A weighted constraint satisfaction approach to human goal-directed decision making
Fig 6
Weighted consideration of subgoal-relevant and -irrelevant constraints approximated optimal choice behavior when the base task was embedded as an initial subgoal task.
A. Path choices in the IA trials. As in Figs 3B and 5B, the proportion of optimal trials for each individual was converted into a probit score, with individual probit scores larger than 3 or smaller than -3 capped at 3 or -3 before averaging. B. Test data objective (summed negative log-likelihood) of the candidate drift-diffusion models compared to the baseline model. Asterisk marks the winning model. For empirical and predicted response time distributions, see S4 Fig. C. Parameter estimates from the winning model. t0, non-decision time. a, decision bound. md and fd, myopic and future advantage weights. gd, goal weight. sz, inter-trial variability of the starting point. sd, inter-trial variability of the drift rate. p, proportional change to md and fd in the subgoal trials.