The Chinese room argument holds that a computer executing a program cannot have a mind, understanding, or consciousness, regardless of how intelligently or human-like the program may make the computer behave. The argument was presented in a 1980 paper by the American philosopher John Searle, entitled "Minds, Brains, and Programs" and published in the journal Behavioral and Brain Sciences. Similar arguments had been made previously by others, including Gottfried Wilhelm Leibniz, Peter Winch, and Anatoly Dneprov. Searle's version has been widely discussed in the years since. The centerpiece of Searle's argument is a thought experiment known as the "Chinese room". The argument is directed against the philosophical positions of functionalism and computationalism, which hold that the mind may be viewed as an information-processing system operating on formal symbols, and that simulation of a given mental state is sufficient for its presence. Specifically, the argument is intended to refute a position Searle calls the strong AI hypothesis: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds." Although its proponents originally presented the argument in reaction to statements of artificial intelligence (AI) researchers, it is not an argument against the goals of mainstream AI research because it does not show a limit in the amount of intelligent behavior a machine can display. The argument applies only to digital computers running programs and does not apply to machines in general. While widely discussed, the argument has been subject to significant criticism and remains controversial among philosophers of mind and AI researchers. == Chinese room thought experiment == Suppose that artificial intelligence research has succeeded in programming a computer to behave as if it understands Chinese. The machine accepts Chinese characters as input, carries out each instruction of the program step by step, and then produces Chinese characters as output. The machine does this so perfectly that no one can tell that they are communicating with a machine and not a hidden Chinese speaker. The questions at issue are these: does the machine actually understand the conversation, or is it just simulating the ability to understand the conversation? Does the machine have a mind in exactly the same sense that people do, or is it just acting as if it had a mind? Now suppose that Searle is in a room with an English version of the program, along with sufficient pencils, paper, erasers and filing cabinets. Chinese characters are slipped in under the door, and he follows the program step-by-step, which eventually instructs him to slide other Chinese characters back out under the door. If the computer had passed the Turing test this way, it follows that Searle would do so as well, simply by running the program by hand. Searle can see no essential difference between the roles of the computer and himself in the experiment. Each simply follows a program, step-by-step, producing behavior that makes them appear to understand. However, Searle would not be able to understand the conversation. Therefore, he argues, it follows that the computer would not be able to understand the conversation either. Searle argues that, without "understanding" (or "intentionality"), we cannot describe what the machine is doing as "thinking" and, since it does not think, it does not have a "mind" in the normal sense of the word. Therefore, he concludes that the strong AI hypothesis is false: a computer running a program that simulates a mind would not have a mind in the same sense that human beings have a mind. == History == Gottfried Wilhelm Leibniz made a similar argument in 1713 against mechanism, the idea that everything that makes up a human being could, in principle, be explained in mechanical terms—in other words, that a person, including their mind, is merely a very complex machine. Leibniz used the thought experiment of expanding the brain until it was the size of a mill. He found it difficult to imagine that a "mind" capable of "perception" could be constructed using only mechanical processes. British philosopher Peter Winch made the same point in his 1958 book The Idea of a Social Science and its Relation to Philosophy, in which he argues that "a man who understands Chinese is not a man who has a firm grasp of the statistical probabilities for the occurrence of the various words in the Chinese language" (p. 108). Soviet cyberneticist Anatoly Dneprov made an essentially identical argument in 1961, in the form of his short story "The Game". In it, a stadium of people act as switches and memory cells implementing a program to translate a sentence from Portuguese, a language none of them know. The game was organized by a "Professor Zarubin" to answer the question "Can mathematical machines think?" Speaking through Zarubin, Dneprov writes that "the only way to prove that machines can think is to turn yourself into a machine and examine your thinking process", and he concludes, as Searle does, that "even the most perfect simulation of machine thinking is not the thinking process itself." In 1974, Lawrence H. Davis imagined duplicating the brain using telephone lines and offices staffed by people, and in 1978, Ned Block envisioned the entire population of China involved in such a brain simulation. This is known as the China brain thought experiment. Searle's version appeared in his 1980 article "Minds, Brains, and Programs", published in Behavioral and Brain Sciences. It eventually became the journal's "most influential target article", generating an enormous number of commentaries and responses in the ensuing decades, and Searle had continued to defend and refine the argument in multiple papers, popular articles, and books. David Cole writes that "the Chinese Room argument has probably been the most widely discussed philosophical argument in cognitive science to appear in the past 25 years". Most of the discussion consists of attempts to refute it. "The overwhelming majority", notes Behavioral and Brain Sciences editor Stevan Harnad, "still think that the Chinese Room Argument is dead wrong". The sheer volume of the literature that has grown up around it inspired Pat Hayes to comment that the field of cognitive science ought to be redefined as "the ongoing research program of showing Searle's Chinese Room Argument to be false". Searle's argument has become "something of a classic in cognitive science", according to Harnad. Varol Akman agrees, and has described the original paper as "an exemplar of philosophical clarity and purity". == Philosophy == Although the Chinese Room argument was originally presented in reaction to the statements of artificial intelligence researchers, philosophers have come to consider it as an important part of the philosophy of mind. It is a challenge to functionalism and the computational theory of mind, and is related to such questions as the mind–body problem, the problem of other minds, the symbol grounding problem, and the hard problem of consciousness. === Strong AI === Searle identified a philosophical position he calls "strong AI": The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds. The definition depends on the distinction between simulating a mind and actually having one. Searle writes that "according to Strong AI, the correct simulation really is a mind. According to Weak AI, the correct simulation is a model of the mind." The claim is implicit in some of the statements of early AI researchers and analysts. For example, in 1957, the economist and psychologist Herbert A. Simon declared that "there are now in the world machines that think, that learn and create". Simon, together with Allen Newell and Cliff Shaw, after having completed the first program that could do formal reasoning (the Logic Theorist), claimed that they had "solved the venerable mind–body problem, explaining how a system composed of matter can have the properties of mind." John Haugeland wrote that "AI wants only the genuine article: machines with minds, in the full and literal sense. This is not science fiction, but real science, based on a theoretical conception as deep as it is daring: namely, we are, at root, computers ourselves." Searle also ascribes the following claims to advocates of strong AI: AI systems can be used to explain the mind; The study of the brain is irrelevant to the study of the mind; and The Turing test is adequate for establishing the existence of mental states. === Strong AI as computationalism or functionalism === In more recent presentations of the Chinese room argument, Searle has identified "strong AI" as "computer functionalism" (a term he attributes to Daniel Dennett). Functionalism is a position in modern philosophy of mind that holds that we can define menta
GeneTalk
GeneTalk is a web-based platform, tool, and database for filtering, reduction and prioritization of human sequence variants from next-generation sequencing (NGS) data. GeneTalk allows editing annotation about sequence variants and build up a crowd sourced database with clinically relevant information for diagnostics of genetic disorders. GeneTalk allows searching for information about specific sequence variants and connects to experts on variants that are potentially disease-relevant. == Application to diagnostics == Users can upload NGS data in Variant Call Format (VCF) onto the GeneTalk server into their accounts. All entries of the file are preprocessed and shown in the integrated VCF viewer. Filtering tools are set by the user to reduce the number of clinically non-relevant variants. After filtering and prioritization users can interpret relevant variants by retrieving information (annotations) about variants from the GeneTalk database. The communication platform allow users to contact experts about specific variants, genes, or genetic disorders, to exchange knowledge and expertise. === Analysis procedure === Steps required to analyze VCF files Upload VCF file Edit pedigree and phenotype information for segregation filtering Filter VCF file by editing the filtering options View results and annotations Add annotations === Filtering tools === The following filtering options may be used to reduce the non-relevant sequence variants in VCF files. Functional – filter out variants that have effects on protein level Linkage – filter out variants that are on specified chromosomes Gene panel – filter variants by genes or gene panels, subscribe to publicly available gene panels or create own ones Frequency – show only variants with a genotype frequency lower than specified Inheritance – filter out variants by presumed mode of inheritance Annotation – show only variants with a score for medical relevance and scientific evidence == Communication platform and expert network == Users can share VCF files with colleagues and coworkers. The integrated mailing systems allows users to contact experts easily. Users can create annotations and comments and rate annotations regarding medical relevance and scientific evidence, that is helpful for the community of users for diagnosis of genetic disorders. Registered users provide information about their field of knowledge in their profile and can be contacted by other users. == Potential applications == Developing diagnostics Genetic analysis Capturing data generated by community Communication and exchange of knowledge and expertise
Eimear Kenny
Eimear E. Kenny is a researcher in population genetics and translation genomics, and is the Founding Director of the Institute for Genomic Health, and Endowed Chair and Professor of Genomic Health at the Icahn School of Medicine at Mount Sinai. She is known for novel approaches in computational genomics, advancing the study of human genetic variation and its connection to disease risk and diagnosis. Her research has laid the foundation for integrating artificial intelligence (AI) and genomics into precision medicine and routine clinical care. By combining genomics, computer science, and medicine, her work leverages genomic sequencing technologies and machine learning algorithms to uncover insights that improve patient care, accelerate genomic data analysis, and enable the future of AI-driven healthcare. She has led multiple genomics-based clinical trials, applying computational biology and AI in clinical settings to advance genomic medicine and precision healthcare. == Research == A recipient of the Early-Career Award from the American Society of Human Genetics (USA), Kenny, as of 2024, leads a team in genetics, computer science, and medicine, focusing on genetic ancestry, large-scale genomics, clinical trials, and genomic medicine at the Institute for Genomic Health. The lab works to advance understanding of genetic ancestry and its impact on health in order to inform better clinical medicine models. She is recognized for her work to leverage biobanks for translational genomics and her development of new genetic tests an strategies for health care management. In one study, she and her colleagues investigated genetic disorders that might be under-diagnosed due to insufficient data, and found a variant in a collagen gene associated with Steel syndrome. This syndrome caused short stature and bone and joint issues and was thought to be rare. However, the study revealed it is common in individuals with Puerto Rican ancestry. Three of Kenny's genomic medicine clinical trials assessed how to bring new technology, such as digital apps, or information, such as polygenic risk scores, into routine clinical care. In the 2010s, Kenny was instrumental in several large-scale sequencing studies, including the 1000 Genomes Project, the Exome Sequencing Project, the Genome Sequencing Project, and the Trans-Omics for Precision Medicine. In 2012, she led work that discovered the variant responsible for blond hair in Melanesia, work that was featured in the Smithsonian NHGRI Human Genome Exhibit in Washington, D.C. In 2017, her group was one of the first to demonstrate that polygenic risk scores derived in predominantly European populations have reduced accuracy when applied in populations now widely acknowledged as a major challenge in the field of genomic risk prediction. As of 2024, she is Principal Investigator in many NIH-funded international consortium focused on computational genomics and genomic medicine, including Electronic Medical Records and Genomics, Polygenic Risk Methods in Diverse Populations, and the Human Pangenome Reference Consortium. In 2023, Kenny played a key role in a groundbreaking advancement in genomics research by helping to map a diverse human pangenome—a major shift from reliance on a single reference genome. Unlike the earlier genetic map, based on one man of mixed European and African ancestry in Buffalo, this new pangenome project captures far greater human genetic diversity. As reported by The Washington Post, Kenny's work demonstrates how a more inclusive human genome can drive discoveries in rare genetic diseases, improve genomic medicine, and accelerate the future of precision healthcare. Kenny was co-developer and current license holder for Random Forest adMIXture (RFMix), a patented software for inferring continental and sub-continental ancestry at genomic loci. == Education and career == Kenny graduated from Trinity College Dublin with a BA in Biochemistry in 1999 and did a masters in Bioinformatics at Leeds University. She received her PhD in Computational Genomics at Rockefeller University, and did her post-doctoral work in the lab of Dr. Carlos D. Bustamante at Stanford University. === Academic appointments === As of 2024, at Mount Sinai, she serves as the Endowed Chair and Professor of Genomic Health, Professor at the Department of Medicine and Professor at the Department of Genetics and Genomic Sciences. Since 2018 she has served as the Founding Director of the Institute for Genomic Health, and since 2022, she also serves as the Founding Director of the Center for Translational Genomics. She is also the Director of Translational Research, Division for Genomic Medicine. Former appointments include Assistant Professor at the Department of Genetics and Genomic Sciences and Member at The Charles Bronfman Institute of Personalized Medicine, both at Mount Sinai. She was also Bioinformatics Programmer at the California Institute of Technology, and research assistant at the Massachusetts Institute of Technology. == Publications == As of 2024, Kenny is an advisor to Cell Genomics. Google Scholar reports 50,623 citations, an h-index of 66 and an i10-index of 130. The five most-cited articles she contributed to are: Auton, A; Brooks, LD; Durbin, RM; Garrison, EP; Kang, HM; Korbel, JO; Marchini, JL; McCarthy, S; McVean, GA; Abecasis, GR (2015). "A global reference for human genetic variation". Nature. 526 (7571): 68–74. Bibcode:2015Natur.526...68T. doi:10.1038/nature15393. PMC 4750478. PMID 26432245.. Cited by 14847 Abecasis, GR; Auton, A; Brooks, LD; DePristo, MA; Durbin, RM; Handsaker, RE; Kang, HM; Marth, GT; McVean, GA (2012). "An integrated map of genetic variation from 1,092 human genomes". Nature. 491 (7422): 56–65. Bibcode:2012Natur.491...56T. doi:10.1038/nature11632. PMC 3498066. PMID 23128226.. Cited by 8287 Jacob A. Tennessen et al. Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes.Science337,64–69(2012).DOI:10.1126/science.1219240 Cited by 1886 Taliun, D.; Harris, D.N.; Kessler, M.D.; et al. (2021). "Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program". Nature. 590 (7845): 290–299. Bibcode:2021Natur.590..290T. doi:10.1038/s41586-021-03205-y. PMC 7875770. PMID 33568819.. Cited by 1369 Vilhjálmsson, BJ; et al. (2015). "Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores". Am J Hum Genet. 97 (4): 576–92. doi:10.1016/j.ajhg.2015.09.001. PMC 4596916. PMID 26430803.. Cited by 1327
Layer (deep learning)
A layer in a deep learning model is a structure or network topology in the model's architecture, which takes information from the previous layers and then passes it to the next layer. == Layer types == The first type of layer is the Dense layer, also called the fully-connected layer, and is used for abstract representations of input data. In this layer, neurons connect to every neuron in the preceding layer. In multilayer perceptron networks, these layers are stacked together. The Convolutional layer is typically used for image analysis tasks. In this layer, the network detects edges, textures, and patterns. The outputs from this layer are then fed into a fully-connected layer for further processing. See also: CNN model. The Pooling layer is used to reduce the size of data input. The Recurrent layer is used for text processing with a memory function. Similar to the Convolutional layer, the output of recurrent layers are usually fed into a fully-connected layer for further processing. See also: RNN model. The Normalization layer adjusts the output data from previous layers to achieve a regular distribution. This results in improved scalability and model training. A Hidden layer is any of the layers in a Neural Network that aren't the input or output layers. == Differences with layers of the neocortex == There is an intrinsic difference between deep learning layering and neocortical layering: deep learning layering depends on network topology, while neocortical layering depends on intra-layers homogeneity.
Inbenta
Inbenta is an AI company that originated in Barcelona, Spain, in 2005. Inbenta is currently headquartered in Allen, Texas, with additional offices in Spain, São Paulo, Brazil, Toulouse, France, and Tokyo, Japan. Inbenta provides natural language processing and semantic search through artificial intelligence. == History == Inbenta raised $12 Million in their Series B funding round to extend the reach of their artificial intelligence for business solutions. In 2023 Inbenta's new chief executive officer Melissa Solis moved Inbenta's headquarters to One Bethany West in Allen, Texas from Foster City, California. == Controversy == On 23 June 2018, Ticketmaster UK identified malicious software on a customer support product hosted by Inbenta Technologies, compromising personal data and payment details for thousands of Ticketmaster customers. Three days later, Inbenta's CEO Issued a message about the incident to convey the full scope of the breach. Also on its FAQ section, Inbenta claimed that "After a careful analysis of all clues and snapshots from our systems, the technical team at Inbenta discovered that the script had been implemented on the payment page. We were unaware of this, and would have advised against doing so had we known, as it presents a point of vulnerability". On November 13, 2020, the Information Commissioner's Office fined Ticketmaster UK Limited £1.25 million for failing to protect customers' payment details. According to the ICO, "It was because of Ticketmaster's business decision to include the [Inbenta] chat bot on its payment page that the chat bot was able to unlawfully process the personal data of customers."
Inverse consistency
In image registration, inverse consistency measures the consistency of mappings between images produced by a registration algorithm. The inverse consistency error, introduced by Christiansen and Johnson in 2001, quantifies the distance between the composition of the mappings from each image to the other, produced by the registration procedure, and the identity function, and is used as a regularisation constraint in the loss function of many registration algorithms to enforce consistent mappings. Inverse consistency is necessary for good image registration but it is not sufficient, since a mapping can be perfectly consistent but not register the images at all. == Definition == Image registration is the process of establishing a common coordinate system between two images, and given two images I 1 : Ω 1 → R I 2 : Ω 2 → R {\displaystyle {\begin{aligned}I_{1}:\Omega _{1}\to \mathbb {R} \\I_{2}:\Omega _{2}\to \mathbb {R} \end{aligned}}} registering a source image I 1 {\displaystyle I_{1}} to a target image I 2 {\displaystyle I_{2}} consists of determining a transformation f 1 : Ω 2 → Ω 1 {\displaystyle f_{1}:\Omega _{2}\to \Omega _{1}} that maps points from the target space to the source space. An ideal registration algorithm should not be sensitive to which image in the pair is used as source or target, and the registration operator should be antisymmetric such that the mappings f 1 : Ω 2 → Ω 1 f 2 : Ω 1 → Ω 2 {\displaystyle {\begin{aligned}f_{1}:\Omega _{2}\to \Omega _{1}\\f_{2}:\Omega _{1}\to \Omega _{2}\end{aligned}}} produced when registering I 1 {\displaystyle I_{1}} to I 2 {\displaystyle I_{2}} and I 2 {\displaystyle I_{2}} to I 1 {\displaystyle I_{1}} respectively should be the inverse of each other, i.e. f 2 = f 1 − 1 {\displaystyle f_{2}=f_{1}^{-1}} and f 1 = f 2 − 1 {\displaystyle f_{1}=f_{2}^{-1}} or, equivalently, f 2 ∘ f 1 = id Ω 2 {\displaystyle f_{2}\circ f_{1}=\operatorname {id} _{\Omega _{2}}} and f 1 ∘ f 2 = id Ω 1 {\displaystyle f_{1}\circ f_{2}=\operatorname {id} _{\Omega _{1}}} , where ∘ {\displaystyle \circ } denotes the function composition operator. Real algorithms are not perfect, and when swapping the role of source and target image in a registration problem the so obtained transformations are not the inverse of each other. Inverse consistency can be enforced by adding to the loss function of the registration a symmetric regularisation term that penalises inconsistent transformations ∫ Ω 2 ‖ f 2 ( f 1 ( x ) ) − x ‖ 2 d x + ∫ Ω 1 ‖ f 1 ( f 2 ( x ) ) − x ‖ 2 d x . {\displaystyle \int _{\Omega _{2}}\left\Vert f_{2}(f_{1}(x))-x\right\Vert ^{2}\mathrm {d} x+\int _{\Omega _{1}}\left\Vert f_{1}(f_{2}(x))-x\right\Vert ^{2}\mathrm {d} x.} Inverse consistency can be used as a quality metric to evaluate image registration results. The inverse consistency error ( I C E {\displaystyle ICE} ) measures the distance between the composition of the two transforms and the identity function, and it can be formulated in terms of both average ( I C E a {\displaystyle ICE_{a}} ) or maximum ( I C E m {\displaystyle ICE_{m}} ) over a region of interest Ω {\displaystyle \Omega } of the image: I C E a = 1 ∫ Ω d x ∫ Ω ‖ f 2 ( f 1 ( x ) ) − x ‖ d x I C E m = max x ∈ Ω ‖ f 2 ( f 1 ( x ) ) − x ‖ . {\displaystyle {\begin{aligned}ICE_{a}&={\frac {1}{\int _{\Omega }\mathrm {d} x}}\int _{\Omega }\left\Vert f_{2}(f_{1}(x))-x\right\Vert \mathrm {d} x\\ICE_{m}&=\max _{x\in \Omega }\left\Vert f_{2}(f_{1}(x))-x\right\Vert .\end{aligned}}} While inverse consistency is a necessary property of good registration algorithms, inverse consistency error alone is not a sufficient metric to evaluate the quality of image registration results, since a perfectly consistent mapping, with no other constraint, may be not even close to correctly register a pair of images.
Visual hierarchy
Visual hierarchy, in Gestalt psychology, describes how particular elements in a visual field stand out more than others in a pattern, creating a perceived order of importance. Although it can occur naturally, the term is most often used in design—especially graphic design and cartography—where elements are arranged to appear more important than others. This order is created by the visual contrast between forms in a field of perception. Objects with highest contrast to their surroundings are recognized first by the human mind. == Evidence == There is some scientific evidence for visual hierarchy using eye tracking. For example, one study found that when people agree that a graphic design is good, they exhibit more similar eye movements; measured by the Fréchet distance. == Theory == The concept of visual hierarchy is based in Gestalt psychological theory, an early 20th-century German theory that proposes that the human brain has innate organizing tendencies that “structure individual elements, shapes or forms into a coherent, organized whole,” especially when processing visual information. The German word Gestalt translates into “form,” “pattern,” or “shape” in English. When an element in a visual field disconnects from the ‘whole’ created by the brain's perceptual organization, it “stands out” to the viewer. The shapes that disconnect most severely from their surroundings stand out the most. This is commonly encapsulated as the Von Restorff effect, which states that isolation attracts attention. === Physical characteristics === The brain distinguishes objects based on differences in their physical appearances. These characteristics fall into four categories: color, size, alignment, and character. Each type of contrast can be used to construct a visual hierarchy. The same characteristics are also sometimes categorized (especially among cartographers) according to the visual variables of Jacques Bertin. Color encompasses the hue, saturation, value, and perceived texture of forms. Dark figures will stand out on a light background, light figures will stand out on a dark background, brightly colored figures will stand out on a muted background, and so on. The fluorescent colors used for tennis balls and other sports equipment is intended to make them instantly stand out against almost any natural visual field. Size has a strong influence on visual hierarchy. Large elements typically attract attention, provided that they can be recognized as figures. Alignment is the arrangement of forms relative to one another. For example, items in the upper left corner of a page are often seen first (at least for those readers accustomed to western languages), the center of the field has prominence. Negative space can also be employed: a figure isolated among large amounts of white space will stand out more than one amid other figures. Character includes several kinds of contrasts based on shape. For example, complex patterns attract more attention than simple or predictable patterns, intricate shapes attract more attention than generalized ones. Even large-scale patterns can attract attention if they contrast with the pattern in the remainder of the visual field. Camouflage is an example of eliminating contrast in character in color and/or character specifically to reduce visual hierarchy. The "squint test" is often suggested as a simple, if unscientific, method to evaluate the visual hierarchy of a graphical product like a map or web page. When viewed out of focus (or from a great distance), the viewer is not distracted by details, but can only see overall (gestalt) patterns such as visual hierarchy. All of the above patterns, except some aspects of character, are recognizable by this method. == Application == Visual hierarchy is an important concept in the field of graphic design, a field that specializes in visual organization. Designers attempt to control visual hierarchy to guide the eye to information in a specific order for a specific purpose. One could compare visual hierarchy in graphic design to grammatical structure in writing in terms of the importance of each principle to these fields. === Cartography === In cartographic design, visual hierarchy is used to emphasize certain important features on a map over less important features. Typically, a map has a purpose that dictates a conceptual hierarchy of what should be more or less important, so one of the goals of the choice of map symbols is to match the visual hierarchy to the conceptual hierarchy. The Visual hierarchy of a map may apply to individual geographic features (such as making a single country stand out), to map layers of related features (e.g., making lakes stand out more than roads), and to the entire layout of map and non-map elements (e.g., making the title look more important than the scale bar). Like the main map elements, such features have weight, and the properties that apply to visual hierarchy of map layers also apply to other elements on the page. Size and alignment are the two main determinants of the visual hierarchy for these features. Cartographers often utilize principles of negative space and figure-ground contrast to design an appropriate visual hierarchy by employing contrast between unused space and layout features. === User experience design and behavioral design === In user experience design and behavioural design, such as web design, visual hierarchy is used to prioritize navigational structures and content, so that audiences focus on elements that facilitate system usage, or increases the chance that they notice content that contains psychological nudges. Color is one of many factors used in the design of a visual hierarchy, and a key factor due to the high salience of color perception.