Thermal-Tactile Integration in Object Temperature Perception
Bibliographic record
Abstract
The brain consistently faces a challenge of whether and how to combine the available information sources to estimate the properties of an object explored by hand. While object perception is an inference process involving multisensory inputs, thermal referral (TR) is an illusion demonstrating how the interaction between thermal and tactile systems can lead to deviations from physical reality-when observers touch three stimulators simultaneously with the middle three fingers of one hand but only the outer two stimulators are heated (or cooled), thermal uniformity is perceived across three fingers. Here, we used TR of warmth to examine the thermal-tactile interaction in object temperature perception. We show that TR is consistent with precision-weighted averaging of thermal sensation across tactile locations. Furthermore, we show that prolonged contact with TR stimulation results in adaptation to the local variations of veridical temperatures instead of the thermal uniformity perceived across three fingers. Our results illuminate the flexibility of processing that underlies thermal-tactile interactions and serve as a basis for thermal display design.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".