Neural Processes Involved in Action Selection During a Mixed-Strategy Game
Bibliographic record
Abstract
Game theory outlines optimal response strategies during mixed-strategy competitions in which available actions are selected probabilistically. The neural processes involved in choosing individual strategic actions, however, remain poorly understood. Here, actions need to be selected (1) in the absence of sensory instruction or reward cues and (2) independent of previous events. This thesis examines the neural processes involved in action selection during mixed-strategy competition. To do so, we both measured and manipulated presaccadic activity in the primate superior colliculus (SC), a structure involved in the generation of orienting saccadic eye movements, during a strategic game. The first study tested whether the SC is involved in choosing saccades under strategic conditions. Monkeys were free to choose either of two saccade targets as they competed against a computer opponent during the mixed-strategy game ‘matching-pennies’. The accuracy with which pre-saccadic SC activity predicted upcoming choice gradually increased in the time leading up to the saccade. Probing the SC with supra-threshold stimulation demonstrated that these evolving signals were functionally involved in preparing strategic saccades. Finally, sub-threshold stimulation of the SC increased the likelihood that contralateral saccades were selected. In the second study, we compared the influence of previous actions and rewards on updating premotor activity in the SC in the strategic condition where eliciting stochastic responses was optimal and in a non-strategic condition where stochastic responses were also elicited but through explicit instruction. To avoid exploitation by opponents during mixed-strategy competitions one should select behaviors unpredictably, that is, independent of previous choices and their outcomes. The iterative updating of neural processes involved in selecting actions to produce mixed-strategy behaviors, however, remain poorly understood In both tasks, premotor activity and behavior were shaped by past actions and rewards with more recent events exerting the largest influence. Importantly, these sequential effects were attenuated under strategic conditions suggesting that updating of selection processes is not entirely automatic but can be tailored to different decision-making contexts. Together our results highlight the active role played by the brain in choosing strategic actions.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".