The information needed to refine constructs is already there—we just need to use it

Construct proliferation has long been viewed as a problem in psychology.In 1937, Gordon Allport observed an abundance of motivational constructs, calling it "the problem of motivation" (Allport, 1937, p. 110).In 1958, Robert Guion hosted a symposium on the many meanings of industrial "morale" (Guion, 1958).Today, analyses of the APA's PsycTest database have found over 38,000 constructs with 43,000+ unique measures-with over 80% only used once or twice (Anvari et al., 2025;Elson et al., 2023).Although new constructs often signal the health of a scientific field (Iliescu et al., 2024), they can also fragment research landscapes, prevent standard comparisons, and otherwise make it harder to do good science (Flake et al., 2022;Meehl, 1992;Yarkoni, 2022).Further complicating matters, Bowling and colleagues (2026) recommend that researchers identifying measures of potentially redundant constructs collect data and then analyze empirical covariation (see also Shaffer et al., 2016).This practice is resource intensive and becomes increasingly prohibitive in terms of cost and data quality as the number of constructs and items increases-it doesn't scale!As the number of constructs and measures increases, it becomes increasingly challenging to explore construct proliferation (Larsen et al., 2008;Wulff & Mata, 2025).In this context, calling for a construct moratorium sounds reasonable.We believe, however, that advances in natural language processing, particularly with transformer architectures, offer tools uniquely designed to structure large amounts of previously unstructured content (Vaswani et al., 2017).Although the traditional approaches require new data collection for every construct comparison, natural language approaches can evaluate the conceptual similarity of hundreds or thousands of constructs using only the content that already exists: the definitions, scale labels, and item wordings that compose our measures.From a content sampling perspective, each new measure represents an attempt to sample from the universe of possible psychological content (Highhouse, 2009;Loevinger, 1957).Instead of stopping construct development, therefore, we should be using natural language to refine the construct space.In other words, the information needed to refine constructs is already there; we just need to use it. Sampling psychological content with natural languageAlthough it is unclear whether psychological experiences are internally represented as natural language (Mandelbaum et al., 2022), our internal experiences are ultimately understood, shared, and then studied as such (Boyd & Schwartz, 2021).