Prefrontal and Medial Temporal Lobe Cortical Contributions to Visual Short-Term Memory
Bibliographic record
Abstract
A number of recent studies have indicated that the medial temporal lobe (MTL) plays a critical role in working memory (WM) and perception, but these results have been highly controversial given the traditional association of MTL with long-term memory. We review the research and highlight important factors that need to be considered in determining the role of MTL in WM including set-size of used stimuli and feature complexity and/or feature conjunctions/bindings embedded in those stimuli. These factors relate to hierarchical and, accordingly, domain-specific theories of functional organization within the temporal lobe. In addition, one must consider process-specific theories too, because two key processes commonly understood to contribute recognition memory, namely, recollection and familiarity, also have robust support from neurophysiological and neuroimaging research as to their functional dissociations within MTL. PFC has long been heavily implicated in WM; however, relatively less is known about how the PFC contributes to recollection and familiarity, although dynamic prefrontal coding models in WM may help to explain their neural mechanisms. The MTL and PFC are heavily interconnected and do not operate independently in underlying WM. We propose that investigation of the interactions between these two regions in WM, particularly their coordinated neural activities, and the modeling of such interactions, will be crucial for the advancing understanding of the neural mechanisms of WM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".