Neural correlates for task switching in the macaque superior colliculus
Bibliographic record
Abstract
Successful task switching requires a network of brain areas to select, maintain, implement, and execute the appropriate task. Although frontoparietal brain areas are thought to play a critical role in task switching by selecting and encoding task rules and exerting top-down control, how brain areas closer to the execution of tasks participate in task switching is unclear. The superior colliculus (SC) integrates information from various brain areas to generate saccades and is likely influenced by task switching. Here, we investigated switch costs in nonhuman primates and their neural correlates in the activity of SC saccade-related neurons in monkeys performing cued, randomly interleaved pro- and anti-saccade trials. We predicted that behavioral switch costs would be associated with differential modulations of SC activity in trials on which the task was switched vs. repeated, with activity on the current trial resembling that associated with the task set of the previous trial when a switch occurred. We observed both error rate and reaction time switch costs and changes in the discharge rate and timing of activity in SC neurons between switch and repeat trials. These changes were present later in the task only after fixation on the cue stimuli but before saccade onset. These results further establish switch costs in macaque monkeys and suggest that SC activity is modulated by task-switching processes in a manner inconsistent with the concept of task set inertia. NEW & NOTEWORTHY Task-switching behavior and superior colliculus (SC) activity were investigated in nonhuman primates performing randomly interleaved pro- and anti-saccade tasks. Here, we report error rate and reaction time switch costs in macaque monkeys and associated differences in stimulus-related activity of saccade-related neurons in the SC. These results provide a neural correlate for task switching and suggest that the SC is modulated by task-switching processes and may reflect the completion of task set reconfiguration.
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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 | 0.001 |
| 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".