Comprehensive verbal fluency features predict executive function performance.

Area of Science:

• Neuroscience
• Cognitive Psychology

Background:

• Semantic verbal fluency (sVF) tasks are standard for assessing executive functions (EFs).
• Current assessment primarily relies on the total number of correctly produced words.
• Previous research suggests more detailed sVF analysis could yield richer EF insights, but this remains unevaluated.

Purpose of the Study:

• To objectively evaluate the predictive potential of a comprehensive set of sVF features for EF performance.
• To compare the efficacy of advanced sVF feature analysis against traditional sum score methods.

Main Methods:

• A machine learning approach was used to predict EF scores from extracted sVF features.
• Features included sum scores, error types, speech breaks, and semantic relatedness.
• Data from 230 healthy monolingual German speakers were analyzed using a comprehensive EF test battery and sVF tasks.

Main Results:

• The comprehensive sVF feature set significantly predicted 8 out of 14 executive function tests.
• This advanced feature set demonstrated superior predictive power compared to traditional sum scores, especially for cognitive flexibility and inhibitory processes.

Conclusions:

• A detailed evaluation of sVF tasks holds significant predictive potential for executive functions.
• This comprehensive approach may serve as a valuable diagnostic screening tool for EFs, offering more nuanced insights than current methods.