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首页> 外文会议>Joint IEEE International Conference on Development and Learning and Epigenetic Robotics >Toward Empathic Communication: Emotion Differentiation via Face-to-Face Interaction in Generative Model of Emotion
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Toward Empathic Communication: Emotion Differentiation via Face-to-Face Interaction in Generative Model of Emotion

机译：走向共情交流：在情感生成模型中通过面对面互动进行情感分化

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In this paper, a model of emotions is proposed based on various neurological and psychological findings. The proposed model consists of three layers: the external/internal appraisal layer, the prediction/decision-making layer, and the emotional memory layer. We implement the proposed model by integrating some deep learning modules such as recurrent attention model, convolutional long short-term memory, and deep deterministic policy gradient. We set a “facial expression” task simulating mother-child interactions and verified emotion differentiation during the task. We also examine the trained model in the “still face” experiment. A claim in this study is that it is a very important step for the constructive approach to compare the proposed model with real human subjects in the same experiment that was carried out in the psychological studies.
机译：在本文中，基于各种神经和心理发现，提出了一种情绪模型。所提出的模型包括三层：外部/内部评估层，预测/决策层和情绪记忆层。我们通过整合一些深度学习模块（如循环注意力模型，卷积长短期记忆和深度确定性策略梯度）来实现该模型。我们设置了一个“面部表情”任务来模拟母子互动，并验证了任务期间的情感分化。我们还在“静止面孔”实验中检查了训练后的模型。这项研究声称，对于在心理学研究中进行的同一实验中，将提议的模型与真实人类受试者进行比较，对于建设性方法而言，这是非常重要的一步。

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