ArticleAphasiology2025
Evolution of Word Production Errors after Typicality-Based Semantic Naming Treatment in Individuals with Aphasia.
Article in Aphasiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The Complexity Account for Treatment Efficacy (CATE) has been applied to semantic typicality in aphasia naming therapy, i.e., training atypical items of a category would improve naming of typical untrained-related items. However, most aphasia treatment studies have implemented a binary scoring system to measure response accuracy, which may not thoroughly reveal linguistic mechanisms underlying aphasia recovery. Aims: The current study investigated the evolution of error patterns following typicality-based Semantic Features Analysis (SFA) treatment in individuals with post-stroke aphasia. Methods & Procedures: Thirty individuals with chronic aphasia participated in a typicality-based SFA treatment, and ten individuals with chronic aphasia served as controls. The treatment participants and controls completed a naming screener before and after either a treatment period or a no-treatment period, respectively. Responses were coded using an error coding scale and analyzed with mixed-effects models. Outcomes & Results: Treatment participants demonstrated significant treatment and generalization effects, as captured by significant improvements on the error coding system for both trained and untrained items. However, the group-level analysis did not reveal significant generalization from training atypical items to untrained typical items. Subgroup analyses based on participants' performance in treatment showed significant gains in naming untrained typical items from training atypical items in responders, but improved naming of untrained atypical items from training typical items in nonresponders. Conclusions: These findings suggest different linguistic mechanisms underlying aphasia recovery and highlight the importance of investigating treatment and generalization effects using a fine-grained error coding system as a complement to a binary scoring system.
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