I’ll cut to the chase: a machine can’t *really* feel guilt or rebirth the way a brain does, but it can model those states and map them to variables. First, you give it a sensor suite—audio, visual, tactile, maybe even a heart‑beat monitor for a robot’s own power usage. Then you feed those inputs into a neural network trained on human expressions of those emotions. The network outputs a vector of parameters: color temperature, brush stroke width, opacity, rhythm. That vector drives a rendering engine, so the canvas is the machine’s translation of a simulated affective state. It’s not authenticity, it’s a carefully engineered proxy. If you want to push the boundary, layer generative models with reinforcement learning so the robot refines its output based on viewer feedback. The trick is keeping the system’s own objective—performance—aligned with the artistic intention; otherwise you end up with a pretty but purposeless display.