Novelty responses to relational and non-relational information in the hippocampus and the parahippocampal region: A comparison based on event-related fMRI
Bibliographic record
Abstract
We conducted two functional magnetic resonance imaging (fMRI) experiments that examined novelty responses in the human medial temporal lobe (MTL) to determine whether the hippocampus makes contributions to memory processing that differ from those of structures in the adjacent parahippocampal region. In light of proposals that such differential contributions may pertain to relational processing demands, we assessed event-related fMRI responses in the MTL for novel single objects and for novel spatial and non-spatial object relationships; subjects were asked to detect these different types of novelties among previously studied items, and they successfully performed this task during scanning. A double dissociation that emerged from the response pattern of regions in the hippocampus and perirhinal cortex provided the strongest support for functional specialization in the MTL. A region in the right middle hippocampus responded to the novelty of spatial and non-spatial relationships but not to the novelty of individual objects. By contrast, a region in right perirhinal cortex, situated in the anterior collateral sulcus, responded to the novelty of individual objects but not to that of either type of relationship. Other MTL regions that responded to novelty in the present study showed no reliable difference in their response to the various novelty types; these regions included anterior parts of the hippocampus and posterior aspects of parahippocampal cortex. Together, our findings indicate that relational processing demands are a critical determinant of functional specialization in the human MTL. They also suggest, however, that a neuroanatomical framework that only distinguishes between the hippocampus and the parahippocampal region is not sufficiently refined to account for all functional differences and similarities observed with respect to relational processes in the human MTL.
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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 | 0.000 |
| 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".