Extended Data Fig. 6: Overall reactivation of states along reward and loss paths during planning.
From: Differential replay of reward and punishment paths predicts approach and avoidance
To estimate overall state reactivation, we conducted a GLM on the probabilistic state reactivation time series per trial, comparing overall reactivation of rewarding vs punishing paths. The resultant beta coefficients for each path’s overall state reactivation were entered into two linear mixed effects models: model 9 predicting state reactivation [Reactivation ~ (Choice × Path Type) + RT + (1 | Subject)] and model 10 predicting sequenceness [Sequenceness ~ (Choice × Path Type) + Reactivation) + RT + (1 | Subject / Lag)]. Note that trials with state reactivation coefficients more than 5 standard deviations from the group mean were excluded (0.03% of trials). (a) Estimated marginal means from linear mixed effects model (on N = 24 participants), where choice and path type (rewarding or punishing, green and red respectively) predicted state reactivation coefficient. There was significantly greater (p = 0.028) reactivation for rewarding than punishing paths (significance given by a two-tailed statistic using a Satterhwaite approximation, and error bars indicate 95% confidence interval – same for B and C). (b) Similar model to A, except that this model predicted sequenceness, and also state reactivation was included as a fixed effect. An interaction between choice and path type on sequenceness was significant (p = 3.023E-6), even when controlling for reactivation. (c) Same model as B but showing the effect of state reactivation on sequenceness (p = 8.542E-14). * p < .05, ** p < .01, *** p < .001.
