Bibliographic record
Abstract
Two highly studied memory functions are memory for associations (items presented in pairs, such as SALT-PEPPER) and memory for order (a list of items whose order matters, such as a telephone number). Order- and association-memory are at the root of many forms of behaviour, from wayfinding, to language, to remembering people's names. Most researchers have investigated memory for order separately from memory for associations. Exceptions to this, associative-chaining models build an ordered list from associations between pairs of items, quite literally understanding association- and order-memory together. Alternatively, positional-coding models have been used to explain order-memory as a completely distinct function from association-memory. Both classes of model have found empirical support and both have faced serious challenges. I argue that models that combine both associative chaining and positional coding are needed. One such hybrid model, which relies on brain-activity rhythms, is promising, but remains to be tested rigourously. I consider two relatively understudied memory behaviours that demand a combination of order- and association-information: memory for the order of items within associations (is it William James or James William?) and judgments of relative order (who left the party earlier, Hermann or William?). Findings from these underexplored procedures are already difficult to reconcile with existing association-memory and order-memory models. Further work with such intermediate experimental paradigms has the potential to provide powerful findings to constrain and guide models into the future, with the aim of explaining a large range of memory functions, encompassing both association- and order-memory.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".