Heinrich, Nils Wendel2026-05-132026-05-132026https://epub.uni-luebeck.de/handle/zhb_hl/3651Humans act in environments that are uncertain, dynamically changing, and only partially controllable. Successful behavior in such settings requires more than reactive feedback correction or rule-based strategies. Agents must continuously regulate how precisely action goals are specified, how strongly predictions guide action, and how they adapt when control deteriorates. Despite extensive work on motor control, decision-making, and learning, existing accounts lack a unified explanation of how behavioral strategies, subjective control beliefs, and computational learning mechanisms jointly support adaptive action control in these dynamic environments that are characterized by uncertainty and action–effect contingencies that change over time. This dissertation develops and empirically grounds a multi-level framework of action control that integrates hierarchical accounts of intention, graded degrees of control, and belief-based regulation of agency. Action control emerges from the dynamic interaction between behavioral markers of ongoing regulation, cognitive forward models supporting both reactive and proactive action selection, and computational mechanisms driving parameter adjustments of these internal forward models. Across four studies that share the same continuous task environment, action control is investigated at behavioral, cognitive, and computational levels. Studies 1 and 2 show that gaze behavior implements a hierarchical organization of action goals over different temporal horizons. Close fixations support immediate control and state-dependent regulation, whereas distant fixations anchor attention to future task-relevant locations, supporting proactive planning. These complementary fixation types adapt flexibly to changes in environmental dynamics and action–effect contingencies, revealing how perceptual control implements both reactive and anticipatory action. Study 3 treats the Sense of Control, the subjective feeling of being in control, as a latent belief that is updated via Bayesian integration. Participants provided control ratings after each trial, serving as observable indicators of their evolving beliefs. Analysis showed that participants derived their ratings by integrating prior expectations with observed performance outcomes. Control ratings decreased when environmental outcomes violated participants’ expectations, and individuals differed in how strongly they weighted performance evidence, indicating variability in belief updating. These results suggest that the Sense of Control reflects the predictive accuracy of internal forward models. Study 4 translated these insights into a cognitive architecture combining forward predictions with error-driven learning. The Sense of Control from Study 3, modeled using the Bayesian framework, was reinterpreted as uncertainty associated with an internal forward model of the task’s environmental dynamics. This uncertainty modulated whether forward predictions were used during action selection. Simulations showed that when high uncertainty led the architecture to suspend forward prediction, learning was severely impaired. Adaptation was slow, performance remained below that of human participants, and internal representations failed to improve because no informative prediction errors were generated. When the architecture relied on predictions despite high uncertainty, corrective error signals enabled efficient parameter updating, resulting in learning trajectories and performance comparable to human participants. This demonstrates that engaging predictive mechanisms early, even under uncertainty, is essential for effective adaptation to dynamic environments. Together, these studies establish a process-based framework in which adaptive action control is neither purely reactive nor rigidly predictive. Instead, it emerges from coordinated mechanisms operating across multiple levels and timescales, with uncertainty guiding when internal models are updated incrementally and when they must be fundamentally restructured. By linking gaze behavior, subjective control beliefs, and predictive learning, this dissertation provides a foundation for investigating structural learning, proactive control, and failures of agency in both human and artificial systems.enAction ControlDynamic EnvironmentsEye-trackingSense of ControlLearningStatistical ModelingCognitive Modeling004thesis.doctoralurn:nbn:de:gbv:841-2026051303