Current computational methods for depression detection via social media primarily rely on static textual analysis, overlooking two key aspects: the temporal evolution of depressive symptoms and the psychological mechanisms behind emotional memory persistence and decay. Existing approaches often treat posts as isolated instances, and simply combining temporal and semantic embeddings can lead to entangled, hard-to-interpret representations. To address these limitations, we propose a novel framework that integrates psychological theories of memory with deep temporal modeling. Our approach introduces a dynamic memory system that simulates natural emotional fading while preserving impactful experiences, alongside a neural architecture that captures symptom progression by jointly modeling content and temporal dynamics. It incorporates an exponential forgetting mechanism, adaptive thresholding for emotional persistence, and a temporal-aware transformer to identify long-term depressive patterns. By aligning computational modeling with psychological insights, our method not only detects expressed symptoms but also traces their development over time. Experiments on eRisk2017 and eRisk2018 show state-of-the-art results, confirming that modeling temporal-psychological dynamics enhances both detection accuracy and clinical interpretability, offering a promising tool for early mental health intervention.