# What Makes a UI Simple? Difficulty and Complexity in Tasks Engaging Visual-Spatial Working Memory
## 2 Methods and Related Work
### Gestalt Principles of Perception and Algorithmic Complexity-Based Measures  
Gestalt is a psychology school that emerged in Germany in the early 20 th century from research in perception (primarily a visual one) and the ability of the human mind to organize individual elements into a large whole, \"other than the sum of its parts\". Detailed review of the history, main points and the \"decline\" of the school in the 1960s can be found in [42], where the authors also note that, during the last 20 years, there has been a revival of interest towards Gestalt approaches. We believe it may be due to the increasing complexity of HCI that accompanies the development of the ubiquitous online environment, to the rapid development of computer vision technologies, and so on. Figure 1: Outline of the Human Processor model. The central position in the Gestalt approach belonged to the law of [PERSON], stating that human organizes the perceived information in a regular, ordered, symmetrical and simple way. Thus, the common essence of the Gestalt principles is decreasing the number of perceived information chunks (units) for more efficient information processing--the human ability obtained from both evolution of the species and from individual experience. The list of Gestalt principles of perception is somehow poorly organized, but they can be briefly summarized as the following [42]:  
* \"proximity\": objects that are placed nearby or in the same area (e.g., inside a frame) are perceived as a group;
* \"similarity\": objects that are matching in color, size, behavior (e.g., movement direction or speed), etc. are perceived as a group;
* \"good form\" and \"closure\": groups are perceived in such a way that the objects form more simple and familiar figures (e.g., cross as two intersecting lines, not as two adjoined angles; or separate dots and strokes as discontinuous drawing of a letter);
* the principles that relate to interaction between foreground and background. It is known that the Gestalt principles are not equivalent in their effect on perception, but until now, no convincing structure describing the effects of individual principles and their combinations was proposed [43; 44; 45]. For example, objects allocated on a two-dimensional field may be groped either based on proximity or on good form, depending on the circumstances. Indeed, the Gestalt adherents proposed no unified formal theory, the fact that allowed critics to declare Gestalt to be merely descriptive and poorly suited for a deeper understanding of psychological and cognitive processes. However, AIT allowed directly linking the Gestalt's concept of \"simplicity\" and the probability--the foundation of the Information Theory--thus uniting the two approaches. AIT implies coded representations of visual forms and states that the complexity of a percept is the length of the shortest string that generates the percept, thus basing itself on Kolmogorov's algorithmic complexity [41]. Since the latter is incomputable, the lengths of the strings produced with compression algorithms often act as its practical approximation, and indeed it has been shown that the coded string lengths, the complexity measures, and performance measures in experiments are highly correlated [43]. In one of our previous works [12], we applied two classical lossless image compression algorithms, RLE [44] and Deflate [45], to describe visual complexity perception. The former algorithm replaces \"runs\" of data with a single data value and the count of how many times if's repeated (see programming code of the implementation in Appendix A). The algorithm doesn't deal with 2D images but can work with the linear string of bytes containing serialized rows of the image. Deflate is based on combination of LZ77 algorithm and Huffman coding: frequent symbols in the string are replaced with shorted representations, while rare symbols are coded with longer ones.