Regulating the Past: Computational and Neural Signatures of Persistence Control

Continual learning requires preserving useful knowledge while adapting rapidly when that knowledge becomes obsolete. Biological learners solve this problem across a continuous history. Yet adaptive-learning models primarily regulate how strongly new evidence updates a state, while analyses of human learning often reset the learner at block boundaries. Such resets can conceal how accumulated confidence constrains subsequent adaptation. We study a complementary control problem: how strongly should inherited state continue to constrain learning? We call this persistence control, and instantiate it as prior-directed tempering in the Hierarchical Gaussian Filter, a widely used model of human learning under uncertainty. In simulated HGF learners with continuous histories, accumulated confidence restricted the influence of new evidence even while volatility remained represented. Tempering restored responsiveness, and mechanism recovery distinguished persistence control from surprise-modulated volatility control. In non-stationary bandits, relaxing prior influence was beneficial when contexts changed frequently, whereas preserving it was advantageous when contexts endured. Human behavior favored graded persistence control over carrying everything forward or resetting at detected changepoints. Fitted trajectories showed that persistence in reward beliefs rose after an isolated misleading outcome and was released after contradictory evidence accumulated. Successful learners also reconfigured persistence more strongly around genuine reversals rather than maintaining uniformly lower persistence. Feedback-locked frontal-midline theta covaried with this protection following misleading outcomes. Together, these results identify persistence as a distinct target of adaptive control. Continual learning, in machines and people, may depend not only on how strongly new evidence is incorporated but also on when prior beliefs should remain influential and when they should be released.