Mental Demand without brain signals: a practical approach to modeling the N-Back task

Open source is a moving force in scientific research. The Cognitive Modeling community largely benefits from this mutualistic relationship, but how large is the actual gap between state-of-the-art methods and practical use in research settings? This study exemplifies complementary approaches for using open source to model cognitive mental demand from three conceptual and practical perspectives: subjective reports, peripheral biosignals, and psychochronometric behavior. Participants answered the NASA-TLX to report their perceived mental demand related to the task. Heart rate variability and pupillometry were the selected biosignals to assess the mental demand of the N-Back task. A demonstration of two uses of ML in the process of feature engineering and exploratory analysis showed that self-report alone is enough to predict a participant's trial level with a Cohen's Kappa of 0.9187 (SD = 0.02), and up to .9290 (SD = 0.02) when combined with the response times during the trial. Linear Mixture Models confirmed the effect of the N-Back level on participant's mental demand. Both the HRV and pupillometry mixture models showed a significant effect of the N-Back level on the features extracted from the biosignals, but HRV features demonstrated to be cost-effective and reliable. The selected feature for HRV: the proportion of low-frequency power over the total power of the HRV predicted the N-Back level with a Cohen’s Kappa of 0.8571 (SD = 0.01) in our best models.