A systematic exploration of model-mechanisms for interactions between item- and association-memory in paired-associate learning
Bibliographic record
Abstract
Paired-associate learning paradigms are extremely com-mon in memory research; however, memory behaviourin these paradigms relies on both memory for items andfor their pairings. We recently developed an experimen-tal paradigm that is able to dissociating effects of item-and association-memory with cued recall [1,2]. Severalmathematical modeling frameworks have been appliedsuccessfully to PA empirical phenomena. However, ournew behavioural results demand that these memorymodels be developed further, in order to identify lociwithin the major models where item-level versus asso-ciation-level effects could materialize. Here we present asystematic approach to modeling item- versus associa-tion-memory effects in PA learning, with a specificfocus on comparing memory modeling frameworks(including the Matrix model [3,4], TODAM [5], andBSB [6,7]).MethodsWe propose a generative model of PA learning basedupon the distributed memory model frameworks pro-posed by the matrix model [3,4] and convolution-corre-lation memory models [5] of associative learning. Tofurtherdefineourmodelweemploythebrain-state-in-a-box model [6,7] as our deblurring mechanism toincrease ecological validity of our model, as opposed toa heuristic such as the winner-take-all choice rule. Herewe model how item- and association-memory manipula-tions may modulate within memory performance in acued recall task. In particular, we ask whether manipula-tions of material-type can passively result in strongerassociations (i.e., without requiring the participant tovary their strategy). For example, current modelingresults demonstrate how items that are learned strongerduring study can result in better association-memory.This modeling approach represents a framework for arange of PA learning effects that have already beenreported (e.g., [1,2]) as well as predicting as-of-yet unob-served patterns. Simulations of how manipulations ofmaterial-type can modulate item- and association-mem-ory have not yet been theoretically explored and canhave profound implications to current memory models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".