Decoding the neural dynamics of everyday prospective remembering: a hidden Markov model approach

Abstract

Introduction: Prospective memory (PM)—the ability to form, maintain, and execute delayed intentions—is essential for everyday functioning. Traditionally, PM paradigms relied on repetitive tasks and focused on transient post-stimulus activity, overlooking the sustained neural processes supporting intention maintenance. Methods: High-density EEG data were recorded during a naturalistic PM paradigm simulating everyday activities (preparing a meal while watching TV), comprising three conditions: naturalistic viewing, event-based PM, and time-based PM. Using individual MRI and hidden Markov modeling (HMM), brain activity was studied at the source level and splitted into an optimal number of states. Results: The HMM analysis identified 6 brain states. Among them, State 3 was characterized by activations over regions of the dorsal attention network (DAN) and was more prominent during the timebased PM task, consistently with the DAN role in sustained attention and time monitoring. State 6, involving core regions of the default mode network (DMN), showed longer inter-activation intervals, suggesting a role in transient and sporadic processes (intention retrieval). Crucially, efficient time checks positively correlated with time spent in these two brain states, linking them to PM accuracy. Discussion: These findings suggest complementary roles of DAN and DMN regions in prospective remembering—continuous monitoring versus retrieval—and demonstrate how combining HMM with naturalistic paradigms offers new insights into the neural dynamics underlying real-world intention maintenance.