The gist of it: offloading memory does not reduce the benefit of list categorisation
Bibliographic record
Abstract
When we can offload to-be-remembered information to an external store, our ability to recall that information from internal memory can be diminished. However, previous research has suggested that associative memory processes may remain intact in the face of offloading behaviour. In the present investigation, we examine how the opportunity to offload memory demands affects the learning of categorised word lists. Across six experiments, participants studied and wrote down word lists that were either strongly associated with a semantic theme (categorised) or word lists that consisted of the same set of words but shuffled across the categorised lists (shuffled). When participants expected to have access to their written lists during the recall test (i.e., a condition that would encourage offloading) but were not given access to it, we found the typical recall advantage for categorised lists. This effect was found to be the same size or larger compared to a condition where participants did not expect to have access to their written lists during the recall test (i.e., a condition that would not allow offloading). We propose that gist memory supported by semantic associations is not substantially reduced in offloading.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".