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

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Bibliographic Details
Title:	Inside Semantic Feature Analysis: A Within-Trial Analysis of Feature Quantity and Quality.
Authors:	Cavanaugh, Robert¹ rcavanaugh1@mghihp.edu, Swiderski, Alexander^2,3,4, Goldberg, Emily^2,3, Hula, William D.^2,3, Evans, William S.², Dickey, Michael Walsh^2,4
Source:	American Journal of Speech-Language Pathology. Jul2026, Vol. 35 Issue 4, p1807-1817. 11p.
Subject Terms:	Chronic diseases, Speech therapy, Anomia, Research funding, Rehabilitation of aphasic persons, Treatment effectiveness, Stroke rehabilitation, Semantics, Stroke, Confidence intervals, Data analysis software, Disease complications
Abstract:	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. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	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. [ABSTRACT FROM AUTHOR]
ISSN:	10580360
DOI:	10.1044/2026_AJSLP-25-00515