Chondrolaryngoplasty: Vocal Considerations, Outcomes, and Effects on Acoustic Measures of Voice.

Animals must continuously balance behavioral goals with the energetic costs of achieving them. This trade-off is especially critical during hunting and foraging, where movement is essential but metabolically expensive. Understanding how such trade-offs are implemented algorithmically in the brain, and how they adapt to changes in the body and environment, remains a central question in neuroscience and ethology (–). In particular, how animals dynamically adjust action selection to minimize energetic cost during natural behavior has rarely been quantified directly, and its developmental robustness and environmental flexibility remain largely unexplored. Larval zebrafish offer a powerful model system for investigating these issues. By 5 d postfertilization (dpf), they engage in visually guided prey capture using a discrete set of stereotyped movement bouts (, ). These bouts are generated by a relatively well-understood neural circuit architecture (, ), and the biomechanics of larval swimming are governed by relatively simple fluid dynamics (, ). Previous studies have proposed that zebrafish behavior may be energy-aware or reward-maximizing, but these inferences have been largely indirect (, ). Critically, no prior work has directly quantified the energetic costs in Joules of individual movement bouts, nor examined how the nervous system adapts action selection in response to changing energetic constraints during growth or environmental perturbation. Here, we combined high-speed behavioral imaging, computational fluid dynamics (CFD), and reinforcement learning to test whether larval zebrafish use an energy-aware control strategy during prey hunting. We show that, while the repertoire of movement types remains stable over development, the relative frequency with which each type is selected changes systematically to match its energetic cost. This energy–probability relationship is preserved across developmental changes in relative energy costs and is actively reoptimized when fish are reared in a high-viscosity environment. A reinforcement learning agent trained to minimize energy while catching prey independently learns the same strategy. These results suggest that larval zebrafish implement a flexible, energy-minimizing control policy for goal-directed behavior. This provides direct evidence that freely behaving animals can integrate energetic considerations into motor planning, and that this optimization emerges robustly across development and in novel environments. By linking biomechanics, behavior, and computational models of control, our results reveal a generalizable principle of action selection under energy constraints.