We propose a novel framework for training hu-manoid robots to exhibit human-like behavior by leveraging musical consonance as a guiding principle. After indexing the neurons in a spiking neural network with the names of keys in a musical keyboard it is trained to produce consonant activations in response to human-generated data, while simultaneously learning to distinguish between human-like and robot-like behavior by producing dissonant activations in response to robot generated data.Then, through reinforcement learning, a humanoid robot is trained to mimic human behavior by using consonance in the network's activations as a reward while the network is shown the robots generated data. Our approach enables the development of modular, task-specific skills ,one per spiking network, and demonstrates the potential for scalable and flexible behavioral learning in humanoid robots.