Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify speakers' emotions and the corresponding causes within conversation contexts. This task has gained increasing attention in recent years. Existing methods typically operate from the perspective of text object, inputting the entire conversation text and relying on neural networks to highlight utterance features as a means of emotion perception, while employing pair concatenation as a single path cause reasoning strategy. However, in conversation scenarios, humans are the true origin of emotions, while language text merely serves as the medium of expression. Consequently, these methods lack the modeling of the cognition and reasoning processes behind the human's behavior from the perspective of the speaker subject. To address this issue, we propose a novel Hierarchical Cognition–Perception–Reasoning Agent (HCPRA) framework. The framework is oriented around the speaker, simulating their personality cognition and conversation scenario cognition. Moreover, it utilizes hybrid memory and implicit emotion enhancement to simulate the human emotion perception. Additionally, it employs a hierarchical emotion cause reasoning mechanism to extract interpretable relationships between the speaker's emotions and their causes through emotion awareness and multi-path cause reasoning. Experimental results demonstrate that our approach achieves state-of-the-art performance on three datasets, with F1 improvement up to 15.61%.