Productive Explanation: A Framework for Evaluating Explanations in Psychological Science

Abstract

he explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains the phenomena it purports to explain. We aim to address this shortcoming in psychology by characterizing the nature of explanation and proposing a framework in which to evaluate explanation. We present a productive account of explanation: a theory explains a phenomenon if and only if it can produce a statistical pattern representing the phenomenon $P$ (bottom panel of Figure 1). Using this account, we outline a workable methodology of explanation. This methodology entails (a) translating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we explicate a number of methodological criteria for evaluating the goodness of this explanatory relation between theory and empirical phenomenon and conclude with a discussion about how this productive explanation methodology can be used within the broader aim of constructing and developing psychological theories.

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