Levels of Processing in Working Memory: Differential Involvement of Frontotemporal Networks
Bibliographic record
Abstract
How does the brain maintain to-be-remembered information in working memory (WM), particularly when the focus of attention is drawn to processing other information? Cognitive models of WM propose that when items are displaced from focal attention recall involves retrieval from long-term memory (LTM). In this fMRI study, we tried to clarify the role of LTM in performance on a WM task and the type of representation that is used to maintain an item in WM during rehearsal-filled versus distractor-filled delays. Participants made a deep or shallow levels-of-processing (LOP) decision about a single word at encoding and tried to recall the word after a delay filled with either rehearsal of the word or a distracting math task. Recalling one word after 10 sec of distraction demonstrated behavioral and neural indices of retrieval from LTM (i.e., LOP effects and medial-temporal lobe activity). In contrast, recall after rehearsal activated cortical areas that reflected reporting the word from focal attention. In addition, areas that showed an LOP effect at encoding (e.g., left ventrolateral VLPFC and the anterior temporal lobes [ATLs]) were reactivated at recall, especially when recall followed distraction. Moreover, activity in left VLPFC during encoding, left ATL during the delay, and left hippocampus during retrieval predicted recall success after distraction. Whereas shallow LOP and rehearsal-related areas supported active maintenance of one item in focal attention, the behavioral processes and neural substrates that support LTM supported recall of one item after it was displaced from focal attention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".