Escaping the jingle–jangle jungle: Increasing conceptual clarity in psychology using large language models

Psychology has long struggled with conceptual redundancy, particularly in theform of “jingle–jangle fallacies,” where different constructs share the same labelor the same construct is described using different terms. This lack of conceptualclarity has hindered cumulative knowledge and comparability across studies andsubfields. We propose that large language models can help address this issue byembedding constructs into a shared semantic space, enabling systematic mappingof conceptual overlap, clarifying of taxonomies, and generating clearer constructdefinitions. Although automation plays a crucial role, we argue that meaningfulprogress requires a coordinated, community-wide effort, combining computationaladvances with expert consensus. Our approach provides a pathway toward greaterconceptual clarity in psychology, ultimately fostering a more unified and rigorousframework for the discipline.