Recency, Repetition, and the Multidimensional Basis of Recognition Memory
Bibliographic record
Abstract
Recency and repetition are two factors that have large effects on human memory performance. One way of viewing the beneficial impact of these variables on recognition memory is to assume that both factors modulate a unidimensional memory trace strength. Although previous functional neuroimaging studies have indicated that recency and repetition may modulate similar brain structures, particularly in the region of the inferior parietal cortex, there is extensive behavioral evidence that human subjects can make independent and accurate recognition memory judgments about both an item's recency and its frequency. In the present study, we used fMRI to examine patterns of brain activity during recognition memory for auditory-verbal stimuli that were parametrically and orthogonally manipulated in terms of recency and number of repetitions. We found in a continuous recognition paradigm that the lateral inferior parietal cortex, a region that has previously been associated with recollective forms of memory, is highly sensitive to recency but not repetition. In a multivariate analysis of whole-brain activation patterns, we found orthogonal components that dissociated recency and repetition variables, indicating largely independent neural bases underlying these two factors. The results demonstrate that although both recency and repetition dramatically improve recognition memory performance, the neural bases for this improvement are dissociable, and thus are difficult to explain in terms of access to a unitary memory trace.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".