Tracking Theory Reception in Psychological Science: A Discourse-Based Approach with a Case Study of Memory-Decay Theory

Psychology is frequently described as theoretically fragmented, yet the field lacks scalable tools for quantifying whether theories consolidate or remain contested over time. We introduce a discourse-based approach that tracks theory reception by classifying how published articles position themselves relative to a target theory—whether they explicitly support it, oppose it, rely on it tacitly, or engage ambiguously. Using structured literature retrieval and full-text processing, we apply this approach at scale with the aid of a large language model (LLM) used as an annotation support tool, while anchoring all classifications in expert-defined stance categories. As a case study, we analyze 472 articles engaging with memory-decay theory published between 1958 and 2023. LLM-assisted stance classifications show strong agreement with expert judgments using only the article abstracts (Cohen’s κ ≈ .80–.83). Across six decades of publications, stance distributions exhibit no systematic increase in tacit acceptance, no sustained decline in opposition, and no consistent drift toward consensus, suggesting theoretical stagnation. Taken together, this work provides a reusable workflow for tracking theory reception over time using features of scientific discourse. All materials required to reproduce and adapt the pipeline—including code, search strategies, screening criteria, and article-level stance labels—are openly available.