Developing global spatial memories by one-shot across-boundary navigation.
Bibliographic record
Abstract
This study investigated to what extent people can develop global spatial representations of a multiroom environment through one-shot physical walking between rooms. In Experiment 1, the participants learned objects' locations in one room of an immersive virtual environment. They were blindfolded and led to walk to a testing position either within the same room (within-boundary) or in an adjacent novel room (across-boundary). They conducted judgments of relative direction (JRD) based on the remembered locations of objects. The participants' actual perspectives and imagined perspectives of JRD trials were manipulated to be aligned or misaligned (i.e., faced the same or opposite cardinal directions). The results showed better JRD performances for the aligned perspectives than the misaligned perspectives in the across-boundary condition; this global sensorimotor alignment effect was comparable with the effect in the within-boundary condition. Experiments 2-6 further examined global sensorimotor alignment effects after across-boundary walking. Experiments 2-3 manipulated factors related to encoding global relations (i.e., explicit instructions to attend to walking and keep track of spatial relations, and visual cues for navigational affordance to another space). Experiments 4-6 manipulated factors related to retrieving global relations in JRD (i.e., learning orientation as one imagined perspective, learning position and orientation as the imagined viewpoint, and the number of imagined perspectives). The results showed robust global sensorimotor alignment effects in all experiments, indicating that the participants updated actual headings relative to remembered objects in the other room. Global spatial updating might be the primary mechanism for developing global spatial representations of a multiscale environment. (PsycInfo Database Record (c) 2022 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 | 0.001 |
| 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 | 0.000 | 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".