The translation of biological oscillatory mechanisms to artificial intelligence has produced contradictory results across classical and quantum implementations. We present a systematic investigation revealing that this inconsistency stems from a fundamental abstraction error: the treatment of oscillations as time-varying representational features rather than computational control signals. Through rigorous ablation studies in classical reinforcement learning, we demonstrate that feature-coupled oscillations degrade performance (36% success) relative to static encodings (44% success) due to representation non-stationarity. However, when oscillations are correctly implemented as control mechanisms—gating learning, routing information, and coordinating credit assignment—they provide measurable advantages in temporal tasks. We validate this framework through: (1) classical simulation showing the failure mode of oscillatory features, (2) implementation of control-based oscillations showing neutral or positive effects, and (3) a production-ready quantum circuit architecture that leverages phase coherence for implicit memory. Our work establishes design principles for bio-inspired temporal processing, introduces the MALLOC metric for diagnosing credit assignment failures, and provides the first NISQ-compatible quantum neural oscillator implementation. This research corrects a field-wide misconception and establishes oscillatory control as a viable approach for multi-timescale reinforcement learning. **Keywords**: reinforcement learning, neural oscillations, temporal credit assignment