Differential contributions of the parahippocampal place area and the anterior hippocampus to human memory for scenes
Bibliographic record
Abstract
Past neuroimaging research has identified a parahippocampal place area (PPA) in the posterior medial temporal lobe (MTL), which responds preferentially to visual scenes and plays a role in episodic memory for this class of stimuli. In the present positron emission tomography study, we examined to what extent the functional characteristics of the PPA resemble those of other, more anterior MTL regions across various learning and recognition-memory tasks. We also determined whether the involvement of the PPA in recognition of previously studied scenes is specific to a particular type of scene information. We found that, like the PPA, anterior hippocampal regions showed a novelty response (higher activation for novel than repeated scenes) and a stimulus-related response (higher activation for scenes than objects) during learning, indicating that MTL structures other than the PPA contribute to the encoding of novel stimulus relationships in scenes. However, these anterior hippocampal regions showed no involvement during recognition of either spatial or nonspatial information contained in scenes. The PPA, by contrast, was consistently involved in recognition of all types of scene details, presumably through interactions with co-activated parietal and occipitotemporal cortices. We suggest that MTL contributions from the PPA are sufficient to support recognition of scenes when the task can be based on a perceptually based familiarity process.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".