fMRI differences in encoding and retrieval of pictures due to encoding strategy in the elderly
Bibliographic record
Abstract
Functional MRI (fMRI) was used to examine the neural correlates of depth of processing during encoding and retrieval of photographs in older normal volunteers (n = 12). Separate scans were run during deep (natural vs. man-made decision) and shallow (color vs. black-and-white decision) encoding and during old/new recognition of pictures initially presented in one of the two encoding conditions. A baseline condition consisting of a scrambled, color photograph was used as a contrast in each scan. Recognition accuracy was greater for the pictures on which semantic decisions were made at encoding, consistent with the expected levels of processing effect. A mixed-effects model was used to compare fMRI differences between conditions (deep-baseline vs. shallow-baseline) in both encoding and retrieval. For encoding, this contrast revealed greater activation associated with deep encoding in several areas, including the left parahippocampal gyrus (PHG), left middle temporal gyrus, and left anterior thalamus. Increased left hippocampal, right dorsolateral, and inferior frontal activations were found for recognition of items that had been presented in the deep relative to the shallow encoding condition. We speculate that the modulation of activity in these regions by the depth of processing manipulation shows that these regions support effective encoding and successful retrieval. A direct comparison between encoding and retrieval revealed greater activation during retrieval in the medial temporal (right hippocampus and bilateral PHG), anterior cingulate, and bilateral prefrontal (inferior and dorsolateral). Most notably, greater right posterior PHG was found during encoding compared to recognition. Focusing on the medial temporal lobe (MTL) region, our results suggest a greater involvement of both anterior MTL and prefrontal regions in retrieval compared to encoding.
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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 | 0.000 |
| 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".