Neural mechanisms for predictive head movement strategies during sequential gaze shifts
Bibliographic record
Abstract
Humans adopt very different head movement strategies for different gaze behaviors, for example, when playing sports versus watching sports on television. Such strategy switching appears to depend on both context and expectation of future gaze positions. Here, we explored the neural mechanisms for such behaviors by training three monkeys to make head-unrestrained gaze shifts toward eccentric radial targets. A randomized color cue provided predictive information about whether that target would be followed by either a return gaze shift to center or another, more eccentric gaze shift, but otherwise animals were allowed to develop their own eye-head coordination strategy. In the first two animals we then stimulated the frontal eye fields (FEF) in conjunction with the color cue, and in the third animal we recorded from neurons in the superior colliculus (SC). Our results show that 1) monkeys can optimize eye-head coordination strategies from trial to trial, based on learned associations between color cues and future gaze sequences, 2) these cue-dependent coordination strategies were preserved in gaze saccades evoked during electrical stimulation of the FEF, and 3) two types of SC responses (the saccade burst and a more prolonged response related to head movement) modulated with these cue-dependent strategies, although only one (the saccade burst) varied in a predictive fashion. These data show that from one moment to the next, the brain can use contextual sensory cues to set up internal "coordination states" that convert fixed cortical gaze commands into the brain stem signals required for predictive head motion.
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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 | 0.000 | 0.000 |
| 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".