论文标题
通过增强记忆单元学习人类认知评估
Learning Human Cognitive Appraisal Through Reinforcement Memory Unit
论文作者
论文摘要
我们为复发性神经网络提出了一种新型的记忆增强机制，该机制在顺序评估任务中利用了人类认知评估的影响。我们将增强记忆的机制概念化为增强记忆单元（RMU），其中包含一个评估状态以及两个正面和负面的增强记忆。通过更强的刺激，这两个增强记忆是腐烂或增强的。此后，评估国家通过积极和负面的增强记忆的竞争进行了更新。因此，RMU可以在刺激暴力变化下学习评估差异，以估计人类情感体验。如视频质量评估和经验任务的视频质量的实验所示，拟议的增强记忆单元在复发性神经网络之间取得了卓越的性能，这证明了RMU在建模人类认知评估中的有效性。
We propose a novel memory-enhancing mechanism for recurrent neural networks that exploits the effect of human cognitive appraisal in sequential assessment tasks. We conceptualize the memory-enhancing mechanism as Reinforcement Memory Unit (RMU) that contains an appraisal state together with two positive and negative reinforcement memories. The two reinforcement memories are decayed or strengthened by stronger stimulus. Thereafter the appraisal state is updated through the competition of positive and negative reinforcement memories. Therefore, RMU can learn the appraisal variation under violent changing of the stimuli for estimating human affective experience. As shown in the experiments of video quality assessment and video quality of experience tasks, the proposed reinforcement memory unit achieves superior performance among recurrent neural networks, that demonstrates the effectiveness of RMU for modeling human cognitive appraisal.
