Testing the spatial reference frames used for manual interception
Bibliographic record
Abstract
While early cortical reach areas are known to represent earth-fixed movement goals in a dynamic, gaze-centered map, it is unknown whether the same spatial reference plays a role in the coding of moving targets for manual interception. We tested the role of gaze-dependent and gaze-independent reference frames in the coding of memorized moving targets, rendered invisible prior to a saccade that intervened before the reach. Gaze-centered coding would require the internal representation of the interception point (IP) to be actively updated across the saccade whereas gaze-independent coding would remain stable. Head-fixed subjects (n = 9) sat in complete darkness and fixated a visual fixation point (FP) presented on a screen in front of them. A target started moving for 2.1 s downward at 7 deg/s at various approach directions (-18, 0, +18 deg), after which it disappeared. Occluded targets passed fixation height in a range from -5 to +5 deg (relative to straight ahead); FP locations ranged from -10 to +10 deg. After a saccade (in saccade trials - as opposed to fixation trials) subjects reached out to intercept the occluded target at fixation height with their index finger (saccade trials). We analyzed the pointing errors using regression analyses. Both initial and final fixation direction, as well as IP relative to these gaze directions affected the pointing errors (fixation trials: R2 = 0.15-0.50; saccade trials: R2 = 0.15-0.62). Importantly, errors in the saccade trials reflected combined effects of fixation direction during target presentation and during the memory period. This suggests that a gaze-dependent representation of the IP is transformed into gaze-independent coordinates before the saccade, but that this transformation is not entirely finished at saccade onset. This explains why the pointing errors reflect a mixture of gaze-dependent and gaze-independent reference frames.
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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 | 0.006 |
| 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 | 0.000 |
| 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".