Kategorie: Communication Design

I decided to focus on the topic „Learning through static images vs animated graphics“. In this blog entry I would like to focus on Cognitive Load Theory. Understanding CLT is like having a map for learning. As we delve into the world of static and animated images, CLT gives us a solid foundation. It tells us how our minds handle information, pointing out where things might get tricky and where we can make learning smoother. By keeping CLT in mind, we can design learning experiences that are just right—neither too overwhelming nor too bland.

Developed by Sweller, CLT sees our minds as natural information processors. Helps us unterstand how we absorb, process, and remember information, making it a good starting point for exploring the differences between learning from static and animated images.

There are 5 basic principles of CLT:

1. Long-term Memory and the Information Store Principle:

Most of what we do relies on the vast library in our brains called Long-Term Memory (LTM). It’s like our personal information storehouse, driving actions like problem-solving based on what we’ve learned before.

2. Schema Theory and the Borrowing and Reorganizing Principle:

How do we fill up this memory bank? CLT suggests we borrow from others by imitating, listening, or reading. Schemas, mental structures that help us organize information, play a big role in this process.

3. Problem Solving and the Randomness as Genesis Principle:

When we can’t borrow knowledge, we turn to problem-solving. Trying out new things and seeing what works becomes a source of fresh understanding.

4. Working Memory and the Narrow Limits of Change Principle:

Our working memory handles new information, but there’s a catch—it can only juggle a few new things at once. Trying to process too much at once leads to a mental traffic jam.

5. Relations Between Long-term and Working Memory and the Environmental Organizing and Linking Principle:

This principle helps transfer organized information from our long-term memory to our working memory, making it easier for us to function in our surroundings. It’s like upgrading information from storage to the front of our minds.

Cognitive Load Theory provides valuable insights into the workings of the human mind during learning. It’s a tool that helps us make sense of the learning journey. Whether we’re dealing with static images or animated ones, understanding how our brains process information is key. 

Sources:

Designers incorporate handmade, imperfect elements such as texture, folds, tears, grains and handmade typography into graphic design for a number of reasons:

Handmade elements build a relationship with the viewer based on trust and shared experience, as imperfections are natural, human and signifiers of a narrative that reveals how an object was made.

The recent shift in design trends towards a more raw and natural aesthetic is accelerating as people increasingly embrace the imperfections in everyday things, recognising that the roughness and uniqueness of these elements is what makes them special.

Sometimes it’s the imperfections that make a design truly memorable, as they can evoke surprise, delight or empathy in users, adding a human touch and authenticity to designs that resonate emotionally with users.

Precise geometric lines illustrate objects that are artificial and technological, whereas curvy, imperfect lines can represent more natural and organic forms, creating a more authentic and relatable aesthetic that resonates with people.

Integrating analogue elements into digital platforms can enhance the user experience and foster a sense of nostalgia as it helps us appreciate imperfection and process, celebrating the beauty of imperfection in a digital age and rediscovering the timeless appeal of authenticity.

In summary, designers are integrating handmade, imperfect elements into graphic design to create designs that are more meaningful, relatable and impactful, ultimately fostering empathy and trust with their audiences.

Sources:

https://www.creativebloq.com/future-handmade-design-5132895

https://bootcamp.uxdesign.cc/embracing-imperfection-64a57d3f6372?source=rss——-1

https://elements.envato.com/learn/back-to-basics-organic-graphic-design-trends

https://utilitiesone.com/analog-communication-embracing-imperfections-in-a-digital-world

In my research, I have tended to ask the questions „What makes randomness feel and look random?“ and „Can randomness be seen as not random?“ In this blog entry, I will explore the clustering illusion and the illusion of randomness.

The Clustering Illusion

When looking at clouds, it becomes clear how easily recognizable shapes such as people, animals, or objects appear. Called the „clustering illusion,“ this is the human tendency to perceive patterns in random data. Our evolutionary development has honed our ability to identify specific objects, including faces, potential threats in shadows, and familiar objects. Once the brain becomes adept at recognizing certain patterns, it tends to see them everywhere.¹

The clustering illusion extends beyond earthly observations to illustrate the human tendency to see random events as more orderly or uniform than they really are. A pertinent example is the perception of stars in the night sky, where certain areas appear densely populated while others appear empty. This illusion arises from the tendency to assign a physical explanation to this non-random distribution, despite the fact that the positions of the stars are inherently random.

The left image below shows an example of a truly random star field, while the right image shows a manually generated star field that appears too uniform to be realistic, but is more in line with human expectations of randomness. In the example on the right, each star is randomly positioned within its own 20 × 20 pixel square, resulting in a more uniform but unrealistic result.²

Image source: https://tomroelandts.com/articles/the-clustering-illusion

The Illusion of Randomness

Richard A. Muller illustrates the illusion of randomness by generating two sets of star patterns. Using a computer, he first creates a plot with completely random locations, resulting in a seemingly uneven distribution of stars. To counteract this perception, Muller then divides the space into 100 smaller boxes and randomly places a star in each box, creating a more uniform appearance. Surprisingly, when asked which pattern looks more random, most people choose the second plot, which is actually the non-random one. The uniform distribution makes it look more random, challenging our intuitive understanding of randomness in seemingly clustered patterns. Muller emphasizes that truly random patterns can appear clustered, leading to the need to make them more uniform to convey a sense of randomness, as in many depictions of stars in artwork.³

Image source: https://muller.lbl.gov/teaching/Physics10/old physics 10/chapters (old)/4-Randomness.htm

What makes randomness feel and look random?

Understanding randomness can be challenging because of the limitations of our intuition. Asan example, consider the image below, with three sets of 132 points representing the nests of Patagonian seabirds, the nest sites of ant colony, and randomly generated coordinates.

Image source: https://behavioralscientist.org/yates-expect-unexpected-why-randomness-doesnt-feel-random-sense-patterns/

Identifying the truly uniform random distribution can be difficult, as our perception of randomness tends to lean toward well-spaced arrangements. Surprisingly, the leftmost image with uniformly distributed points is often perceived as less random than the other two images, which represent the locations of ant and seabird nests. This cognitive bias led to research into metrics that objectively determine spatial randomness, removing human perception from the equation. While our brains may struggle with randomness, recognizing it is crucial in various situations. This finding not only challenges our preconceived notions, but also underscores the importance of using objective metrics to reveal the true nature of randomness in spatial patterns.⁴

Sources

1. https://medium.com/@nexy.io/clustering-illusion-1055023fec59 ↩︎
2. https://tomroelandts.com/articles/the-clustering-illusion ↩︎
3. https://muller.lbl.gov/teaching/Physics10/old physics 10/chapters (old)/4-Randomness.htm ↩︎
4. https://behavioralscientist.org/yates-expect-unexpected-why-randomness-doesnt-feel-random-sense-patterns/ ↩︎

NCS-Farbmodell (Natural Color System, 1979)

The Natural Color System (NCS) is a color system that originated from the Hering-Johansson theory and was realized in the form of a color atlas by Hesselgren in 1953. Subsequently, in 1972, Hård, Sivik, and Tonnquist further developed the NCS, which became a Swedish standard in 1979.

The foundational concept of the NCS revolves around Hering’s idea of six elementary color perceptions: white (W), black (S), yellow (Y), red (R), blue (B), and green (G), with all other color perceptions having varying degrees of relation to these six. In the NCS, each color is defined by its similarity to these six elementary colors [4].

Notably, a color cannot be similar to more than two hues simultaneously. For instance, yellow and blue, as well as red and green, are mutually exclusive. The NCS chromaticness (c) is determined by the sum of color variables, while its hue (F) is determined by their ratio.

Colors in the NCS are denoted as sc-F, where s represents blackness, c represents chromaticness (linked to saturation), and F represents the hue of the color. The s and c values, both comprising two digits, are written without space and are separated from F by a hyphen. For example, in the notation 2040-G40Y, the hue is intermediary between green (G) and yellow (Y) in a ratio of 40 to 60.

The geometric representation of the NCS is a symmetric double cone with white and black at the vertices. The other four primary hues are positioned on the circle of full colors, forming corners of a square that touches this circle (refer to Figs. 4, 5, and 6). This geometric configuration mirrors that of the Ostwald system.

Coloroid System

The Coloroid System, developed at Budapest Polytechnical University by Nemcsics and first published in 1975, is a color system that focuses on surface colors illuminated by daylight and perceived by individuals with normal color vision. It is built upon harmonic color differences that aim to approximate aesthetic uniformity. Recognized as a Hungarian standard since 2002, Coloroid has semipolar coordinates representing color points in a linear circular cylinder. These coordinates include the angular coordinate denoting hue (A), radial coordinate representing saturation (T), and vertical axial coordinate indicating luminosity (V).

In the Coloroid system, absolute white (W) and absolute black (S) are positioned at the upper and lower limits of the color space, respectively. White corresponds to the color of a surface illuminated by CIE D65 beam distribution, while black represents a surface perfectly absorbing light. Coloroid limit colors, forming a closed curve within the color space, are the most saturated colors on the Coloroid cylinder. The system also includes 48 Coloroid basic colors, characterized by integer numbers and evenly distributed on the CIE 1931 diagram.

Coloroid color planes are defined by the achromatic axis, with each plane having the same hue and dominant wavelength. Delimited by the neutral axis and Coloroid delimiting curves, these planes determine the shape of surfaces for each hue. Coloroid basic hues, corresponding to basic colors, are organized into 48 sections. Saturation (T), lightness (V), and hue (A) values define the color in the Coloroid system, with notation expressed as hue–saturation–lightness (A–T–V), such as 13-22-56.

Notably, Coloroid saturation quantifies a surface color’s saturation, measured on a scale close to aesthetic uniformity. Lightness denotes the distance from absolute black on a graduated scale, while hue indicates the color’s position within 48 sections based on dominant wavelength. The system’s geometric representation is a regular symmetric double cone, providing a comprehensive framework for defining and categorizing surface colors.

Küppers’ Atlas and Rhombohedric Color System

The German engineer Harald Küppers has introduced an atlas designed specifically for the graphic arts and printing industry, encompassing more than 5,500 nuances. The color samples in this atlas are generated through the four-color printing technique, blending transparent dyes—yellow, magenta, cyan, and black—alongside the white background of the paper.

The gradations of these mixtures are quantified in percentages, directly corresponding to the proportion of surface covered by each dye. This notation serves not only to distinguish different nuances but also functions as a formula for color reproduction. The variations are denoted by 10% differences between individual samples and between consecutive color charts.

The published color charts are categorized into five series, with three being characterized as achromatic mixtures due to the inclusion of black, and the remaining two designated as chromatic mixtures with exclusive interventions of yellow, magenta, and cyan. In the initial three series, black is incrementally added to a mixture of two chromatic dyes, maintained at a consistent 10% variation throughout the entire series and from one chart to another. In the fourth series, yellow is successively introduced to a fixed mixture of two chromatic dyes (magenta and cyan). Series 1–4 consist of 11 charts, representing 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 99% of the dye added to the fixed binary mixture. The fifth series includes two additional charts: one featuring yellow-cyan mixtures and 99% magenta, and the other with yellow-magenta mixtures and 99% cyan.

Küppers also represents his system in a three-dimensional space, visualized either as a cube or with a rhombohedric shape, comprising an upper tetrahedron, a central octahedron, and a lower tetrahedron. In the rhombohedric model, white is positioned at the upper vertex of the upper tetrahedron, housing the three subtractive primaries—yellow, magenta, and cyan—in its base. This base also serves as one of the faces of the central octahedron. The lower triangular face of the octahedron features the three colors resulting from the mixture of subtractive primaries in pairs: green (yellow with cyan), red (yellow with magenta), and blue (cyan with magenta). This face is, in turn, shared with the lower tetrahedron, where black is situated at the lower vertex.

Sources: Nemcsics, A. & Caivano, J. L. (2015). Color order systems. In Springer eBooks (S. 1–16). https://doi.org/10.1007/978-3-642-27851-8_232-7

In this blog entry, I would like to explore how computer-generated randomness actually works and whether true randomness exists.

Randomness has a long history, as evidenced by practices such as the drawing of lots from a helmet described already in the ancient Greek epic poem Iliad. In 17th-century discussions that predate Newton, the distinction between events perceived as unpredictable and those that are truly random was recognized in the exploration of the relationship between chance, cause, and necessity. This philosophical inquiry led to metaphysical questions about human agency. Although contemporary scientific consensus defines randomness as a sequence without repetition, bias, or pattern, our beliefs about chance, cause, and effect often clash with empirical realities (Lostritto, 2015).

Randomness, a concept deeply embedded in both the natural and digital worlds, takes on unique dimensions when encountered in the controlled environment of a computer. In this exploration, we delve into the intricacies of true randomness and pseudorandomness, examining their implications, applications, and the underlying mechanisms that drive their generation.

True random numbers

In the field of computing, achieving true randomness is an elusive goal. True random numbers, unlike their deterministic counterparts, result from unpredictable physical processes such as radioactive decay, atmospheric fluctuations, or electrical noise. These numbers, which have no discernible pattern, provide a level of unpredictability that is critical in fields such as cryptography and simulation. Unlike pseudorandom numbers, which follow a repeatable sequence based on a seed value, true random numbers offer an inherent unpredictability that adds a layer of security and authenticity to various computational processes (Owais, 2023).

Pseudo-random numbers

While the deterministic nature of computers creates a challenge in generating true randomness, pseudorandomness steps in to fill the need for unpredictability. Pseudorandom numbers are generated by a series of mathematical operations initiated by a seed value. Despite being called „random“, these numbers are actually predictable – a property rooted in the repeatability of the mathematical operations (Lostritto, 2015). Commonly used in everyday computing, pseudorandom numbers play a role in simulations, randomized algorithms, and various applications where a semblance of randomness is required (Owais, 2023). Understanding the balance between predictability and randomness in the pseudorandom domain is critical to optimizing algorithms and ensuring the reliability of computational results.

In 1946, John von Neumann and Stanislaw Ulam used ENIAC to create the first pseudorandom number using an algorithm called the middle square method. This method involves selecting a „seed“, a truly random number, and performing calculations by multiplying it by itself, taking the middle of the result, and repeating the process. However, the randomness of these pseudo-random numbers is tied to the initial seed, resulting in a predictable sequence if the same seed is chosen. The length of the repeating pattern, called the period, depends on the length of the seed. Despite their predictability, pseudorandom numbers serve the practical purpose of providing the randomness necessary to generate unpredictable sequences. The complexity of the seed affects the unpredictability of the sequence, with a more complex seed leading to a more unpredictable result, though still within practical limits (Owais, 2023).

Generative art and randomness

Generative art is a creative approach that uses randomness in code to varying degrees. Whether prominently featured or subtly integrated, randomness plays a crucial role in shaping these works of art. In essence, generative art is created through programming, incorporating random elements that result in unique visual outputs each time the program is runned. By giving up control and allowing random forces to influence the creative process, generative art brings a dynamic and ever-changing quality to visual expression (Ferraro, 2021).

Generative artist Jared Tarbell highlights the importance of randomness in programming, emphasizing that the execution of a program is typically predictable. By introducing randomness into the creative process, artists can experience unexpected results, fostering a sense of surprise even for the creator. Tarbell suggests that embracing randomness allows for unpredictable outcomes that may not be achievable through controlled efforts, similar to the spontaneity found in contact improvisation, a dance form in which performers respond to the environment presented to them, resulting in sequences unattainable through deliberate planning (Ferraro, 2021).

Examples for artworks using computer generated randomness

Vera Molnár: Interruptions

In this collection, the artist, Vera Molnár, begins with a grid filled with straight lines of equal length. The lines are randomly rotated, creating an intricate pattern that suggests various forces disrupting a regular structure. In addition, the artist introduces „interruptions“ by randomly erasing sections of certain lines, creating voids that are shaped by both the missing parts and the surrounding elements. (src: https://dam.org/museum/artists_ui/artists/molnar-vera/interruptions/ )

Jared S Tarbell: Truchet Tile Multiscale

In this series, the artist begins with Truchet tiles, square design elements arranged in a grid with random rotations. The sketch adds a layer of complexity by incorporating tiles at different scales. The entire composition is made up of black and white ellipses, with arcs formed by multiple ellipses rotated around a radius. Notably, this tile set scales seamlessly with no visible seams, creating a unique and visually appealing pattern. (src: https://www.infinite.center/2021/01/25/truchet-tile-multiscale/ )

On this website you can see more generative artworks where randomness plays a key role: https://www.lerandom.art/

Sources:

Lostritto, Carl (2015): The Value of Randomness in Art and Design. In: https://www.fastcompany.com/3052333/the-value-of-randomness-in-art-and-design

Owais, Mohd (2023): Random Numbers Generators: How computers think randomly? In: https://medium.com/@mohammdowais/how-computers-think-randomly-80fa37183949

Ferraro, Marc (2021): Random Rules — Why you should check out generative art. In: https://marc-ferraro.medium.com/random-rules-why-you-should-check-out-generative-art-61948b54da87

DAM: Interruptions (1968-1969) In: https://dam.org/museum/artists_ui/artists/molnar-vera/interruptions/

Tarbell, Jared S. (2021): Truchet Tile Multiscale. In: https://www.infinite.center/2021/01/25/truchet-tile-multiscale/