Figure 2.

Details of the model and fitting results. (A) The model takes as inputs information about the rewards (i.e., $r_1$ and $r_2$; blue nodes) and time delays (i.e., $t_1$ and $t_2$; yellow nodes), and converts these inputs to a subjective representation (i.e., $I_r$ and $I_t$, respectively) through with parameters ${\alpha }_r$ and ${\alpha }_t$. Features are selected with the parameter $\omega$ (i.e., the green node). Deliberation among the SS and LL alternatives is modulated by lateral inhibition parameters ${\beta }_{SS}$ and ${\beta }_{LL}$ (i.e., the orange node). Once an accumulator reaches a threshold amount of preference, a decision is made corresponding to the winning accumulator (i.e., the red node). (B) Example of how the model implements self-control-like behavior through lateral inhibition (${\beta }_{SS}=0.2$ and ${\beta }_{LL}=0.1$) and not valuation ($I_r=I_t=0.5$). (C) Model fitting results in terms of a z-transformed BIC statistic separated by model constraint (rows) and subjects (columns), color coded according to the legend on the right. Empty circles indicate that a parameter was free to vary, whereas filled nodes indicate that a parameter was fixed. The model structures are grouped by the number of free parameters: black, blue, green and red indicate that a total of 3, 4, 5, and 6 free parameters were used, respectively. (D) Model fits from each model in (C), aggregated across subjects. For the zBIC, lower values (blue) indicate better model performance.