Extended Data Fig. 6: Alternative dynamical models.

We tested whether two alternative models of FOF function could explain our findings. In both models, we refer to the the FOF contralateral to the lottery as the lottery node, L (square), and the FOF contralateral to the surebet as the surebet node, SB (round). These shapes also correspond to the neural activity plots in the upper panels of b,c,f,g. a. The input into each node of the FOF is the expected value (EV) of the corresponding offer. As such, the neurons in this model correlate with lottery magnitude. b. Upper panel: unilateral silencing of L dramatically decreases the firing rate of L (compare the grey and purple squares). The dots represent the mean network responses across 20 trials (per lottery, n = 6 × 20 = 120 simulated trials). The error bars represent the 95% confidence interval of the mean across 200 permutations. Lower panel: Silencing L results in a dramatic behavioral shift away from choosing the lottery - a contralateral impairment. Silencing SB would also result in a contralateral impairment (inconsistent with our findings). The dots represent the mean P(choose lottery) based on the activity shown in the upper panel. The error bar is the 95% CI of the mean. The error bars represent the 95% confidence interval of the mean across 200 permutations. c. as in b for bilateral silencing. Here, the behavioral effect is an increase in noise, not a shift away from the lottery. d. In this model there is an upstream process that decides whether to choose the lottery or surebet, and the FOF gets as input this binary decision²⁵. f,g as in b,c. The unilateral effects are large and bilateral silencing increases noise. e. Since the input the FOF in this model is post-decision, the neurons in this model do not correlate with lottery magnitude after conditioning on choice.