The Role of Haptic Expectations in Reaching to Grasp: From Pantomime to Natural Grasps and Back Again
Bibliographic record
Abstract
When we reach to pick up an object, our actions are effortlessly informed by the object's spatial information, the position of our limbs, stored knowledge of the object's material properties, and what we want to do with the object. A substantial body of evidence suggests that grasps are under the control of "automatic, unconscious" sensorimotor modules housed in the "dorsal stream" of the posterior parietal cortex. Visual online feedback has a strong effect on the hand's in-flight grasp aperture. Previous work of ours exploited this effect to show that grasps are refractory to cued expectations for visual feedback. Nonetheless, when we reach out to pretend to grasp an object (pantomime grasp), our actions are performed with greater cognitive effort and they engage structures outside of the dorsal stream, including the ventral stream. Here we ask whether our previous finding would extend to cued expectations for haptic feedback. Our method involved a mirror apparatus that allowed participants to see a "virtual" target cylinder as a reflection in the mirror at the start of all trials. On "haptic feedback" trials, participants reached behind the mirror to grasp a size-matched cylinder, spatially coincident with the virtual one. On "no-haptic feedback" trials, participants reached behind the mirror and grasped into "thin air" because no cylinder was present. To manipulate haptic expectation, we organized the haptic conditions into blocked, alternating, and randomized schedules with and without verbal cues about the availability of haptic feedback. Replicating earlier work, we found the strongest haptic effects with the blocked schedules and the weakest effects in the randomized uncued schedule. Crucially, the haptic effects in the cued randomized schedule was intermediate. An analysis of the influence of the upcoming and immediately preceding haptic feedback condition in the cued and uncued random schedules showed that cuing the upcoming haptic condition shifted the haptic influence on grip aperture from the immediately preceding trial to the upcoming trial. These findings indicate that, unlike cues to the availability of visual feedback, participants take advantage of cues to the availability of haptic feedback, flexibly engaging pantomime, and natural modes of grasping to optimize the movement.
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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 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".