When the user expresses a piece of knowledge in one
modality—graphics or text—the complementary one is
automatically updated so the two remain
coherent at all times.

Humans assimilate data and information, converting it simultaneously into meaningful knowledge and understanding of systems through words and pictures. During eons of human evolution, the human brain has been trained to capture and analyze images, enabling us to escape predators and capture food. In contrast, processing spoken words, let alone text, is a product of a relatively recent stage in that evolution. As our brains are hardwired to process imagery, graphics naturally appeal to the brain more immediately than words. However, words can express ideas and assertions that are way too complex or even impossible to express graphically; as an example, just try graphically representing this sentence to sense the validity of this claim. While a picture may be worth a thousand words, a word or sentence is indeed sometimes worth a thousand pictures. A problem with the richness of natural language is the potential ambiguity that arises from its use. This does not imply that pictures cannot be ambiguous as well, but graphic ambiguity is greatly reduced, even eliminated, by assigning formal semantics to pictorial symbols of things and to the relationships among them.

Diagrams aid cognitive processing due to their specificity [ 11], a theory proposing that graphical representations limit abstraction and thereby aid “ proces-sibility.” That is, diagrams, because they usually involve fewer interpretations than free text, are more tractable than unconstrained textual notation. When corresponding words and pictures are presented near each other, learners are better able to hold corresponding words and pictures in working memory at the same time, enabling the integration of visual and verbal models [ 8]. A contribution of diagrams may be that they reduce the cognitive load of assigning abstract data to appropriate spatial and temporal dimensions; for example, whereas information about temporal ordering is only implicit in text, a flow diagram reduces errors in answering questions about that ordering [ 6].

A theory called “multimedia learning” proposed in [ 8, 9] is based on three main research-supported cognitive assumptions:

Dual channel. Humans have separate systems for processing visual and verbal representations [ 1, 3];

Limited capacity. The amount of processing that can take place within each information-process-ing channel is limited [ 1, 2, 10]; and

Active processing. Meaningful learning occurs during active cognitive processing, paying attention to words and pictures and mentally integrating them into coherent representations. The active-processing assumption is a manifestation of the constructivist theory in education, which focuses the construction of knowledge by one’s mind as the centerpiece of the educational effort [ 12]. That is, in order for learning to be meaningful, learners must engage physically, intellectually, and emotionally in constructing their own knowledge.

 

As the literature suggests, there is great value in designing a modeling approach and supporting tool to meet the challenges posed by these assumptions. While [ 9] used them to suggest ways to reduce cognitive overload while designing multimedia instruction, they can also be a basis for designing an effective conceptual-modeling framework. Indeed, conceptual modeling is the active cognitive effort of concurrent diagramming and verbalization of one’s thoughts. The resulting diagrams and text together constitute the system’s conceptual model. A model based on a set of the most primitive and generic elements is general enough to be applicable to a host of domains yet simple enough to express the most complex systems. A sufficiently expressive model can help detect design-level errors, be reasoned about, make predictions, be communicated to other stakeholders, and evolve throughout a system’s life cycle.

Such an environment would help us take advantage of the verbal and visual channels and relieve cognitive loads while designing, modeling, and communicating complex systems to stakeholders. These were key motivations some 15 years ago in my design of OPM [ 4]. The OPM modeling environ-