ArticleAmerican journal of speech-language pathology2026
Inside Semantic Feature Analysis: A Within-Trial Analysis of Feature Quantity and Quality.
Article in American journal of speech-language pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Concept-Level Semantic Representations Remain Decodable in Chronic Post-Stroke Aphasia.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
purposeAphasia 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.
methodA 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.
resultsParticipants 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
conclusionsFindings 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.
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