to integrate a VWM model with a cognitive attention model. It only requires three interpretable hyper-parameters: spatial capacity, feature certainty, and memory decay. We experimentally show that our base model without memory decay replicates the set-size effect and swap errors of human data on a continuous reproduction task. More importantly, we show that by introducing a memory decay, we can achieve a statistically significant (p ≪ 0.001) improvement in model fit, suggesting a potentially important role of memory decay in VWM. Further, our VWM model can be easily extended to model pre- and post-cue conditions, consistently achieving KL divergence between modelled and human performance of less than 0.05.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".