Bimanual reaches with symbolic cues exhibit errors in target selection
Bibliographic record
Abstract
We examined symmetric and asymmetric bimanual reaches to targets specified by direct spatial cues and by indirect symbolic cues. Symbolically-cued asymmetric reaches have been shown to exhibit longer reaction times (RTs) compared to symmetric reaches, whereas no such RT cost is observed when targets are directly cued – a pattern thought to implicate increased demands on response selection (Diedrichsen et al., 2001). Assuming that symbolically-cued reaches engage intentional control whereas directly-cued reaches engage more automatic visuo-motor control mechanisms, we asked whether bimanual movements exhibit more interference with symbolic cues than with spatial cues. Participants made bimanual symmetric and asymmetric reaches to short- and long-distance targets cued either symbolically or spatially. We replicated the reaction time cost for symbolically-cued asymmetric movements. A subset of these asymmetric reaches also showed large trajectory corrections. It appeared that this subset had been incorrectly prepared and the movements required of the left and right hands had been switched. No such errors in reach selection were observed when targets were directly cued. In contrast to the RT cost and errors in selection for symbolically-cued movements, we observed little evidence of interference once movements were initiated and executed toward the correct targets regardless of the type of cue.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".