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<p>Understanding how cognition unfolds from neurophysiological signals presents a promising direction for cognitive science studies and wearable-enabled human–robot interaction applications. However, uncovering latent neurodynamic geometry and temporal progression remains challenging and underexplored due to the lack of observable temporal organization for annotation and, consequently, the difficulty of training models in a supervised manner. This study proposes a representational learning method for this segmentation problem that shifts the solution away from statistical change-point detection methods and Hidden Markov Models. Our method employs self-supervised learning to discover emergent properties of the underlying temporal organization directly from the neurodynamic data itself. Four objectives are introduced and jointly optimized, including within-stage temporal predictability, boundary contrast, cross-trial alignment, and sparse stage-specific feature weights. Population-based evolutionary search was adopted to explore the multiple-basins-of-attraction landscape, where mutation and crossover govern the convergence process. We validated the framework on EEG recordings collected from participants performing an embodied road-crossing decision-making task, which simulates a typical cognitive processing transition from perceptual assessment to risk evaluation and decision commitment. Results showed that our method achieves an order-of-magnitude improvement in boundary contrast of the discovered stages, indicating that the learning behavior fundamentally changes the working principle from seeking local statistical consistency to capturing higher-order global temporal organization. This inter-stage divergence serves as the driving force for latent regime discovery while preserving local temporal continuity and coherence. Ablation and sensitivity studies demonstrate that the model performance is robust in identifying cross-trial transferable state geometry and handling data variability introduced by subject and stimulus heterogeneity. The reconstructed cognitive stages are also behaviorally plausible, and the dimensions attended by the model are well aligned with the neurophysiological underpinnings governing critical cognitive activities underlying each stage.</p>

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Self‑supervised evolutionary learning for progression-aware segmentation of neurodynamic time series during safety‑critical decision making

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Xiaoshan Zhou,
  • Carol C. Menassa,
  • Vineet R. Kamat

摘要

Understanding how cognition unfolds from neurophysiological signals presents a promising direction for cognitive science studies and wearable-enabled human–robot interaction applications. However, uncovering latent neurodynamic geometry and temporal progression remains challenging and underexplored due to the lack of observable temporal organization for annotation and, consequently, the difficulty of training models in a supervised manner. This study proposes a representational learning method for this segmentation problem that shifts the solution away from statistical change-point detection methods and Hidden Markov Models. Our method employs self-supervised learning to discover emergent properties of the underlying temporal organization directly from the neurodynamic data itself. Four objectives are introduced and jointly optimized, including within-stage temporal predictability, boundary contrast, cross-trial alignment, and sparse stage-specific feature weights. Population-based evolutionary search was adopted to explore the multiple-basins-of-attraction landscape, where mutation and crossover govern the convergence process. We validated the framework on EEG recordings collected from participants performing an embodied road-crossing decision-making task, which simulates a typical cognitive processing transition from perceptual assessment to risk evaluation and decision commitment. Results showed that our method achieves an order-of-magnitude improvement in boundary contrast of the discovered stages, indicating that the learning behavior fundamentally changes the working principle from seeking local statistical consistency to capturing higher-order global temporal organization. This inter-stage divergence serves as the driving force for latent regime discovery while preserving local temporal continuity and coherence. Ablation and sensitivity studies demonstrate that the model performance is robust in identifying cross-trial transferable state geometry and handling data variability introduced by subject and stimulus heterogeneity. The reconstructed cognitive stages are also behaviorally plausible, and the dimensions attended by the model are well aligned with the neurophysiological underpinnings governing critical cognitive activities underlying each stage.