Bibliographic record
Abstract
Abstract Humans’ sense of space helps provide the scaffolding upon which autobiographical memories are built. This allows humans to situate event memories in particular locations and underlies many aspects of cognition. Spatial memory draws upon a hierarchy of representations, from simple sensory features, body motion cues, and heading direction to complex features indicative of boundaries, landmarks, environmental geometry, and scenes. Humans preferentially encode features into spatial memory that are most relevant for orienting, navigating, and future planning. To navigate in unfamiliar environments, people encode the most immediately relevant cues into spatial working memory and use path integration to update spatial representations. In familiar spatial environments, people rely on their habitual knowledge, drawing on well-learned associations between local landmarks and bodily responses such as “turn left at the corner store.” Alternatively, humans use higher level spatial knowledge in a range of different reference frames, from viewpoint-specific snapshots to viewpoint-invariant cognitive maps, to navigate in more flexible ways, allowing for detours and shortcuts. A hierarchical network of brain regions including the hippocampus engages when people employ cognitive maps. In addition to being used for planning routes, this hierarchical spatial memory network drives many other aspects of cognition, including autobiographical memory retrieval, spatial perception, mental imagery, and episodic future thinking.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.019 |
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".