Allocentric visual cues influence mental transformation of bodies
Bibliographic record
Abstract
Identifying a human body stimulus involves mentally rotating an embodied spatial representation of one's body (motoric embodiment) and projecting it onto the stimulus (spatial embodiment). Interactions between these two processes (spatial and motoric embodiment) may thus reveal cues about the underlying reference frames. The allocentric visual reference frame, and hence the perceived orientation of the body relative to gravity, was modulated using the York Tumbling Room, a fully furnished cubic room with strong directional cues that can be rotated around a participant's roll axis. Sixteen participants were seated upright (relative to gravity) in the Tumbling Room and made judgments about body and hand stimuli that were presented in the frontal plane at orientations of 0°, 90°, 180° (upside down), or 270° relative to them. Body stimuli have an intrinsic visual polarity relative to the environment whereas hands do not. Simultaneously the room was oriented 0°, 90°, 180° (upside down), or 270° relative to gravity resulting in sixteen combinations of orientations. Body stimuli were more accurately identified when room and body stimuli were aligned. However, such congruency did not facilitate identifying hand stimuli. We conclude that static allocentric visual cues can affect embodiment and hence performance in an egocentric mental transformation task. Reaction times to identify either hands or bodies showed no dependence on room orientation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".