Visual, delay, and oculomotor timing and tuning in macaque dorsal pulvinar during instructed and free choice memory saccades
Bibliographic record
Abstract
Causal perturbations suggest that primate dorsal pulvinar plays a crucial role in target selection and saccade planning, though its basic neuronal properties remain unclear. Some functional aspects of dorsal pulvinar and interconnected frontoparietal areas-e.g. ipsilesional choice bias after inactivation-are similar. But it is unknown if dorsal pulvinar shares oculomotor properties of cortical circuitry, in particular delay and choice-related activity. We investigated such properties in macaque dorsal pulvinar during instructed and free-choice memory saccades. Most recorded units showed visual (12%), saccade-related (30%), or both types of responses (22%). Visual responses were primarily contralateral; diverse saccade-related responses were predominantly post-saccadic with a weak contralateral bias. Memory delay and pre-saccadic enhancement was infrequent (11-9%)-instead, activity was often suppressed during saccade planning (25%) and further during execution (15%). Surprisingly, only few units exhibited classical visuomotor patterns combining cue and continuous delay activity or pre-saccadic ramping; moreover, 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 visual and post-saccadic responses imply its 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".