A model by Don Norman outlining the cognitive steps users take when interacting with a system: goal formation, planning, specifying, performing, perceiving, interpreting, and comparing. Important for designing user-friendly and effective products by understanding and supporting user behavior at each stage.
An experimental design where different groups of participants are exposed to different conditions, allowing for comparison between groups. Important for understanding and applying different experimental designs in user research.
A model that explains behavior change through the interaction of three elements: motivation, ability, and triggers. Crucial for designing interventions and experiences that effectively change user behavior.
Accessible Rich Internet Applications (ARIA) is a set of attributes that enhance the accessibility of web content for people with disabilities. Essential for making web applications more usable and inclusive.
The reduction of restraint in behavior, often due to the absence of social cues, which can lead to impulsive actions and emotional outbursts. Important for understanding user behavior in online and anonymous contexts.
The process by which attention is guided by internal goals and external stimuli, affecting how information is processed and remembered. Useful for designing user interfaces that direct user attention effectively.
A principle stating that as the flexibility of a system increases, its usability often decreases, and vice versa. Crucial for balancing versatility and ease of use in design.
A prompt or cue that initiates a behavior or response, often used in behavior design to encourage specific actions. Crucial for designing systems that effectively prompt desired user behaviors.
The drive to perform an activity for its inherent satisfaction rather than for some separable consequence. Crucial for designing experiences that engage users through inherent enjoyment and interest.
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
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 principle that states tasks always take longer than expected, even when considering Hofstadter's Law itself. Important for setting realistic project timelines and managing expectations in digital product development.
A type of model architecture primarily used in natural language processing tasks, known for its efficiency and scalability. Essential for state-of-the-art NLP applications.
A theory suggesting that information processed at a deeper, more meaningful level is better remembered than information processed at a shallow level. Crucial for designing educational and informational content that enhances retention and understanding.
The phenomenon where people remember information better when it is presented through multiple sensory modalities rather than a single modality. Crucial for enhancing memory retention and understanding through multimodal presentations.
The mental and physical effort required to complete a task, influencing user experience and performance. Crucial for designing systems that minimize cognitive and physical load, enhancing usability and efficiency.
Also known as Self Relevance Effect, the tendency for individuals to better remember information that is personally relevant or related to themselves. Important for designing personalized user experiences and enhancing memory retention.
A statistical rule stating that nearly all values in a normal distribution (99.7%) lie within three standard deviations (sigma) of the mean. Important for identifying outliers and understanding variability in data, aiding in quality control and performance assessment in digital product design.
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 tendency for individuals to mimic the actions of a larger group, often leading to conformity and groupthink. Crucial for understanding social influence and designing experiences that consider group dynamics.
The study of mental processes such as perception, memory, reasoning, and problem-solving. Important for designing interfaces that align with how users process information and make decisions.
A statistical method used to identify underlying relationships between variables by grouping them into factors. Crucial for simplifying data and identifying key variables in research.
A cognitive bias where people perceive an outcome as certain while it is actually uncertain, based on how information is presented. Crucial for understanding and mitigating biased user decision-making.
The drive to perform an activity due to external rewards or pressures rather than for the inherent enjoyment of the activity itself. Important for designing systems that effectively use external incentives to motivate user behavior.
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 mental shortcut where current emotions influence decisions, often bypassing logic and reasoning. Important for understanding how emotions impact user decisions, aiding in more effective design and marketing.
Small rewards or incentives given to users to encourage specific behaviors or actions. Important for motivating user engagement and fostering desired behaviors.
The principle stating that there is a limit to the amount of complexity that users can handle, and if designers don't manage complexity, users will. Crucial for designing user-friendly systems that manage complexity effectively.
A Gestalt principle where the mind completes incomplete figures to form a whole, aiding in the perception of shapes and objects. Crucial for designing visual elements that are easily understood by users.
The series of actions or operations involved in the acquisition, interpretation, storage, and retrieval of information. Crucial for understanding how users handle information and designing systems that align with cognitive processes.
The economic theory that suggests limited availability of a resource increases its value, influencing decision-making and behavior. Important for creating urgency and increasing perceived value in marketing.
The belief that abilities and intelligence can be developed through dedication and hard work. Important for fostering a culture of continuous learning and improvement.
Critical Incident Technique (CIT) is a method used to gather and analyze specific incidents that significantly contribute to an activity or outcome. This method is important for identifying key factors that influence performance and user satisfaction.
A behavioral economic theory that describes how people choose between probabilistic alternatives that involve risk, where the probabilities of outcomes are known. Crucial for understanding decision-making under risk and designing systems that align with user behavior.
A state of overthinking and indecision that prevents making a choice, often due to too many options or uncertainty. Important for designing interfaces that simplify decision-making processes for users.
Practical applications of behavioral science to understand and influence human behavior in various contexts. Crucial for applying scientific insights to design and improve user experiences and outcomes.
A decision-making rule where individuals choose the option with the highest perceived value based on the first good reason that comes to mind, ignoring other information. Crucial for understanding and designing for quick decision-making processes.
A cognitive process that groups information into manageable units, making it easier to remember and process. Important for designing user interfaces that enhance usability and information retention.
A cognitive bias where people seek out more information than is needed to make a decision, often leading to analysis paralysis. Crucial for designing decision-making processes that avoid information overload for users.
The tendency to believe that large or significant events must have large or significant causes. Important for understanding cognitive biases in decision-making and designing systems that present accurate causal relationships.
A decision-making strategy that involves choosing an option that meets the minimum requirements rather than seeking the optimal solution, balancing effort and outcome. Important for designing user experiences that accommodate decision-making under constraints.
A cognitive architecture model that explains how humans can learn and adapt to new tasks. Useful for understanding user learning and behavior adaptation, informing better user experience design.
The design of environments in which people make decisions, influencing their choices and behaviors. Important for creating user experiences that guide decision-making processes effectively.
A phenomenon where users fail to notice significant changes in their visual field. Important for understanding and designing around potential user perception issues.
Needs and expectations that are not explicitly stated by users but are inferred from their behavior and context. Crucial for identifying and addressing unarticulated user needs.
A cognitive bias where someone mistakenly assumes that others have the same background knowledge they do. Essential for designers to ensure communications and products are clear and accessible to all users, regardless of their background knowledge.
A motivational theory suggesting that individuals are motivated to act based on the expected outcomes of their actions and the attractiveness of those outcomes. Important for understanding motivation and behavior, distinct from decision-making under uncertainty.
The theory that people adjust their behavior in response to the perceived level of risk, often taking more risks when they feel more protected. Important for designing safety features and understanding behavior changes in response to risk perception.
A statistical theory that states that the distribution of sample means approximates a normal distribution as the sample size becomes larger, regardless of the population's distribution. Important for making inferences about population parameters and ensuring the validity of statistical tests in digital product design.
A cognitive bias where people prefer the option that seems to eliminate risk entirely, even if another option offers a greater overall benefit. Important for understanding decision-making and designing risk communication for users.
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.
A cognitive bias where individuals or organizations continue to invest in a failing project or decision due to the amount of resources already committed. Important for designers to recognize and mitigate their own risks of continuing unsuccessful initiatives.
The principle that elements in a digital interface maintain consistent appearance, position, and behavior across different pages and states to help users maintain orientation and familiarity. Important for creating a stable and predictable user experience, reducing disorientation and enhancing usability.
A cognitive bias where people judge the likelihood of an event based on the size of its category rather than its actual probability. Crucial for designers to understand how category size influences user perception and decision-making processes.
Methods and techniques used to overcome mental blocks that hinder creative thinking and problem-solving. Crucial for maintaining productivity and fostering innovation in design.
Garbage In-Garbage Out (GIGO) is a principle stating that the quality of output is determined by the quality of the input, especially in computing and data processing. Crucial for ensuring accurate and reliable data inputs in design and decision-making processes.
The study of computers as persuasive technologies, focusing on how they can change attitudes or behaviors. Important for designing systems that effectively influence user behavior ethically.
A phenomenon where new information interferes with the ability to recall previously learned information, affecting memory retention. Crucial for understanding memory dynamics and designing educational or training programs.
The tendency to perceive a greater quantity as a better value, regardless of the actual utility. Important for understanding consumer behavior and designing effective marketing strategies.
A cognitive bias where individuals overestimate the accuracy of their judgments, especially when they have a lot of information. Important for understanding and mitigating overconfidence in user decision-making.