Memory and navigation: Compression of space varies with route length and turns
Bibliographic record
Abstract
For memory to be efficient and useful during recall, problem-solving, and planning, retrieval must be compressed in time. Evidence from rodents suggests that neural compression during replay of spatial memories varies widely, with a range of compression ratios reported from 6:1 to 64:1. Anecdotal evidence suggests that similar compression occurs during mental navigation in humans: we recall how to get from one place to another countless times almost every day of our lives, and this recall never takes as long as physically travelling those routes would take. In this experiment we sought to determine whether this behavioural compression could be measured during mental navigation in humans (spatial memory replay), and which factors might affect the compression of such spatial memories. To this end, thirty participants mentally navigated routes between two landmarks, which varied in length and number of turns, as we measured replay times and recorded ratings of familiarity, detail, and presence. A multi-level model was used to determine which factors were associated with variation in compression. Route length and number of turns emerged from this model as significantly correlated with compression, such that longer routes were more compressed while compression was attenuated as the number of turns in a route increased. This suggests that compression during recall may be affected by specific features of a route, especially those that may act to segment the space or event being represented.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".