Hippocampus long‐axis specialization throughout development: A meta‐analysis
Bibliographic record
Abstract
The human adult hippocampus can be subdivided into the head, or anterior hippocampus and its body and tail, or posterior hippocampus, and a wealth of functional differences along the longitudinal axis have been reported. One line of literature emphasizes specialization for different aspects of cognition, whereas another emphasizes the unique role of the anterior hippocampus in emotional processing. While some research suggests that functional differences in memory between the anterior and posterior hippocampus appear early in development, it remains unclear whether this is also the case for functional differences in emotion processing. The goal of this meta-analysis was to determine whether the long-axis functional specialization observed in adults is present earlier in development. Using a quantitative meta-analysis, long-axis functional specialization was assessed using the data from 26 functional magnetic resonance imaging studies, which included 39 contrasts and 804 participants ranging in age from 4 to 21 years. Results indicated that emotion was more strongly localized to the anterior hippocampus, with memory being more strongly localized to the posterior hippocampus, demonstrating long-axis specialization with regard to memory and emotion in children similar to that seen in adults. An additional analysis of laterality indicated that while memory was left dominant, emotion was processed bilaterally.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".