Human decision-making in uncertain environments depends not only on how individuals learn from experience but also on how contextual factors modulate this learning. The present project investigates how environmental valence (win vs. loss domain), feedback availability (full vs. partial), and regulatory focus (exploration vs. exploitation emphasis) jointly shape value learning and evidence accumulation. N = 60 participants completed an adapted two-stage decision-making task in which transitions between states follow a Markovian structure, while the above manipulations induce systematic shifts in motivational and informational context. We compared model-free and hybrid (model-free + model-based) learning algorithms embedded within a reinforcement learning-diffusion decision model framework that maps subjective value differences onto drift rate and overall value context onto decision thresholds. This approach allowed us to quantify how learning strategies translate into temporal decision dynamics. We further tested the condition effects of environmental valence, feedback availability, and regulatory focus on cognitive parameters. Hierarchical Bayesian model comparison results reveal the best fit for hybrid learning algorithms. Both environmental valence and regulatory focus interact with learning rates, with higher learning rates in the win domain and a promotion focus, and lower learning rates in the loss domain and a prevention focus. Feedback availability influences non-decision time and accuracy. The study offers a way to characterize how motivational and informational factors modulate the interplay between model-free and model-based learning and the dynamics of evidence accumulation. We discuss how the findings advance a mechanistic understanding of adaptive decision-making.