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Using Neural Networks to Uncover the Relationship between Highly Variable Behavior and EEG during a Working Memory Task with Distractors

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  • Christine Beauchene

    (Neuromedical Control Systems Laboratory, Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
    These authors contributed equally to this work.)

  • Silu Men

    (Neuromedical Control Systems Laboratory, Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
    These authors contributed equally to this work.)

  • Thomas Hinault

    (U1077 INSERM-EPHE-UNICAEN, 14032 Caen, France)

  • Susan M. Courtney

    (Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA
    These authors contributed equally to this work.)

  • Sridevi V. Sarma

    (Neuromedical Control Systems Laboratory, Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
    These authors contributed equally to this work.)

Abstract

Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants ( r = 0.53 ± 0.08 ). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants ( r = 0.87 ± 0.04 ). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions.

Suggested Citation

  • Christine Beauchene & Silu Men & Thomas Hinault & Susan M. Courtney & Sridevi V. Sarma, 2022. "Using Neural Networks to Uncover the Relationship between Highly Variable Behavior and EEG during a Working Memory Task with Distractors," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1848-:d:826225
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    References listed on IDEAS

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    1. Frederic M. Stoll & Vincent Fontanier & Emmanuel Procyk, 2016. "Specific frontal neural dynamics contribute to decisions to check," Nature Communications, Nature, vol. 7(1), pages 1-14, September.
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