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@INPROCEEDINGS{Kuhles:1022031,
      author       = {Kuhles, Gianna and Camilleri, Julia and Hamdan, Sami and
                      Heim, Stefan and Eickhoff, Simon and Patil, Kaustubh and
                      Weis, Susanne},
      title        = {{P}itfalls in using {ML} to predict executive function
                      performance by linguistic variables},
      school       = {Heinrich-Heine Universität Düsseldorf},
      reportid     = {FZJ-2024-01168},
      year         = {2024},
      abstract     = {Introduction: A connection between executive function (EF)
                      performance and prosody was previously found in numerous
                      mental disorders (Filipe et al., 2018; Le et al., 2011;
                      Nevler et al., 2017). However, it is so far unresolved how
                      different subdomains of EF and prosody are related to each
                      other. Thus, the present study strived to explore the
                      relationships of EF and prosody using a machine learning
                      (ML) regression approach aiming to predict EF performance
                      from various prosodic features.Methods: Healthy participants
                      (n = 231) performed several spontaneous speech tasks, as
                      well as commonly used EF tests, spanning different EF
                      subdomains. Prosodic features were extracted automatically
                      from the speech samples. We then used a standard ML approach
                      to predict EF performance from prosody. As is common, we
                      controlled for confounding effects of age, sex, and
                      education Subsequently, the most predictive features for
                      each of the successfully predicted EF variables were
                      identified.Results: Results indicated that spectral prosodic
                      parameters were particularly important for successful
                      prediction, which is in line with previous literature (Le et
                      al., 2011). However, a thorough assessment of the analysis
                      pipeline revealed a leakage of the effects of sex, age, and
                      education into the prediction, basically indicating the
                      prediction performance – at least for some of the
                      variables – was mainly driven by sex, age, and education
                      confounds, rather than our prosody features. While results
                      of ML analyses might appear to fit with previous results,
                      present findings strongly underline the importance of
                      educated control of any ML pipeline. Thus, we suggest
                      running sanity checks for predicting cognitive performance
                      as well as caution with the interpretation of ML prediction
                      results.Discussion:Taking these methodological
                      considerations into account, the outcome of the present
                      study provides insights into the specific relationships
                      between prosody and executive function performance,
                      concurrently warning about possible pitfalls with the use of
                      ML. While our findings are in line with previous studies
                      (Filipe et al., 2018; Le et al., 2011; Nevler et al., 2017),
                      further research should investigate whether the predictive
                      power of prosody can serve as a biomarker of executive
                      dysfunction in the future.},
      month         = {Jan},
      date          = {2024-01-22},
      organization  = {European Workshop on Cognitive
                       Neuropsychology, Bressanone/Brixen
                       (Italy), 22 Jan 2024 - 26 Jan 2024},
      subtyp        = {After Call},
      cin          = {INM-7 / INM-1},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      JL SMHB - Joint Lab Supercomputing and Modeling for the
                      Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-01168},
      url          = {https://juser.fz-juelich.de/record/1022031},
}