Fig. 8: Modeling stimulation-induced shifts in cost-benefit decision-making.

Aggregated behavioral data from monkey P (a, b) and monkey D (c, d) across significant sessions, modeled using an A2C reinforcement learning framework. Red and green dots represent Av and Ap decisions, respectively. Decision boundaries were estimated via logistic regression under baseline (Stim-off; solid black line) and Stim-on (dashed black line) conditions. In both monkeys, EMS shifted the decision boundary, reflecting an altered weighting of aversion and reward. e A2C model simulations showing how increasing γ (aversion sensitivity) values alter decision boundaries under fixed β values, for each monkey. f A2C model parameters: Mean γ (aversion sensitivity) during Stim-off and Stim-on blocks, separately for each monkey across significant sessions ( ± SEM; monkey P: n = 15 sessions; monkey D: n = 13 sessions). g Econometric model: Boxplots show Δγ across significant and non-significant sessions. The center line represents the median, the bounds of the box denote the 25th and 75th percentiles, and the whiskers extend to the most extreme data points within 1.5× the interquartile range. A more negative Δγ indicates enhanced sensitivity to aversion under EMS. Source data are provided as a Source Data file.