Abstract
In current practice, psychological explanations typically present a narrative in which a theory renders a putative empirical phenomenon intuitively likely. However, whether the theory actually implies the phenomenon in question is also left to this intuition. To design a test for such a theory, different experts iterate through possible experimental setups until they agree that a particular manipulation should show the effect. The fact that this crucial link has to be fleshed out by polling experts, reveals an Achilles’ heel in current psychological theories. Nobody ever had to ask Einstein what would happen to light in the famous eclipse that Eddington observed (Dyson et al., 1920), because Einstein’s opinion was irrelevant. The reason for this is that Einstein’s theory can be and is implemented in a formal model, which means that every competent researcher can check whether the theory does or does imply a given phenomenon. That such independent verification of theoretical implications is not possible in many cases in psychology has direct consequences for the evaluation of the evidence for and against theories. For example, Vohs et al. (2021) suggest that the empirical phenomena associated with the theory of ego-depletion are not robust, as the experimental tasks used did not produce these phenomena. However, it is difficult to gauge whether or not this constitutes evidence against ego-depletion, because in the absence of an unambiguous formalization we cannot even be sure that the theory implies the anticipated phenomena. This points to an important desideratum for explanatory systems, namely that they should be (specific enough to be) encoded in a formal system (e.g., a set of mathematical equations, logical formalisms, or model simulations). We contribute to this task by proposing an account of productive explanation, in which the theory specifies a formal model that produces statistical patterns that reflect empirical phenomena that are purportedly explained by the theory. Expressing the theory in a formal model, and showing how that formal model produces patterns in data, brings transparency to the relation between the theory and the empirical phenomenon. To achieve this aim, we combine insights taken from recent discussions on theory construction (Borsboom et al., 2021; van Rooij and Baggio, 2021) and philosophical considerations (e.g., Cummins, 2000; Haig, 2005) with existing approaches to indirect inference in system dynamics (Haslbeck et al., 2021; Hosseinichimeh et al., 2016) to arrive at a workable methodology for establishing empirical implications. This productive explanation methodology involves a) translating a verbal theory into a set of model equations, b) representing empirical phenomena as statistical patterns in putative data, c) assessing whether the formal model actually produces the targeted phenomenon. In addition, we explicate a number of important criteria for evaluating the goodness of this explanatory relation between theory and empirical phenomenon.
