The Role of Frontal Eye Field in Saccadic Mixed-strategy Decision-making
Bibliographic record
Abstract
Abstract Game theory can predict the distribution of choices in aggregate during mixed-strategy games, yet the neural process mediating individual probabilistic choices remains poorly understood. Here, we examined the role of frontal eye field (FEF) in a decision-making task when macaques were trained to play a mixed-strategic game – Matching Pennies – against a computer opponent. Neuronal activities of FEF neurons predicted the animals’ upcoming saccadic choices and these activities became increasingly more selective as the choice deadline approached. Subthreshold electrical micro-stimulation applied in FEF also biased choices. Extended stimulation biased choices towards the preferred FEF vector whereas early termination of stimulation biased choices away from the preferred FEF vector. By contrast, micro-stimulation biased choices in the preferred direction during a non-strategic perceptual luminance discrimination task. We conclude that FEF is causally contributing to mixed-strategy decision-making process although the timing of FEF activation contributes to the decision process in a more non-linear manner during strategic compared to perceptual decision-making.
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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 | 0.002 |
| 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 | 0.001 | 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".