In this paper, we review the evidence that learning is driven by signaling of Prediction Error [PE] by some neurons. We model associative learning in artificial neural networks using Hebbian (non-PE) learning algorithms to investigate whether the data used to implicate PE in learning can arise without actual PE computation. We conclude that the metabolic demands of synaptic change during Hebbian learning would produce a PE-correlated component in functional magnetic resonance imaging (fMRI), which suggests that the research used to imply PE in learning is currently inconclusive.