The Neural Substrates of Recognition Memory for Verbal Information: Spanning the Divide between Short- and Long-term Memory
Bibliographic record
Abstract
One of the classic categorical divisions in the history of memory research is that between short-term and long-term memory. Indeed, because memory for the immediate past (a few seconds) and memory for the relatively more remote past (several seconds and beyond) are assumed to rely on distinct neural systems, more often than not, memory research has focused either on short- (or "working memory") or on long-term memory. Using an auditory-verbal continuous recognition paradigm designed for fMRI, we examined how the neural signatures of recognition memory change across an interval of time (from 2.5 to 30 sec) that spans this hypothetical division between short- and long-term memory. The results revealed that activity during successful auditory-verbal item recognition in inferior parietal cortex and the posterior superior temporal lobe was maximal for early lags, whereas, conversely, activity in the left inferior frontal gyrus increased as a function of lag. Taken together, the results reveal that as the interval between item repetitions increases, there is a shift in the distribution of memory-related activity that moves from posterior temporo-parietal cortex (lags 1-4) to inferior frontal regions (lags 5-10), indicating that as time advances, the burden of recognition memory is increasingly placed on top-down retrieval mechanisms that are mediated by structures in inferior frontal cortex.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".