This thesis describes an analysis of contextual coherence in the visual imagination using three disciplines: cognition, computation, and neuroscience. I examine the topic by augmenting a model of the visual imagination, SOILIE, with an improved version of their top-n model of coherence. I show that the augmented, serial local hill search model, Coherencer, is an improvement over the original model using a new, quantitative evaluation. I then demonstrate that Coherencer is better than a competitive model from the literature on general coherence; it is better than the original top-n model across different compression representations, mainly co-occurrence probabilities and holographic vectors; and it is consistent with contemporary, neuroscientific research on the hippocampus, specifically Scene Construction Theory.