The interactive effects of divided attention and semantic elaboration on associative recognition memory: an fMRI study
Bibliographic record
Abstract
The present study explored the opposing effects on memory of semantic elaboration and division of attention on learning and recognition of verbal paired associates. Previous work had found that levels of recollection were reduced under divided attention conditions, even after equating expressed elaboration levels between full and divided attention. The present experiments not only confirmed this finding but also found that participants based their expressed levels of elaboration largely on normative values rather than on subjectively achieved levels of elaboration. In terms of related brain processes, experiment 2 used functional magnetic resonance to show that division of attention was associated with reduced levels of both prefrontal and hippocampal activity and with a reduction in connectivity between the anterior hippocampus and medial-orbital regions of the prefrontal cortex. Increased levels of elaboration were associated with increased activity in prefrontal regions immediately after stimulus presentation. Additionally, connectivity between the hippocampus and medial-prefrontal cortex was enhanced by increases in elaboration under full attention but reduced by increases in elaboration under conditions of divided attention. Our results therefore show that two factors influencing memory-elaboration and attention-are mediated largely by processes in the prefrontal cortex, the hippocampus, and the functional connectivity between these two structures.
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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 | 0.003 |
| 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 | 0.001 |
| 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".