Visual, delay and oculomotor timing and tuning in macaque dorsal pulvinar during instructed and free choice memory saccades
Bibliographic record
Abstract
Abstract Causal perturbations suggest that the primate dorsal pulvinar (dPul) plays a crucial role in target selection and saccade planning, but its basic visuomotor neuronal properties are unclear. While some functional aspects of dPul and interconnected frontoparietal areas – e.g. ipsilesional choice bias after inactivation – are similar, it is unknown if dPul shares oculomotor properties of the cortical circuitry, in particular the delay and choice-related activity. We investigated such properties in macaque dPul during instructed and free-choice memory saccades. Most recorded units showed visual (16%), visuomotor (29%) or motor-related (35%) responses. Visual responses were mainly contralateral; motor-related responses were predominantly post-saccadic (64%) and showed weak contralateral bias. Pre-saccadic enhancement was infrequent (9-15%) – instead, activity was often suppressed during saccade planning (30%) and execution (19%). Surprisingly, only few units exhibited classical visuomotor patterns combining cue and continuous delay activity until the saccade or pre-saccadic ramping, and most spatially-selective neurons did not encode the upcoming decision during free-choice delay. Thus, in absence of a visible goal, the dorsal pulvinar has a limited role in prospective saccade planning, with patterns partially complementing its frontoparietal partners. Conversely, prevalent cue and post-saccadic responses imply the participation in integrating spatial goals with processing across saccades.
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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".