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.

翻译：我们建议为经常性神经网络建立一个新型的增强记忆机制,在连续评估任务中利用人类认知评估的影响。我们把增强记忆机制概念化为强化记忆单位,它包含一个评估状态,同时包含两个正负强化记忆。两个强化记忆被更强大的刺激破坏或加强。此后,通过对正负强化记忆的竞争更新评估状态。因此,强化神经网络可以学习在暴力改变人类感知体验的刺激下评估的差异。正如视频质量评估实验和经验任务视频质量实验所显示的那样,拟议的强化记忆单位在经常性神经网络中取得优异的性能,这显示了RMU在模拟人类认知评估方面的有效性。