Re-assessing the role of operational definitions in psychology

Operational definitions have long been a core approach to measuring and relating observed data to theoretical constructs in psychological science. However, many contemporary modeling approaches violate basic assumptions of operational definitions and operationalism more generally -- foregoing assumptions about objectivity, repeatability, independence, and fixed elicitation procedures. Counterintuitively, these departures imbue model-based definitions of constructs with superior measurement properties, such as improved reliability and validity, when compared to their operational counterparts. Instead of relying on operational definitions of constructs, we instead suggest that psychology can adapt relational definitions, representing constructs as latent variables in a multilevel generative model of behavior, self-report, or neuroimaging data. These model-based metrics can better reflect measurement error at multiple levels, account for the interactions between measurement devices (tasks, scales) and measurement objects (participants, processes), provide a holistic account of latent constructs and how they manifest across different measurements, facilitate convergence or discrimination tests among different tasks seeking to measure the same construct, and improve scientific communication by clarifying core psychological concepts. Relational definitions of important constructs should naturally emerge as we apply models more regularly, and these definitions and models will improve as we discover mathematical approaches that are suited to describing psychological processes.