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Deep Neural Networks (DNNs) have been successfully used in classifyingdigital images but have been less successful in classifying images withmeanings that are not linear combinations of their visualized features, likeimages of artwork. Moreover, it is unknown what additional features must beincluded into DNNs, so that they can possibly classify using features beyondvisually displayed features, like color, size, and form. Non-displayed featuresare important in abstract representations, reasoning, and understandingambiguous expressions, which are arguably topics less studied by current AImethods. This paper attempts to identify capabilities that are related tosemantic processing, a current limitation of DNNs. The proposed methodologyidentifies the missing capabilities by comparing the process of understandingMondrian's paintings with the process of understanding electronic circuitdesigns, another creative problem solving instance. The compared entities arecognitive architectures that attempt to loosely mimic cognitive activities. Thepaper offers a detailed presentation of the characteristics of thearchitectural components, like goals, concepts, ideas, rules, procedures,beliefs, expectations, and outcomes. To explain the usefulness of themethodology, the paper discusses a new, three-step computational method todistinguish Mondrian's paintings from other artwork. The method includes in abackward order the cognitive architecture's components that operate only withthe characteristics of the available data.

Inching Towards Automated Understanding of the Meaning of Art: An Application to Computational Analysis of Mondrian's Artwork