The Effect of Divided Attention on Encoding and Retrieval in Episodic Memory Revealed by Positron Emission Tomography
Bibliographic record
Abstract
The effects of divided attention (DA) on episodic memory encoding and retrieval were investigated in 12 normal young subjects by positron emission tomography (PET). Cerebral blood flow was measured while subjects were concurrently performing a memory task (encoding and retrieval of visually presented word pairs) and an auditory tone-discrimination task. The PET data were analyzed using multivariate Partial Least Squares (PLS), and the results revealed three sets of neural correlates related to specific task contrasts. Brain activity, relatively greater under conditions of full attention (FA) than DA, was identified in the occipital-temporal, medial, and ventral-frontal areas, whereas areas showing relatively more activity under DA than FA were found in the cerebellum, temporo-parietal, left anterior-cingulate gyrus, and bilateral dorsolateral-prefrontal areas. Regions more active during encoding than during retrieval were located in the hippocampus, temporal and the prefrontal cortex of the left hemisphere, and regions more active during retrieval than during encoding included areas in the medial and right-prefrontal cortex, basal ganglia, thalamus, and cuneus. DA at encoding was associated with specific decreases in rCBF in the left-prefrontal areas, whereas DA at retrieval was associated with decreased rCBF in a relatively small region in the right-prefrontal cortex. These different patterns of activity are related to the behavioral results, which showed a substantial decrease in memory performance when the DA task was performed at encoding, but no change in memory levels when the DA task was performed at retrieval.
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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 | 0.001 | 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".