True and false recognition in MINERVA2: Integrating fuzzy-trace theory and computational memory modeling.
Bibliographic record
Abstract
Previous research suggests that the MINERVA2 model can capture basic Deese/Roediger/McDermott (DRM) false recognition findings with either randomized representations or distributional semantic representations. In the current article, we extended this line of research by showing that MINERVA2 can accommodate not only basic DRM recognition findings but also the effects of various theory-driven manipulations. Importantly, we incorporated two assumptions of fuzzy-trace theory into MINERVA2: the verbatim-gist distinction and hierarchies of gist. To implement the verbatim-gist distinction, we represented local gist traces with distributional semantic vectors and verbatim traces with holographic word-form vectors. With separate representations incorporated, MINERVA2 successfully simulated a wide range of empirical effects in the DRM illusion, as well as remember/know and source judgments. To incorporate hierarchies of gist into the framework, we added an assumption that an item's storage quality depends on its semantic similarity to the preceding item. This accommodated the effect of global gist beyond that of local gist and solved the problem of storage independence in multitrace models of episodic memory. Our findings provided extensive evidence that MINERVA2 is a viable candidate for scalable modeling of the DRM illusion and strengthened the connection between computational modeling and substantive theories of false memory. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".