A Neural-Logical Framework for Working Memory Dynamics via MPD and Mental Logic Circuits

We propose a unified framework to model human working memory (WM) dynamics using **Markovian Parallax Denigrate (MPD)** writing tasks, convexity of WM trajectories, and the formation of **mental logic circuits** (flip-flops, MUX, classical and quantum gates). This framework captures three phases: 1. **MPD-induced convex WM growth** 2. **Transition to logic-based WM under cognitive fatigue** 3. **Dynamic feedback loops enabling online object manipulation** We show both **neuroscientific and mathematical evidence** for convexity, Markovian O(1) computations, and the generalization to complex classical and quantum mental circuits.