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Cognitive Science and User Experience
Cognitive Science is not a new term for professionals from social science backgrounds. But I assume it is definitely a less heard term for UX professionals who transitioned from other fields.
The proliferation of UX boot camps and UX certification courses in recent years enabled many talented enthusiastic passionate professionals from other fields to transition into the UX Field. But the problem is many transitioned UX professionals, and, stakeholders new to User Experience think UX is only restricted to redesigning products based on pointers from usability testing, card sorting, and user interviews.
Many ignore the fact that Social Science knowledge is needed for a deeper understanding of users, this mindset might lead to missing the opportunity to provide a better user experience to all users. This ignorance happens due to three major reasons
- Lack of Secondary Research.
- Lack of Time.
- Over-emphasized quantitative view in real-time behavioral analytics.
In this article, I try to share my knowledge about cognitive science and its relevance in providing a better user experience.
1. What is Cognitive Science?
Cognitive Science’s intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures.
Cognitive science is the interdisciplinary, scientific study of the human mind and its processes. It examines the nature, tasks, and functions of cognition.
Cognitive scientists try to understand human cognitive abilities like perception, language, memory, attention, reasoning, and emotion.
To understand the above functions cognitive science borrows knowledge from fields such as psychology, artificial intelligence, philosophy, neuroscience, linguistics, and anthropology.
A central tenet of cognitive science is that a complete understanding of the mind/brain cannot be attained by studying only a single level.
An example would be the problem of remembering a phone number and recalling it later. One approach to understanding this process would be to study behavior through direct observation, or naturalistic observation. A person could be presented with a phone number and be asked to recall it after some delay of time. Then, the accuracy of the response could be measured.
Another approach to measuring cognitive ability would be to study the firings of individual neurons while a person is trying to remember the phone number.
Neither of these experiments on its own would fully explain how the process of remembering a phone number works.
Even if the technology to map out every neuron in the brain in real-time were available, and it were known when each neuron was firing, it would still be impossible to know how a particular firing of neurons translates into the observed behavior. Thus, an understanding of how these two levels relate to each other is imperative.
Studying a particular phenomenon from multiple levels creates a better understanding of the processes that occur in the brain to give rise to a particular behavior.
David Marr (neuroscientist and physiologist) integrated results from psychology, artificial intelligence, and neurophysiology into new models of visual processing. Marr treated human vision as an information processing system. He put forth the idea that one must understand “information processing systems” at three distinct complementary levels of analysis. This idea is known in cognitive science as Marr’s Tri-Level of Analysis.
- Computational Level : specifying the goals of the computation. what does the system do ?(e.g.: what problems does it solve or overcome?) and similarly, why does it do these things?
- Representation and algorithmic Level: giving a representation of the inputs and outputs and the algorithms which transform one into the other. How does the system do what it does?, specifically, what representations does it use and what processes does it employ to build? and manipulate the representations?
- Implementation Level: how the algorithm and representation may be physically realized? (in the case of biological vision, what neural structures and neuronal activities implement the visual system)
The fundamental concept of cognitive science is that “thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures.”
Lets see how different fields contribute to cognitive science
1.1 Psychology:the study of behaviour and the mind.
To address the crucial questions about the nature of the mind, psychological experiments need to be interpretable within a theoretical framework that postulates mental representations and procedures. One of the best ways of developing theoretical frameworks is by forming and testing computational models intended to be analogous to mental operations.
1.2 Artificial Intelligence: theory and development of machines able to perceive, synthesize, and infering information, such as visual perception, speech recognition, decision-making, and translation between languages.
To complement psychological experiments on deductive reasoning, concept formation, mental imagery, and analogical problem solving, researchers have developed computational models that simulate aspects of human performance. Designing, building, and experimenting with computational models is the central method of artificial intelligence (AI), the branch of computer science concerned with intelligent systems. Ideally in cognitive science, computational models and psychological experimentation go hand in hand, but much important work in AI has examined the power of different approaches to knowledge representation in relative isolation from experimental psychology. Abstract questions such as the nature of representation and computation need not be addressed in the everyday practice of psychology or artificial intelligence, but they inevitably arise when researchers think deeply about what they are doing.
1.3 Neuroscience:the study of the nervous system.
Like cognitive psychologists, neuroscientists often perform controlled experiments, but their observations are very different since neuroscientists are concerned directly with the nature of the brain. With nonhuman subjects, researchers can insert electrodes and record the firing of individual neurons. With humans for whom this technique would be too invasive, it is now common to use magnetic and positron scanning devices to observe what is happening in different parts of the brain while people are doing various mental tasks. For example, brain scans have identified the regions of the brain involved in mental imagery and word interpretation. Additional evidence about brain functioning is gathered by observing the performance of people whose brains have been damaged in identifiable ways. A stroke, for example, in a part of the brain dedicated to language can produce deficits such as the inability to utter sentences. Like cognitive psychology, neuroscience is often theoretical and experimental, and theory development is frequently aided by developing computational models of the behavior of groups of neurons.
1.4 Anthropology: the general study of human society and culture.
Cognitive anthropology expands the examination of human thinking to consider how thought works in different cultural settings. The study of mind should obviously not be restricted to how English speakers think but should consider possible differences in modes of thinking across cultures. Cognitive science is becoming increasingly aware of the need to view the operations of the mind in particular physical and social environments. For cultural anthropologists, the main method is ethnography, which requires living and interacting with members of a culture to a sufficient extent that their social and cognitive systems become apparent. Cognitive anthropologists have investigated, for example, the similarities and differences across cultures in words for colors.
1.5 Philosophy: the study of knowledge, reality, and existence.
Traditionally, philosophers do not perform systematic empirical observations or construct computational models, although there has been a recent rise in work in experimental philosophy. But philosophy remains important to cognitive science because it deals with fundamental issues that underlie the experimental and computational approach to mind.
1.6 Linguistics: the study of language.
It entails a comprehensive, systematic, objective, and precise analysis of all aspects of language, particularly its nature and structure. Linguistics is concerned with both the cognitive and social aspects of language. cognitive linguistics, puts less emphasis on syntax (the system of rules for the structure of a sentence in a language) and more on semantics (the study of the meanings of words and phrases) and concepts.
Sapir–Whorf hypothesis,that the structure of a particular language is capable of influencing the cognitive patterns through which a person shapes his or her world view. Critically, the language one speaks or signs can have downstream effects on ostensibly nonlinguistic cognitive domains, ranging from memory, to social cognition, perception, decision-making, and more.
The cognitive sciences have been dominated by English-speaking researchers studying other English speakers.
The over-reliance on English in the cognitive sciences has led to an underestimation of the centrality of language to cognition at large.
To live up to its mission of understanding the representational and computational capacities of the human mind, cognitive science needs to broaden the linguistic diversity represented in its participants and researchers.
2. How does Cognitive Science help to provide a better User Experience?
Cognitive science is one field that helps to find out the nuances of human behavior, which help digital designers refine user experiences of products that offer great solutions. Yes, Cognitive science has unifying theoretical ideas, but we have to appreciate the diversity of outlooks and methods that researchers in different fields bring to the study of mind and intelligence.
Populistically many think UX Research is only about understanding only the user’s perception, emotions while using the product i.e “Who will experience the consequences of the design decisions and how will various types of users perceive that experience?” Yes, Continuously taking feedback from the user’s the utmost important thing to do for UX Professionals. But it is also about the understanding user’s attention, cognitive load (memory), and reasoning when they interact with products.
Since UX Research is also trying to understand the cognitive functions of humans when they interacting with the product same like cognitive science. It can borrow knowledge from different fields like cognitive science to understand user experience deeper.
- Psychology: How do emotions, motivations, needs, heuristics, and biases of diffrent users play into designing the product?
- Anthropology: How do society and culture of the users affect their experience with product?
- Linguistics: How UX writing (language) used in the product affect the user experience? How we need to customize the icons, symbols for different language speakers? (Because We learnt from cognitive science that the language one speaks or signs can have downstream effects on ostensibly nonlinguistic cognitive domains, ranging from memory, to social cognition, perception, decision-making)
- Neuroscience: How do biological factors like cortisol(stress hormone) levels affect the user experience?
- Philosophy: Is the design decision is objective and ethical for target users? How their set of beliefs/ideology affect their experience with their product?
- Artificial Intelligence: Apart from AI’s natural interface applications such as NLP, Speech Recognition which is capable of providing better experience to users. It is also important to understand AI’s Cognitive science applications such as Expert Systems. I believe by integrating expert systems with digital products will lead a way to provide best user experience to the customers. To build expert systems, product builders need to build computional model of expert’s knowledge & technique that should simulate the expertise of an particular expert.
Some of you know UX Research is an evolved concept and its one of the roots is cognitive science. But Still I feel It is a high time to emphasize the knowlege & relevance of cognitive science in UX Research because It will help empower the Humanizing the technology in deeper level not just on surace level.
UX Researchers should work collectively in hope of understanding the users mind and its interactions with the product. As we move into a world where we move away from point-and-click and into Artificial Intelligence (AI), Augmented Reality (AR), and Voice controlled interfaces, a whole new world of User Experience is opening up, one which requires an in-depth understanding of different people.
“UX Research need to enlarge their horizon to encompass both lived human experience and the possibilities for transformation inherent in human experience.”
Feel Free to connect with me on Linkedin! https://www.linkedin.com/in/isanthoshgandhi
Sources & References
- Cognitive Science & Artificial Intelligence (https://psu.pb.unizin.org/ist110/chapter/5-3-emotional-design/)
- Why Cognitive Science is a powerful tool for good UX? (https://medium.com/@MBatoufflet/why-cognitive-science-is-a-powerful-tool-for-good-ux-1f60df2bff04)
- Cognitive Science (https://plato.stanford.edu/entries/cognitive-science/)
- Demystifying UX Research and the Science of Design (https://lollypop.design/blog/2018/december/demystifying-ux-research-and-the-science-of-design/)
- Over-reliance on English hinders cognitive science (https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(22)00236-4)
- The Embodied Mind (https://mitpress.mit.edu/9780262720212/the-embodied-mind/)
- Applications of Artificial Intelligence & Associated Technologies (https://www.semanticscholar.org/paper/Applications-of-Artificial-Intelligence-%26-Borana/d5b061e6565ce421b4b0b7d56296e882085dc308)
- Cognitive science and artificial intelligence: simulating the human mind and its complexity.(https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ccs.2019.0022)
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