An embedded computational framework of memory: The critical role of representations in veridical and false recall predictions
Bibliographic record
Abstract
Human memory is reconstructive and thus fundamentally imperfect. One of its critical flaws is false recall-the erroneous recollection of unstudied items. Despite its significant implications, false recall poses a challenge for existing computational models of serial recall, which struggle to provide item-specific predictions. Across six experiments, each involving 100 young adults, we address this issue using the Embedded Computational Framework of Memory (eCFM) that integrates existing accounts of semantic and episodic memory. While the framework provides a comprehensive account of memory processing, its innovation lies in the inclusion of a comprehensive lexicon of word knowledge derived from distributional semantic models. By integrating a lexicon that captures orthographic, phonological, and semantic relationships within an episodic memory model, the eCFM successfully accounts for patterns of veridical serial recall (e.g., proportion correct, intralist errors, omissions) while also capturing false recall (e.g., extralist errors including both critical lures and non-critical lures). We demonstrate the model's capabilities through simulations applied to six experiments, with lists of words (Experiments 1A, 1B, 2A, and 2B) and non-words (Experiments 3A and 3B) that are either related or unrelated semantically (Experiments 1A and 1B), phonologically (Experiments 2A and 2B), or orthographically (Experiments 3A and 3B). This approach fills a computational gap in modelling serial recall and underscores the importance of integrating traditionally separate areas of semantic and episodic memory to provide more precise predictions and holistic memory models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".