Bibliographic record
Abstract
Recently we have found that during visually-guided grasping tasks, individuals prefer to use their right hand to pick up an object, particularly if it is small in size. However, during haptically-guided (using touch) grasping tasks, a significant increase in left-hand use emerges, particularly when grasping small objects (which require finer discrimination). Is the increase in left-hand use due to a left-hand/right-hemisphere specialization for haptic discrimination? To address this question, blindfolded participants were instructed to haptically assess and replicate an array of small objects (LEGOs) as quickly and accurately as possible. Located on a building plate in front of the participant was an array of five different blocks to be replicated (reference array) using a closer array of ten blocks (sample array). Using hapsis, participants would choose from and remove the blocks needed from the sample array and place them onto a smaller fixed building plate directly in front of them, creating a replica of the reference array. Participants completed the task bimanually, or exclusively using the left or right hand (counterbalanced between participants). Measurements included time to complete each trial and the number of mistakes made by each hand per trial. Additionally, we measured the time each hand spent discriminating the arrays during the bimanual trials. Results showed that participants were fastest at completing each trial when they used both hands. However, for these trials participants made significantly more mistakes when compared to the left but not the right hand. Furthermore, participants spent significantly more time discriminating the blocks with their left hand during the bimanual trials. The results align with previous findings of a left-hand/right-hemisphere specialization for haptic discrimination, which may explain the increase in left-hand use for grasping without vision. Furthermore the results suggest that during a haptically-guided bimanual task, hemispheric cross-talk may interfere with performance. Meeting abstract presented at VSS 2014
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.024 |
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".