Practice-Related Predictors of Semantic Feature Verification Treatment for Aphasia

Abstract

Purpose: Specifying the active ingredients in aphasia interventions can inform treatment theory and improve clinical implementation. This secondary analysis examined three practice-related predictors of treatment response in semantic feature verification (SFV) treatment. We hypothesized that (a) successful feature verification practice would be associated with naming outcomes if SFV operates similarly to standard feature generation semantic feature analysis and (b) successful retrieval practice would be associated with naming outcomes for treated, but not semantically related, untreated words if SFV operates via a retrieval practice–oriented lexical activation mechanism. Method: Item-level data from nine participants with poststroke aphasia who received SFV treatment reported in the work of Evans, Cavanaugh, Quique, et al. (2021) were analyzed using Bayesian generalized linear mixed-effects models. Models evaluated whether performance on three treatment components (facilitated retrieval, feature verification, and effortful retrieval) moderated treatment response for treated and semantically related, untreated words. Results: There was no evidence for or against a relationship between successful feature verification practice and treatment response. In contrast, there was a robust relationship between the two retrieval practice components and treatment response for treated words only. Discussion: Findings were consistent with the second hypothesis: Retrieval practice, but not feature verification practice, appears to be a practice-related predictor of treatment response in SFV. However, treatment components are likely interdependent, and feature verification may still be an active ingredient in SFV. Further research is needed to evaluate the causal role of treatment components on treatment outcomes in aphasia.