What Matters in Semantic Feature Analysis: Practice-Related Predictors of Treatment Response in Aphasia

Area of Science:

• Neuroscience
• Speech-Language Pathology

Background:

• Aphasia, a language disorder resulting from brain damage, often impairs word retrieval.
• Semantic Feature Analysis (SFA) is a treatment approach aimed at improving naming abilities in individuals with aphasia.

Purpose of the Study:

• To determine the predictive value of practice-related variables in Semantic Feature Analysis (SFA) treatment.
• To investigate how treatment intensity and participant engagement influence naming accuracy in chronic aphasia.

Main Methods:

• 17 participants with chronic aphasia received daily SFA for 4 weeks.
• Treatment involved individualized semantic probe lists, administered sequentially in a multiple-baseline design.
• Naming accuracy was assessed at baseline, post-treatment, and at 1-month follow-up.

Main Results:

• A higher average number of participant-generated features per trial positively predicted naming accuracy for both treated and untreated items.
• Total treatment time and average trials per hour did not significantly predict naming accuracy.
• The number of treatment trials showed a trend toward predicting naming accuracy for treated items only.

Conclusions:

• The quantity of patient-generated semantic features appears more critical for SFA treatment success than treatment duration or frequency.
• These findings highlight the importance of active participant engagement in generating semantic features for improved naming, generalization, and maintenance in aphasia therapy.