Imagine Your Crossed Hands as Uncrossed: Visual Imagery Impacts the Crossed-Hands Deficit
Bibliographic record
Abstract
Successful interaction with our environment requires accurate tactile localization. Although we seem to localize tactile stimuli effortlessly, the processes underlying this ability are complex. This is evidenced by the crossed-hands deficit, in which tactile localization performance suffers when the hands are crossed. The deficit results from the conflict between an internal reference frame, based in somatotopic coordinates, and an external reference frame, based in external spatial coordinates. Previous evidence in favour of the integration model employed manipulations to the external reference frame (e.g., blindfolding participants), which reduced the deficit by reducing conflict between the two reference frames. The present study extends this finding by asking blindfolded participants to visually imagine their crossed arms as uncrossed. This imagery manipulation further decreased the magnitude of the crossed-hands deficit by bringing information in the two reference frames into alignment. This imagery manipulation differentially affected males and females, which was consistent with the previously observed sex difference in this effect: females tend to show a larger crossed-hands deficit than males and females were more impacted by the imagery manipulation. Results are discussed in terms of the integration model of the crossed-hands deficit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".