Humans Optimally Integrate Cutaneous and Proprioceptive Cues In Haptic Size Perception
Bibliographic record
Abstract
Abstract Sensory perception often relies on the brain’s integration of multiple noisy inputs (cues), a process known as cue combination. Cue combination within the sense of touch has been understudied. Here, we investigated whether humans optimally combine haptic cutaneous and hand configuration cues when discerning the size (e.g., diameter) of a disk held edge-on between the thumb and index fingers. When these two fingers span the diameter of a disk to contact its perimeter, a hand configuration cue (relating to the perceived distance between the fingers) provides information about the disk’s size. Less obviously, cutaneous cues to disk size may be provided simultaneously from the indentation of the skin caused by the curvature of the disk (smaller disks cause greater indentation). It is unknown whether humans make use of all these cues when perceiving the size of the held object, and if so, whether they integrate the cues optimally. We considered three hypotheses for how humans might use these cues: they might rely solely on the least noisy cue (Winner-Take-All Model, WTA), combine cues based on a simple arithmetic average (Average-Measurement Model, AVG), or combine cues via an optimal weighted average (Optimally-Weighted Model, OPT). In three experiments involving 34 participants, we measured the reliabilities of these cues and compared participant performance to the predictions of the three models. Each experiment tested participants using a two-interval forced-choice (2IFC) paradigm with 3D printed disk stimuli. On each trial, under occluded vision, participants felt two disks sequentially and responded which felt larger. Participants were tested with each finger’s cutaneous cue alone, the configuration cue alone, and all three cues together. In two experiments, the disks presented were circular. In a third experiment, unknown to participants, some of the presented disks were oval-like cue-conflict stimuli. The improvement of accuracy observed in multi-cue conditions over single-cue conditions, and the Point of Subjective Equality (PSE) shifts observed in cue-conflict conditions, were consistent with optimal cue combination. We conclude that humans are capable of combining haptic cutaneous and configuration cues optimally to judge the sizes of held objects.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".