Bibliographic record
Abstract
The hippocampus plays a key role in establishing spatial and event boundaries, crucial for constructing and maintaining cognitive maps in spatial and non-spatial domains. The present set of studies investigated these processes in the context of navigating large-scale, virtual spaces (Google Street View) and viewing visual narratives (film). The first study is composed of two behavioural experiments that highlight the representational priority of turns along travelled routes. Locations associated with turns were recollected better and produced a temporal overestimation bias, compared with locations following turns. The second study presents an fMRI investigation of the interactions between hippocampal representations of decision points and navigational goals along familiar routes. The relationship between decision points and goal representations was modulated by goal proximity, and developed with experience in the virtual environment. This experience-related development suggests that hippocampal representations are reconfigured to represent the most relevant aspects of the environment. The third study presents an fMRI investigation of a large cross-sectional adult lifespan sample in which participants watched a movie. The hippocampus was the only region that consistently represented within-event details as unrelated or dissimilar, in a manner that was distinct from the neocortex. While most neocortical representations remained stable with age, the pattern overlap in the hippocampus, the anterior temporal lobe, and the orbitofrontal cortex increased significantly, suggesting a loss of precision and a potential susceptibility to interference. Together, these studies provide evidence that the representation and segmentation of the temporal flow of experience are key organizational features of episodic memory and navigation. These dimensions are captured in moment-to-moment hippocampal dynamics and malleable by different contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".