I'm investigating the role of situation selection as a dynamic sequencing control problem. How do agents adaptively arrange emotional experiences to pursue internal needs under changing constraints? I frame situation selection as a state-dependent, value-guided decision process under uncertainty, in which momentary affect, learned values, and available options interact recursively over time to order emotional inputs in ways that reflect current goals. This framework integrates insights from emotion regulation, affective forecasting, and reinforcement learning into a unified control-based account of how emotional trajectories are shaped before emotions unfold.

Across a series of controlled behavioral experiments, I show that situation selection is systematic, state-dependent, and multiply determined. People do not simply seek pleasure or avoid discomfort. Instead, current affect reliably biases which emotional experiences people want next, including their appetite for arousal, challenge, and informational value. Crucially, this work distinguishes short-term effectiveness from long-term adaptiveness: strategies that reduce immediate distress can impair learning, flexibility, and future regulation capacity, producing maladaptive trajectories across repeated decisions.

Methodologically, my work combines tightly controlled experimental paradigms with formal models of behavioral choice. I study prospective approach-avoid decisions over emotional stimuli to isolate forecasting, uncertainty, and within-person state effects. Analytically, I use multilevel models, mediation analyses, and computational modeling to characterize how forecasted valence, interest, and motivational state jointly shape choice. The result is a mechanistic, process-level account of how emotional environments are selected, and how repeated selections accumulate to shape emotional life over time.