Inside Semantic Feature Analysis: A Within-Trial Analysis of Feature Quantity and Quality

Purpose: Aphasia rehabilitation increasingly emphasizes the importance of understanding the mechanisms and ingredients underlying behavioral interventions. Semantic Feature Analysis (SFA) is a commonly-used intervention for anomia in aphasia but there is little evidence directly examining its active ingredients. Using within-trial responses during SFA, we sought to explore how generating semantic features might facilitate naming improvements. Method: A retrospective analysis evaluated data collected from a clinical trial focused on intensive SFA treatment for individuals with chronic aphasia. The study included 44 adults with chronic aphasia following left-hemisphere stroke. A pre-trained semantic model was used to estimate the semantic relatedness between features and targets.Results: Participants were 2.6 (95% CI: [2.16, 3.12]) times more likely to correctly name target words after engaging in feature generation. Each additional feature generated was associated with a three percentage-point increase (95% CI: [0.2, 0.4]) in the probability of a correct response at the end of the trial; a one standard deviation increase in semantic similarity (i.e., feature quality) was associated with a six percentage-point increase (95% CI: [0.4, 0.10]). Model comparison favored semantic similarity over feature generation count in predicting final response accuracy.Conclusions: Findings provide converging evidence that semantic feature generation is an active ingredient in SFA treatment, emphasizing the importance of feature quantity and semantic quality, consistent with a spreading activation account of SFA’s benefits. Further research is warranted to validate relatedness values from semantic model embeddings, and to explore the relationship between within-trial feature generation and generalization to semantically related but untreated words.