Adaptive behavior requires organisms to make decisions under uncertainty, balancing the exploitation of known options with exploration as environmental structure changes. Across ecology and neuroscience, this problem has been studied using distinct experimental and theoretical frameworks, including probabilistic choice, reversal learning, foraging tasks, reinforcement learning, and Bayesian inference. Here, we synthesize some of these ideas within a predictive processing perspective, arguing that they address a shared computational challenge: inferring latent environmental structure and adjusting behavior in response to different sources of variability. We distinguish key forms of uncertainty and review evidence that animals can regulate learning rates, persistence, and exploration according to the inferred origin of outcome variability. Laboratory paradigms such as probabilistic reversal learning provide controlled settings to dissociate sensitivity to noise from sensitivity to change, while foraging tasks reveal how local fluctuations are integrated with global estimates of environmental quality. Across species, apparent decision variability often reflects adaptive sampling rather than suboptimal noise. We further review evidence suggesting that cortical and subcortical circuits can encode predictions and environmental statistics, and that neuromodulator systems, including noradrenaline, acetylcholine, dopamine, and serotonin, modulate the influence of new evidence relative to prior beliefs. Together, these findings support a view of adaptive decision-making as hierarchical uncertainty resolution that operates across behavioral timescales and experimental contexts, and provide a framework for linking ecological decision rules, laboratory models, and neural mechanisms.
Treviño et al. (Thu,) studied this question.