Bridging theory, models, and measurement in psychological science
Psychology theorizes, models, and measures in disconnected ways.
TMM exists to change that.
Over the past two decades, computational models have become increasingly prominent in psychological science, allowing researchers to formalize theoretical claims and state assumptions explicitly. Simultaneously, advances in statistics and measurement theory have opened new avenues for quantifying human behavior with greater rigor.
Yet at the intersection of theory, measurement, and modeling, conceptual and methodological gaps limit the explanatory power of our models and the validity of our measurements. These challenges restrict how successfully models capture psychological theory, and how well measurement instruments capture psychological constructs.
The Theory, Model, Measurement (TMM) network exists to address these fundamental issues by connecting researchers across domains and engaging with both conceptual limitations and technical solutions. We bring together computational modelers, measurement theorists, philosophers of science, and empirical researchers to advance psychology toward a truly quantitative science.
At the intersection of theory, measurement, and modeling, two crucial gaps limit psychology's progress as a quantitative science.
The relationship between prediction and explanation in theory assessment remains poorly understood. Models that successfully forecast outcomes are not necessarily theoretically grounded—offering impressive predictive accuracy without revealing the psychological processes that underlie observed patterns.
Predictive success can be mistaken for theoretical progress while explanatory models remain confined to narrow empirical domains.
The connection between measurement and theoretical constructs remains fundamentally underspecified. This encompasses the coordination problem—where latent constructs may lack clear alignment with how they are measured—and the inference problem—where statistical regularities may not meaningfully map onto psychological constructs.
Both problems are mediated by auxiliary assumptions that are rarely made explicit—creating situations where statistical convenience is confused with psychological reality.
The risk: Without addressing these gaps, psychology risks producing increasingly sophisticated models that remain descriptively impressive but theoretically shallow—offering forecasts without understanding and parameters without psychological interpretation.
Making auxiliary assumptions explicit and understanding what our models can—and cannot—reveal.
Bridging computational modeling, measurement theory, and philosophy of science under one conversation.
Connecting methodological specialists with empirical researchers so that neither works in isolation.
Acknowledging the limits of psychological inference and resisting theoretical shallowness.