Stimulus complexity and retelling output in aphasia: an exploratory single-case analysis

Discourse-based aphasia therapies often rely on narrative stimuli, yet the structural characteristics of these materials remain largely unexamined. While stimulus complexity may influence language production and cognitive load, few studies have proposed quantifiable methods for profiling discourse-level structure. This study developed and applied a transparent, rule-based structural profiling approach for narrative stimuli used in story-retelling therapy. Twelve stimuli were analyzed using a Python-based pipeline to extract sentence count, average sentence length, complex sentence ratio, and a surface-level segmentation proxy (Estimated Information Units). Composite scores were calculated using z-score normalization and used to classify stimuli into Low, Medium, and High complexity groups. As a proof-of-concept application, produced Information Unit (IU) change was examined descriptively to contextualize stimulus-level patterns across treated versus evaluation-only conditions. Stimuli varied in structural complexity, and IU change patterns were variable across stimuli. These findings demonstrate the feasibility of quantifying structural properties of narrative stimuli and illustrate how stimulus profiling can support stimulus calibration and sequencing in discourse-based aphasia research. Further work with larger samples is needed to validate these observations and evaluate broader applications of structural profiling in discourse-level intervention.