A phenomenon where people fail to recognize a repeated item in a visual sequence, impacting information processing and perception. Important for understanding visual perception and designing interfaces that avoid repetitive confusion.
A cognitive bias where people disproportionately prefer smaller, immediate rewards over larger, later rewards. Important for understanding and designing around user decision-making and reward structures.
The study of how colors affect perceptions and behaviors. Important for designing experiences that evoke desired emotional responses from users.
A cognitive bias where individuals favor others who are perceived to be similar to themselves, affecting judgments and decision-making. Crucial for understanding biases in team dynamics and decision-making processes among designers.
A component in neural networks that allows the model to focus on specific parts of the input, improving performance. Essential for developing advanced AI models, particularly in natural language processing.
Quantitative data that provides broad, numerical insights but often lacks the contextual depth that thick data provides. Useful for capturing high-level trends and patterns, but should be complemented with thick data to gain a deeper understanding of user behavior and motivations.
A phenomenon where an item that stands out is more likely to be remembered than other items, often used in design to highlight important elements. Crucial for designing interfaces that effectively capture user attention.
Emotional states where individuals are calm and rational, often contrasted with hot states where emotions run high. Important for understanding decision-making processes and designing experiences that accommodate both states.
A visual or auditory cue that indicates how to interact with an element in the user interface. Crucial for enhancing usability by clearly communicating the purpose and function of UI elements.
The tendency for people's perception to be affected by their recurring thoughts at the time. Important for understanding how current thoughts influence user perception and decision-making.
The ability to understand and share the feelings of customers, crucial for creating user-centered designs and experiences. Crucial for designing products that truly meet user needs and expectations.
The ease with which visual information can be processed and understood by the viewer. Important for creating intuitive and accessible interfaces.
A leadership philosophy where the leader prioritizes the needs of the team, empowering and supporting members to achieve their full potential and fostering a collaborative, inclusive environment. Important for building strong, motivated teams, enhancing collaboration, and promoting a culture of trust and respect within an organization.
A usability testing approach where designers assume that users are easily confused and distracted, focusing on simplicity and clarity in design. Crucial for ensuring that interfaces are intuitive and easy to use under various conditions.
A tendency to avoid making decisions that might lead to regret, influencing risk-taking and decision-making behaviors. Crucial for understanding decision-making processes and designing systems that minimize regret.
A theory that explains how the amount of mental effort required to process information can impact user experience and task performance. Important for designing user interfaces that minimize unnecessary cognitive effort, enhancing usability and user satisfaction.
Not Invented Here (NIH) syndrome refers to the aversion to using or buying products, research, or knowledge developed outside an organization. This mindset can hinder innovation and collaboration.
Decision-making strategies that use simple heuristics to make quick, efficient, and satisfactory choices with limited information. Important for designing user experiences that support quick and efficient decision-making.
A cognitive bias where one negative trait of a person or thing influences the perception of other traits. Important for designing experiences that counteract or mitigate negative biases in user perception.
A cognitive bias where people see patterns in random data. Important for designers to improve data interpretation and avoid false conclusions based on perceived random patterns.
A set of ten general principles for user interface design created by Jakob Nielsen to improve usability. Essential for evaluating and improving user interface designs.
A social norm of responding to a positive action with another positive action, fostering mutual benefit and cooperation. Important for designing user experiences and systems that encourage positive reciprocal interactions.
A theoretical concept in economics that portrays humans as rational and self-interested agents who aim to maximize their utility. Important for understanding economic decision-making and designing systems that align with rational behavior.
A psychological phenomenon where individuals are perceived as more likable if they make a mistake, provided they are generally competent. Important for understanding human perception and leveraging relatability in marketing and leadership.
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
The application of neuroscience principles to design, aiming to create more effective and engaging user experiences based on how the brain processes information. Crucial for creating designs that align with human cognitive and emotional processes.
A theory of motivation that emphasizes the importance of autonomy, competence, and relatedness in fostering intrinsic motivation and psychological well-being. Important for understanding how to design experiences that support user motivation and well-being.
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
The ability to use learned knowledge and experience, often increasing with age and accumulated learning. Important for understanding how expertise and knowledge accumulation impact design and decision-making.
Specific and less common keyword phrases that visitors are more likely to use when they are closer to making a purchase or when using voice search. Important for targeting niche markets and improving SEO with highly specific search terms.
The minimum difference in stimulus intensity that a person can detect, also known as the just noticeable difference (JND). Crucial for designing user interfaces that are sensitive to changes in user input and feedback.
A graphical representation of the distribution of numerical data, typically showing the frequency of data points in successive intervals. Important for analyzing and interpreting data distributions, aiding in decision-making and optimization in product design.
The tendency to attribute positive qualities to one's own choices and downplay the negatives, enhancing post-decision satisfaction. Useful for understanding user satisfaction and designing experiences that reinforce positive decision outcomes.
The study of how the brain perceives and responds to art and design, exploring the neural basis for aesthetic experiences. Important for understanding the neurological underpinnings of aesthetic preferences and enhancing design impact.
The discrepancy between what people intend to do and what they actually do. Crucial for designing interventions that bridge the gap between user intentions and actions.
The phenomenon where external incentives diminish intrinsic motivation, leading to reduced performance or engagement. Important for designing motivational strategies that do not undermine intrinsic motivation.
The phenomenon where higher-priced products are perceived to be of higher quality, regardless of the actual quality. Useful for understanding consumer perceptions and designing effective pricing strategies.
The tendency for negative information to have a greater impact on one's psychological state and processes than neutral or positive information. Important for understanding and mitigating the impact of negative information.
A stimulus that gains reinforcing properties through association with a primary reinforcer, such as money or tokens, which are associated with basic needs. Essential for understanding complex behavior reinforcement strategies and designing effective reward systems.
A cognitive bias where decision-making is affected by the lack of information or uncertainty. Important for understanding and mitigating user decision-making biases due to uncertainty or lack of information.
A framework that combines multiple theories to explain and predict behavior, focusing on intention, knowledge, skills, environmental constraints, and habits. Crucial for designing interventions that effectively change user behavior.
A cognitive bias where people overemphasize information that is placed prominently or in a way that catches their attention first. Crucial for designing interfaces and information displays that manage user attention effectively.
An interdisciplinary study of systems, examining the complex interactions and relationships between components within a whole. Crucial for understanding and designing complex, interconnected systems.
The tendency to cling to one's beliefs even in the face of contradictory evidence. Important for understanding resistance to change and designing interventions that address this bias.
A cognitive bias where people judge the likelihood of an event based on its relative size rather than absolute probability. Important for understanding user decision-making biases and designing systems that present information accurately.
The tendency to give more weight to negative experiences or information than positive ones. Crucial for understanding user behavior and designing systems that balance positive and negative feedback.
A design technique that overrides the default scrolling behavior, often to create a more controlled or immersive experience. Controversial; can enhance or hinder user experience depending on implementation.
A philosophical approach to culture and literature that seeks to confront the social, historical, and ideological forces and structures that produce and constrain it. Valuable for analyzing and addressing power dynamics and biases in design.
The tendency to judge the strength of arguments based on the believability of their conclusions rather than the logical strength of the arguments. Important for understanding cognitive biases that affect decision-making and user perceptions.
A phenomenon where the probability of recalling an item from a list depends on the length of the list. Important for understanding memory processes and designing effective information presentation.
Retrieval-Augmented Generation (RAG) is an AI approach that combines retrieval of relevant documents with generative models to produce accurate and contextually relevant responses. Essential for improving the accuracy and reliability of AI-generated content.
A framework inspired by Maslow's Hierarchy of Needs, applied to user experience design, prioritizing basic functionality and reliability before enhancing usability and delight. Essential for creating well-rounded and satisfying user experiences.
The path or sequence of actions users follow based on information scent to find their desired information. Crucial for understanding user behavior and optimizing content discovery paths.
The application of behavioral science principles to improve the design and usability of digital products, focusing on user behavior and interactions. Important for creating user experiences that are intuitive and engaging by leveraging behavioral insights.
The study of the nature, structure, and variation of language, including phonetics, phonology, syntax, semantics, and pragmatics. Essential for understanding how language influences communication and user interactions in digital products.
The mathematical study of waiting lines or queues. Useful for optimizing user flow and reducing wait times in user interfaces.
A principle that states the time it takes to make a decision increases with the number and complexity of choices available. Crucial for designing user interfaces that minimize cognitive load and enhance decision-making efficiency.
The phenomenon where individuals' expectations about a situation influence their actual experience of that situation. Useful for understanding the influence of expectations on outcomes.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
The study of the principles that govern human behavior, including how people respond to stimuli and learn from their environment. Crucial for designing user experiences that anticipate and influence user behavior.