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Continuous Prediction of Cognitive State Using A Marked-Point Process Modeling Framework.

Yalda Amidi, Angelique C Paulk, Darin D Dougherty

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
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    Summary
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    This study introduces a new marked-point process framework to analyze continuous neural data alongside discrete behavioral outcomes in cognitive tasks. This approach allows for estimating cognitive states at a fine temporal resolution, overcoming limitations of previous models.

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    Area of Science:

    • Cognitive Neuroscience
    • Computational Neuroscience
    • Dynamical Systems

    Background:

    • Cognitive tasks often yield behavioral data structured in discrete trials.
    • Existing neural models struggle with continuous neural data and discrete trial structures.
    • Accurate modeling of cognitive states requires fine temporal resolution.

    Purpose of the Study:

    • To develop a novel framework for characterizing multivariate behavioral outcomes in trial-structured cognitive tasks.
    • To estimate cognitive states at a fine temporal resolution using continuous neural data.
    • To model the relationship between observed behavior and underlying dynamical cognitive processes.

    Main Methods:

    • Proposed a state-space marked-point process modeling framework.
    • Defined the framework for response time and binary decision outcomes.
    • Developed state estimation, system identification, and an EM algorithm for parameter estimation.
    • Applied the framework to behavioral data from an emotional conflict resolution task.

    Main Results:

    • Successfully estimated cognitive processes at a fine temporal resolution.
    • Demonstrated the framework's ability to handle continuous neural data and discrete behavioral outcomes.
    • Showcased the model's effectiveness in an emotional conflict resolution task.

    Conclusions:

    • The marked-point process framework offers a powerful new tool for analyzing cognitive task data.
    • This approach enhances our ability to understand cognitive dynamics beyond traditional trial-by-trial analyses.
    • The model provides a more accurate estimation of cognitive states with high temporal precision.