To defend the objectivity and epistemic significance of science from claims that theory choice reflects scientists' values, McMullin has suggested that we clearly identify epistemic values - those such as truth which are the characteristic normative goals of science - and distinguish them from non-epistemic values. The question of the objectivity of a scientific inquiry then reduces to the question of whether it is primarily driven by epistemic values. This thesis illustrates how, using a decision-theoretic model, we can decide whether a motivation is epistemic or non-epistemic by looking at the consequences of potential actions that it attaches to. Building on this structural definition, we produce a succession of further definitions, distinguishing between epistemically and non-epistemically motivated inquiries, people, methods of persuasion and processes of interpretation. The resulting concept of epistemic value can demarcate science and non-science that is not committed to any particular method, nor to methodological anarchy. The model allows us to examine the potential behaviour of hypothetical agents. This method shows that epistemic motivation results in a desire for reliable information, while non-epistemic motivation makes information undesirable or even aversive. From this we get an empirical criterion distinguishing the two attitudes. Another useful hypothetical is to imagine a scientist who wants to assert a maximum number of truths by making a small number of statements. Under these circumstances, we find it can be rational to assert a theory with known false consequences, or a theory that is strictly meaningless but empirically adequate. Since the thesis makes use of Bayesian decision theory, the question naturally arises of how applicable it is to real people. The first part of the thesis defends the descriptive use of BDT in ordinary belief/desire explanation and shows that this does not involve any strong metaphysical presumptions about the entity being explained.