The influence of recollection and familiarity in the formation and updating of associative representations
Bibliographic record
Abstract
Prior representations affect future learning. Little is known, however, about the effects of recollective or familiarity-based representations on such learning. We investigate the ability to reuse or reassociate elements from recollection- and familiarity-based associations to form new associations. Past neuropsychological research suggests that hippocampal, and presumably recollective, representations are more flexible than extra-hippocampal, presumably familiarity-based, representations. We therefore hypothesize that the elements of recollective associations, as opposed to familiarity-based representations, may be more easily manipulated and decoupled from each other, and facilitate the formation of new associations. To investigate this hypothesis we used the AB/AC learning paradigm. Across two recall studies we observed an advantage in learning AC word pairs if AB word pairs were initially recollected. Furthermore, AB word pairs were more likely to intrude during a final AC test if those AB word pairs were initially familiarity-based. A third experiment using a recognition version of the AB/AC paradigm ruled out the possibility that our findings were due to memory strength. Our results support the idea that elements in recollective associative traces may be more discretely coded, leading to their flexible use, whereas elements in familiarity-based associative traces are less flexible.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".