The role of the hippocampus in memory and mental construction
Bibliographic record
Abstract
Much has been learned about the processes that support the remembrance of past autobiographical episodes and their importance for a number of cognitive tasks. This work has focused on hippocampal contributions to constructing coherent mental representations of scenarios for these tasks, which has opened up new questions about the underlying hippocampal mechanisms. We propose a new framework to answer these questions, which incorporates task demands that prompt hippocampal contributions to mental construction, the online formation of such mental representations, and how these demands relate to the functional organization of the hippocampus. Synthesizing findings from autobiographical memory research, our framework suggests that the interaction of two task characteristics influences the recruitment of the hippocampus: (1) the degree of task open-endedness (quantified by the presence/absence of a retrieval framework) and (2) the degree to which the integration of perceptual details is required. These characteristics inform the relative weighting of anterior and posterior hippocampal involvement, following an organizational model in which the anterior and posterior hippocampus support constructions on the basis of conceptual and perceptual representations, respectively. The anticipated outcome of our framework is a refined understanding of hippocampal contributions to memory and to the host of related cognitive functions.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".