Comprehensive verbal fluency features predict executive function performance

Abstract

Semantic verbal fluency (sVF) tasks are commonly used in clinical diagnostic batteries as well as in a research context. When performing sVF tasks to assess executive functions (EFs) the sum of correctly produced words is the main measure. Although previous research indicates potentially better insights into EF performance by the use of finer grained sVF information, this has not yet been objectively evaluated. To investigate the potential of employing a finer grained sVF feature set to predict EF performance, healthy monolingual German speaking participants (n = 230) were tested with a comprehensive EF test battery and sVF tasks, from which features including sum scores, error types, speech breaks and semantic relatedness were extracted. A machine learning method was applied to predict EF scores from sVF features in previously unseen subjects. To investigate the predictive power of the advanced sVF feature set, we compared it to the commonly used sum score analysis. Results revealed that 8 / 14 EF tests were predicted significantly using the comprehensive sVF feature set, which outperformed sum scores particularly in predicting cognitive flexibility and inhibitory processes. These findings highlight the predictive potential of a comprehensive evaluation of sVF tasks which might be used as diagnostic screening of EFs.

Figure 1

Correlation coefficients of true and predicted executive function variables computed with full feature set. Executive function variables were predicted based on 43 verbal fluency features. Results shown in this table illustrate executive function variables which could be predicted at p < 0.01 from verbal fluency data; Colour groups indicate EF domains and colour gradients denote different EF tests within this EF domain; NBN N-back non-verbal; NVLT Non-verbal learning test; SOA Stimulus onset asynchrony; SPM Raven’s standard progressive matrices; STROOP Stroop test; TMT Trail making test; WCST Wisconsin Card Sorting Test; WAF-G Divided attention; WAF-R Spatial attention.

Figure 2

Correlation coefficients of true and predicted executive function variables computed with classical feature set. Executive function variables were predicted based on the sum scores of all 3 semantic verbal fluency tests as well as the total sum score across these 3 tests, which led to a total number of four verbal fluency features. Results shown in this table illustrate executive function variables which could be predicted with p < 0.01 from verbal fluency data; Colour groups indicate EF domains and gradients denote different EF tests within this EF domain; MACK Mackworth Clock Test; NVLT non-verbal learning test; SPM Raven’s Standard Progressive Matrices; STROOP Stroop Test; WAF-G Divided attention; WAF-R Spatial attention.