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We propose a novel memory-enhancing mechanism for recurrent neural networksthat exploits the effect of human cognitive appraisal in sequential assessmenttasks. We conceptualize the memory-enhancing mechanism as Reinforcement MemoryUnit (RMU) that contains an appraisal state together with two positive andnegative reinforcement memories. The two reinforcement memories are decayed orstrengthened by stronger stimulus. Thereafter the appraisal state is updatedthrough the competition of positive and negative reinforcement memories.Therefore, RMU can learn the appraisal variation under violent changing of thestimuli for estimating human affective experience. As shown in the experimentsof video quality assessment and video quality of experience tasks, the proposedreinforcement memory unit achieves superior performance among recurrent neuralnetworks, that demonstrates the effectiveness of RMU for modeling humancognitive appraisal.

Learning Human Cognitive Appraisal Through Reinforcement Memory Unit