Bayesian Inference of Hidden Cognitive Performance and Arousal States in Presence of Music

Abstract

Poor arousal management may lead to reduced cognitive performance. Specifying a model and decoder to infer the cognitive arousal and performance contributes to arousal regulation via non-invasive actuators such as music. Methods: We employ a Bayesian filtering approach within an expectationmaximization framework to track the hidden states during the n-back task in the presence of calming and exciting music. We decode the arousal and performance states from the skin conductance and behavioral signals, respectively. We derive an arousal-performance model based on the Yerkes-Dodson law. We design a performance-based arousal decoder by considering the corresponding performance and skin conductance as the observation. Results: The quantified arousal and performance are presented. The existence of Yerkes-Dodson law can be interpreted from the arousal-performance relationship. Findings display higher matrices of performance within the exciting music. Conclusions: The performance-based arousal decoder has a better agreement with the Yerkes-Dodson law. Our study can be implemented in designing non-invasive closed-loop systems.

A multimodal dataset for investigating working memory in presence of music: a pilot study

“…Cognitive performance is a hidden brain state that demonstrates the general performance of one's cognitive functions (Khazaei et al, 2024 ). The cognitive functions of humans can be divided into two groups, namely, basic cognitive functions and higher-level cognitive functions.…”

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“…Cognitive performance is a hidden brain state that demonstrates the general performance of one's cognitive functions (Khazaei et al, 2024). The cognitive functions of humans can be divided into two groups, namely, basic cognitive functions and higher-level cognitive functions.…”