Major advances in the science of learning are rarely incorporated into real-world settings with much success. Progress has been held back by two gaps in the literature. First, a lack of mechanistic models for the underlying neural processes that generate learning outcomes. Second, models for the organizational processes for implementing change are so complex that they are not understandable to practitioners who try to use them. I argue that by framing implementation processes as ill-structured problems, models for organizational change can be simplified into a user-friendly framework of processes, principles, and pitfalls. Like all ill-structured problems, implementing the science of learning into human systems entails intricate human factors. Trust stands out as a key human factor because learning (either in a school setting or in the workplace) entails accepting vulnerability to other people. A systematic review and Game Theory analysis suggests that trust may be a driver of a wide spectrum of student behaviors typically attributed to motivation. A multi-phase, mixed-methods study finds evidence that the strategy of clarifying ambiguous expectations can boost students’ effortful cooperation on learning activities, and the effects seem not to be mediated by classical motivational constructs nor classical trust constructs. I argue that classical theories of trust do not represent the actual underlying psychological processes by which trust is learned and enacted, and that these processes are best understood to be learning and decision-making processes. Modern neuroscience now provides the means to model learning and decision-making down to the level of concrete information processing operations occurring in the physical brain, but until now, this has yet to be translated into a model of learning for educators and education researchers. Defining system boundaries of neural systems that each perform complementary functions by extracting, storing, and processing different kinds of information yields predictions about how to optimize human learning. This leads to a new taxonomy of trust that outlines unique learning mechanisms, functionalities, and error modes for three forms of trust corresponding to three neural action-selection systems: Deliberative, Procedural, and Instinctual. Thereby generating new predictions about how trust is learned and enacted in human environments.