The papers in this special section are aimed at contributions from computational neuroscience and psychology, artificial intelligence, machine learning, and affective computing, challenging and expanding current research on interpretation and estimation of human affective behavior from longitudinal data, i.e., single or multiple modalities captured over extended periods of time allowing efficient representation of behavior and inference in terms of affect and other socio-cognitive dimensions.

中文翻译：

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关于从纵向行为数据自动感知人类影响的特刊


这个特殊部分的论文旨在贡献来自计算神经科学和心理学、人工智能、机器学习和情感计算的贡献，挑战和扩展当前从纵向数据（即捕获的单一或多种模式）解释和估计人类情感行为的研究。在较长的时间内允许在情感和其他社会认知维度方面有效地表示行为和推理。