Segmented Space: Measuring Tactile Localisation in Body Coordinates
Bibliographic record
Abstract
Previous research showing systematic localisation errors in touch perception related to eye and head position has suggested that touch is at least partially localised in a visual reference frame. However, many previous studies had participants report the location of tactile stimuli relative to a visual probe, which may force coding into a visual reference. Also, the visual probe could itself be subject to an effect of eye or head position. Thus, it is necessary to assess the perceived position of a tactile stimulus using a within-modality measure in order to make definitive conclusions about the coordinate system in which touch might be coded. Here, we present a novel method for measuring the perceived location of a touch in body coordinates: the Segmented Space Method (SSM). In the SSM participants imagine the region within which the stimulus could be presented divided into several equally spaced, and numbered, segments. Participants then simply report the number corresponding to the segment in which they perceived the stimulus. The SSM represents a simple and novel method that can be easily extended to other modalities by dividing any response space into numbered segments centred on some appropriate reference point (e.g. the head, the torso, the hand, or some point in space off the body). Here we apply SSM to the forearm during eccentric viewing and report localisation errors for touch similar to those previously reported using a crossmodal comparison. The data collected with the SSM strengthen the theory that tactile spatial localisation is generally coded in a visual reference frame even when visual coding is not required by the task.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".