Effect of room size on geometry and features cue preference during reorientation: Modulating encoding strength or cue weighting
Bibliographic record
Abstract
Three experiments investigated how the room size affects preferential use of geometric and non-geometric cues during reorientation inside a room. We hypothesised that room size may affect preferential use of geometric and non-geometric cues by affecting the encoding of the cues (the encoding hypothesis), the retrieval of the cues (the retrieval hypothesis), or both the encoding and retrieval of the cues (the encoding-plus-retrieval hypothesis). In immersive virtual rectangular rooms, participants learned objects' locations with respect to geometric (room shape) and non-geometric cues (features on walls or isolated objects). During the test, participants localised objects with the geometric cue only, non-geometric cues only, or both. The two cues were placed at the original locations or displaced relative to each other (conflicting cues) when both were presented at testing. We manipulated the room size between participants within each experiment. The results showed that the room size affected cue preference using conflicting cues but did not affect response accuracy using single cues at testing. These results support the retrieval hypothesis. The results were discussed in terms of the effects of cue salience and stability on cue interaction in reorientation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| 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".