Escaping the Jingle-Jangle Jungle: Increasing Conceptual Clarity in Psychology Using Large Language Models

Abstract

Psychology has long struggled with conceptual redundancy, particularly in the form of “jingle-jangle fallacies,” in which different constructs share the same label or the same construct is described using different terms. This lack of conceptual clarity has hindered cumulative knowledge and comparability across studies and subfields. We propose that large language models can help address this issue by placing constructs into a shared semantic space, enabling the systematic mapping of conceptual overlap and clarification of taxonomies and generating clearer construct definitions. Although automation plays a crucial role, we argue that meaningful progress requires a coordinated, community-wide effort, combining computational advances with expert deliberation. Our approach provides a pathway toward greater conceptual clarity in psychology, fostering a more unified and rigorous framework for the discipline.