Updating self-location by self-motion and visual cues in familiar multiscale spaces.
Bibliographic record
Abstract
This study examined functions of self-motion and visual cues in updating people's actual headings in multiscale spaces. In an immersive virtual environment, the participants learned objects' locations inside two misaligned rectangular rooms by locomoting within and between the rooms. In each testing trial, the participants locomoted to adopt an actual perspective in one room, and then they judged relative direction to a target from an imagined perspective in the other room (remote perspective taking). The imagined and actual perspectives had the same or opposite cardinal directions (globally aligned or misaligned) or had the same or opposite orientations defined by room structures (locally aligned or misaligned). Global or local sensorimotor alignment effects mean that performance is better when imagined and actual perspectives were globally or locally aligned than when misaligned. We examined these effects to infer updating actual headings in global or local representations. The results showed local but no global sensorimotor alignment effect. By contrast, there were both global and local sensorimotor alignment effects when the participants judged across-room relative headings prior to remote perspective taking. These results indicate that people update headings in local representations based on visual similarities between local spaces. People update headings in global representations based on self-motion cues available in across-boundary navigation, but updating headings globally requires tasks to activate global-relevant sensorimotor representations. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.000 |
| 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".