Greater benefits of multisensory integration during complex sensorimotor transformations
Bibliographic record
Abstract
Multisensory integration enables rapid and accurate behavior. To orient in space, sensory information registered initially in different reference frames has to be integrated with the current postural information to produce an appropriate motor response. In some postures, multisensory integration requires convergence of sensory evidence across hemispheres, which would presumably lessen or hinder integration. Here, we examined orienting gaze shifts in humans to visual, tactile, or visuotactile stimuli when the hands were either in a default uncrossed posture or a crossed posture requiring convergence across hemispheres. Surprisingly, we observed the greatest benefits of multisensory integration in the crossed posture, as indexed by reaction time (RT) decreases. Moreover, such shortening of RTs to multisensory stimuli did not come at the cost of increased error propensity. To explain these results, we propose that two accepted principles of multisensory integration, the spatial principle and inverse effectiveness, dynamically interact to aid the rapid and accurate resolution of complex sensorimotor transformations. First, early mutual inhibition of initial visual and tactile responses registered in different hemispheres reduces error propensity. Second, inverse effectiveness in the integration of the weakened visual response with the remapped tactile representation expedites the generation of the correct motor response. Our results imply that the concept of inverse effectiveness, which is usually associated with external stimulus properties, might extend to internal spatial representations that are more complex given certain body postures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".