Does a turn in the road mark a turn of events? Turns along travelled routes provide contextual boundaries during navigation
Bibliographic record
Abstract
Physical or conceptual boundaries segment continuous experience into component events. Boundaries accentuate differences between events that occur on either side of them, and reduce differences among events within them. Here we show that turns during navigation have the same effect on memory for routes as they do for episodes. Across three experiments, turns selectively enhanced participants’ recollection of locations immediately preceding them. In addition, in Experiments 1 and 2, the presence of an intervening turn enhanced participants’ ability to discriminate between event durations across boundaries, but impaired their ability to discriminate between their ordinal positions. In Experiment 3, the reported increase in recollection of pre-turn locations was also reflected in subjective dilation of the time spent at pre-turn, relative to post-turn, locations. Together, these results highlight the fundamental role of turns in the segmentation of spatial and temporal memory and indicate a potential mechanism for the segmentation of experience generally.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".