Dissociating Reasoning and Planning through Markov and Quantum Metacognitive Dynamics

Abstract Online metacognitive processes support learning and decision-making by monitoring and integrating heuristic cues, such as evidence variability and conflict, throughout deliberation. Certain computational approaches, such as Markov and Quantum random-walk models (MRM and QRM), describe these metacognitive processes as dynamical systems, enabling inference of latent metacognitive states from response choices and response times without relying on explicit metacognitive judgments. MRM and QRM characterize how uncertainty is represented and updated during decision-making, with MRM tracking transitions between definite latent states and QRM allowing simultaneous superposition over competing latent states. In the present study, we evaluated the effectiveness of MRM and QRM in capturing latent metacognitive dynamics using explicit confidence judgments in reasoning tasks and subsequent behavioral patterns in planning tasks. QRM provided greater predictive utility than MRM across difficulty levels and high-conflict reasoning trials. In planning, MRM tended to provide greater predictive utility across harder planning conditions. Overall, model-derived TECS was comparable to or outperformed commonly used empirical measures in predicting subsequent behavior. These results suggest that Quantum dynamics may better capture reasoning under conflict, where participants integrate evidence across uncertainty levels. In contrast, Markov dynamics may align more naturally with the planning of sequential steps, particularly in harder planning conditions involving the evaluation of potential action paths. In other words, reasoning and planning may differ in how uncertainty is represented, monitored, and resolved over time, and random-walk models provide a principled means of characterizing these differences in cognitive dynamics.