Decoding a Cognitive Performance State from Behavioral Data in the Presence of Auditory Stimuli.

Abstract

Objective: Cognitive performance state is an unobserved state that refers to the overall performance of cognitive functions. Deriving an informative observation vector as well as the adaptive model and decoder would be essential in decoding the hidden performance.

Methods: We decode the performance from behavioral observation data using the Bayesian state-space approach. Forming an observation from the paired binary response with the associated continuous reaction time may lead to an overestimation of the performance, especially when an incorrect response is accompanied by a fast reaction time. We apply the marked point process (MPP) framework such that the performance decoder takes the correct/incorrect responses and the reaction time associated with correct responses as an observation. We compare the MPP-based performance with two other decoders in which the pairs of binary and continuous signals are taken as the observation; one decoder considers an autoregressive (AR) model for the performance state, and the other one employs an autoregressive-autoregressive conditional heteroskedasticity (AR-ARCH) model which incorporates the time-varying and adaptive innovation term within the model.

Results: To evaluate decoders, we use the simulated data and the n-back experimental data in the presence of multiple music sessions.

Conclusion: The Bayesian state-space approach is a promising way to decode the performance state. With respect to individual perspective, the estimated MPP-based and ARCH-based performance states outperform the AR-based estimation. Based on the aggregated data analysis, the ARCH-based performance decoder outperforms the other decoders.

Significance: Performance decoders can be employed in educational settings and smart workplaces to monitor one's performance and contribute to developing a feedback controller in closed-loop architecture to improve cognitive performance.