Decoder Design for Concurrent Estimation of Arousal and Performance from One Continuous and Two Binary Observations

Human cognitive functions are linked to hidden cognitive states, i.e., arousal and performance. The Yerkes-Dodson law suggests an inverted-U link between these two states, and they may need to be decoded concurrently. However, conventional decoders decode these states separately without including their non-linear interplay.

We develop a concurrent arousal-performance (CAP) decoder using a Bayesian state-space framework that accounts for their psychological link. The correctness of response and arousal events are binary data to be linked to performance and arousal states, respectively. The reaction time is a continuous observation jointly linked to both states via a quadratic function. We evaluate the framework on simulated data and two experimental datasets. Specifically, data acquired on subjects performing 1-back and 3-back memory tasks during which they are elicited by relaxing, exiting, and AI-generated relaxing music, as well as by smell fragrance and intake coffee are used.

The CAP decoder outperforms the previously developed decoder in reflecting the inverted-quadratic arousal-performance link, suggesting the presence of the Yerkes-Dodson law. The decoded arousal state peaks during an exciting music session, while the decoded performance state is aligned with the task difficulty.

The developed framework reliably decodes the hidden arousal and performance and reveals their link.

This research advances the safe personalized intervention design.