Beyond States: Investigating the Effects of Context on User Modeling with Feature-Conditioned Markov Models

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User behavior simulation is widely used to evaluate interactive information retrieval systems, but classical state-based approaches (e.g., Markov models) have limited ability to incorporate contextual information relevant for decision-making. We address this limitation by introducing a feature-conditioned Markov-style user model, in which transition probabilities are modeled as functions of positional, content-based, and interaction-derived features, enabling context-aware decision making while preserving the structural simplicity and computational efficiency of state-based models. Applying a multi-level framework that assesses predictive fit and behavioral fidelity, we analyze how different sources of contextual information contribute to realistic user simulation across multiple datasets, search settings, and feature configurations. Our results show that incorporating contextual features improves the models' ability to reproduce key aspects of real user interactions, but that their effectiveness hinges on search scenario and modeling objective. Instead of a one-size-fits-all solution, effective simulation requires task- and setting-specific feature selection. Our framework provides a practical and interpretable basis for making these choices.