  Deep Neural Networks (DNNs) have been successfully used in classifying
digital images but have been less successful in classifying images with
meanings that are not linear combinations of their visualized features, like
images of artwork. Moreover, it is unknown what additional features must be
included into DNNs, so that they can possibly classify using features beyond
visually displayed features, like color, size, and form. Non-displayed features
are important in abstract representations, reasoning, and understanding
ambiguous expressions, which are arguably topics less studied by current AI
methods. This paper attempts to identify capabilities that are related to
semantic processing, a current limitation of DNNs. The proposed methodology
identifies the missing capabilities by comparing the process of understanding
Mondrian's paintings with the process of understanding electronic circuit
designs, another creative problem solving instance. The compared entities are
cognitive architectures that attempt to loosely mimic cognitive activities. The
paper offers a detailed presentation of the characteristics of the
architectural components, like goals, concepts, ideas, rules, procedures,
beliefs, expectations, and outcomes. To explain the usefulness of the
methodology, the paper discusses a new, three-step computational method to
distinguish Mondrian's paintings from other artwork. The method includes in a
backward order the cognitive architecture's components that operate only with
the characteristics of the available data.
