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

Area of Science:

• Linguistics
• Speech-Language Pathology
• Computational Linguistics

Background:

• Discourse-based aphasia therapies commonly use narrative stimuli.
• The structural complexity of these narrative materials is not well understood.
• Quantifiable methods for profiling discourse-level structure are lacking.

Purpose of the Study:

• To develop and apply a transparent, rule-based structural profiling approach for narrative stimuli in story-retelling therapy.
• To quantify the structural properties of narrative stimuli.
• To classify stimuli into complexity groups (Low, Medium, High).

Main Methods:

• Analyzed 12 narrative stimuli using a Python-based pipeline.
• Extracted features: sentence count, average sentence length, complex sentence ratio, and Estimated Information Units (EIUs).
• Calculated composite scores using z-score normalization for complexity classification.

Main Results:

• Narrative stimuli exhibited variability in structural complexity.
• EIUs varied across stimuli, indicating differences in information flow.
• Proof-of-concept analysis showed variable Information Unit (IU) change patterns across conditions.

Conclusions:

• Demonstrated the feasibility of quantifying structural properties of narrative stimuli.
• Stimulus profiling can aid in calibrating and sequencing materials in discourse-based aphasia research.
• Further research with larger samples is needed to validate findings and explore broader applications.