<![CDATA[Computational accounts of human exploration usually assume a stationary policy, in which a single set of parameters for value sensitivity and uncertainty seeking generates every choice in a task, so that all within-task variation is treated as decision noise. Neuroscience instead treats exploration and exploitation as dissociable modes between which the brain switches, which a stationary model cannot represent. We introduce the Markov-Switching Reinforcement-Learning (MS-RL) model, in which a first-order hidden Markov chain governs transitions among a small number of latent decision regimes, each with its own softmax policy over a shared value-learning process. The three regimes are exploitation, directed exploration, and random exploration. The model contains the standard stationary account (one regime) and a temporally unstructured mixture (memoryless transitions) as nested special cases, making the stationarity assumption testable. We estimate the model using Expectation-Maximization with Viterbi decoding and select the number of regimes using the Bayesian information criterion. A parameter- and state-recovery study confirmed that the generating parameters and latent regime paths are recoverable (parameter correlations 0.88–0.94; state accuracy 86 percent; Cohen’s kappa ≈ 0.79). Applied to an openly available two-armed bandit dataset (46 adults, 13,800 choices), a three-regime MS-RL model was preferred over the nested baselines, two- and four-regime variants, and a single-regime model with smoothly time-varying value sensitivity. An ablation analysis attributes the largest gains to the latent regimes and temporal switching. The decoded regime path revealed a systematic within-game shift from directed exploration toward exploitation. The estimated transition matrix provides a per-participant measure of strategy change that has no counterpart in stationary models. We conclude that exploration is better described as a dynamic state than as a fixed trait, and that modeling it as a switching process is both more accurate and useful.]]>
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