The use of visual and nonvisual cues in updating the perceived position of the world during translation
Bibliographic record
Abstract
During self-motion the perceived positions of objects remain fixed in perceptual space. This requires that their perceived positions are updated relative to the viewer. Here we assess the roles of visual and non-visual information in this spatial updating. To investigate the role of visual cues observers sat in an enclosed, immersive, virtual environment formed by six rear-projection screens. A simulated room was presented stereographically and shifted relative to the observer. A playing card, whose movement was phase-locked to the room, floated in front of the subject who judged if this card was displaced more or less than the room. Surprisingly, perceived stability occurred not when the card’s movement matched the room’s displacement but when perspective alignment was maintained and parallax between the card and the room was removed. The role of the complementary non-visual cues was investigated by physically moving subjects in the dark. Subjects judged whether a floating target was displaced more or less than if it were earth stable. To be judged as earth-stationary the target had to move in the same direction as the observer: more so if the movement was passive. We conclude that both visual and non-visual cues to self-motion and active involvement in the movement are simultaneously required for veridical spatial updating.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".