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 pretrained semantic model was used to estimate the semantic relatedness between features and targets.

RESULTS

Participants were 2.6 (95% confidence interval [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% confidence interval [CI; 0.2, 0.4]) in the probability of a correct response at the end of the trial; a 1 SD 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.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.32159406.