# Evaluation of Effective Cognition for the QGIS Processing Modeler
## 3 Theory and Measurement Methods  
### Theory Physics of Notations  
Physics of Notations is an objective theory for evaluating visual notation [3,16]. This theory is widely used in all areas of software engineering, not only GIS software because creating diagrams is frequently required in information technology (IT). The first work was an assessment of the Esri ModelBuilder in the area of VPL in GIS applications [17]. The Physics of Notations theory can be used not only for evaluating existing notation but also improving graphical notation or designing new ones. It means that the visual notation in QGIS can be assessed and improved if any drawbacks are identified. Exploring this theory for the new design is very beneficial in design new graphical vocabulary for any purpose. This paper presents the opportunity to make new suggestions according to the theory for QGIS Processing Modeler. The Physics of Notations theory states nine principles that recommend fulfilling cognitively effective notation. _Cognitive effectiveness_ is defined as the speed, ease and accuracy with which a representation can be processed by the human mind [18]. The aim is to read the diagram quickly, without mistakes, and comprehend it accurately. The nine principles are organised as connected ideas where the first central principle is the Principle of Semiotic Clarity. The modular structure of Physics of Notations is designed to make it easy to add or remove principles, emphasising that they are not fixed or immutable but can be modified or extended by future research [19]. The principles are [3]:  
* **Principle of Semiotic Clarity**  
The principle of Semiotic Clarity expresses a one to one correspondence between the syntactic model and semantic features. According to this principle, symbol redundancy, symbol overload, symbol deficit or symbol excess is not permissible. The principle reflects the ontological analysis. * **Principle of Perceptual Discriminability**  
The second principle of Perceptual Discriminability states that different symbols should be clearly distinguishable from each other by visual variables. * **Principle of Visual Expressiveness**  
The principle of Visual Expressiveness states that the full range of visual variables and their full capacity should be used to represent the notational elements. Colour is one of the most effective visual variables. The human visual system is very sensitive to differences in colour and can quickly and accurately distinguish them. Differences in colour are found three times faster than shape and are also easy to remember [20]. The level of expressiveness is measured from level 1 (lowest) to 8 (highest). * **Principle of Graphic Economy**  
The principle states that the number of symbols in a graphical vocabulary must be manageable by human working memory. The choice of symbol affects the ease of memorizing and recalling visual diagrams. The magic number seven express a suitable number of symbols. The range for an of \(7\pm 2\) symbols is suitable. More different symbols in basic graphical vocabulary than nine are demanding for comprehension. * **Principle of Dual Coding** The principle suggests using the text to support the meanings of symbols and clarity. Two methods (graphics and text) provide the user with information and improve comprehensibility. The base is on the duality of mental representation [21]. * **Principle of Semantic Transparency** This principle evaluates how symbols associate the real meaning of an element. Here, associations are sought between the shape or other visual symbol variables and their real properties, and the form implies content. * **Principle of Complexity Management** This principle recommends producing hierarchical levels of the diagram and dividing it into separate modules and create hierarchical structures. It is suitable for large models when comprehension exceeds human working memory capacity. Modularity means scaling information into separate chunks. Modularisation is the division of large systems into smaller parts or separate subsystems.