Temporal Dynamics Underlying Prelimbic Prefrontal Cortical Regulation of Action Selection and Outcome Evaluation during Risk/Reward Decision-Making
Bibliographic record
Abstract
Risk/reward decision-making is a dynamic process that includes periods of deliberation before action selection and evaluation of the action outcomes that bias subsequent choices. Inactivation of the prelimbic (PL) cortex has revealed its integral role in updating decision biases in the face of changes in probabilistic reward contingencies, yet how phasic PL signals during different phases of the decision process influence choice remains unclear. We used temporally specific optogenetic inhibition to selectively disrupt PL activity coinciding with action selection and outcome phases to examine how these signals influence choice. Male rats expressing the inhibitory opsin eArchT within PL excitatory neurons were well trained on a probabilistic discounting task, entailing choice between small/certain versus large/risky rewards, the probability of which varied over a session (50–12.5%). During testing, brief light pulses suppressed PL activity before choice or after different outcomes. Prechoice suppression reduced bias toward more preferred/higher utility options and disrupted how recent outcomes influenced subsequent choice. Inhibition during risky losses induced a similar profile, but here, the impact of reward omissions were either amplified or diminished, relative to the context of the estimated profitability of the risky option. Inhibition during large or small reward receipt reduced risky choice when this option was more profitable, suggesting these signals can both reinforce rewarded risky choices and also act as a relative value comparator signal that augments incentive for larger rewards. These findings reveal multifaceted contributions by the PL in implementing decisions and integrating action–outcome feedback to assign context to the decision space. SIGNIFICANCE STATEMENT The PL prefrontal cortex plays an integral role in guiding risk/reward decisions, but how activity in this region during different phases of the decision process influences choice is unclear. By using temporally specific optogenetic manipulations of this activity, the present study unveiled previously uncharacterized and differential contributions by PL in implementing decision policies and how evaluation of decision outcomes shape subsequent choice. These findings provide novel insight into the dynamic processes engaged by the PL that underlie action selection in situations involving reward uncertainty that may aid in understanding the mechanism underlying normal and aberrant decision-making processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".