Gaze Behaviour Reveals Flexible Encoding of Competing Reach Goals Under Conditions of Target Uncertainty
Bibliographic record
Abstract
Abstract In daily tasks, we are often confronted with competing potential targets and must select one to act on. It has been suggested that, prior to target selection, the human brain encodes the motor goals of multiple, potential targets. However, this view remains controversial and it has been argued that only a single motor goal is encoded, or that motor goals are only specified after target selection. To investigate this issue, we measured participants’ gaze behaviour while viewing two potential reach targets, one of which was cued after a preview period. We applied visuomotor rotations to dissociate each visual target location from its corresponding motor goal location; i.e., the location participants needed to aim their hand toward to bring the rotated cursor to the target. During the preview period, participants most often fixated both motor goals but also frequently fixated one, or neither, motor goal location. Further gaze analysis revealed that on trials in which both motor goals were fixated, both locations were held in memory simultaneously. These findings show that, at the level of single trials, the brain most often encodes multiple motor goals prior to target selection, but may also encode either one or no motor goals. This result may help reconcile a key debate concerning the specification of motor goals in cases of target uncertainty.
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 | 0.000 |
| 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".