A theoretical approach that focuses on observable behaviors and dismisses internal processes, emphasizing the role of environmental factors in shaping behavior. Foundational for understanding how external factors influence user behavior and for designing behavior-based interventions.
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 schedule of reinforcement where a desired behavior is reinforced every time it occurs, promoting quick learning and behavior maintenance. Important for designing systems that encourage consistent user behavior.
The theory that all behaviors are acquired through conditioning, often used to understand and influence behavior change. Important for designing interventions that promote positive behavior change.
The evaluation of products based on their ability to influence and shape user behavior. Useful for assessing how well a product guides and influences user actions and decisions.
A temporary increase in the frequency and intensity of a behavior when reinforcement is first removed. Useful for understanding user behavior changes in response to modifications in design or system features.
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.
A technique or tool used to lock oneself into following through on a commitment, often by adding a cost to failing to do so. Useful for designing interventions that help users stick to their goals and commitments.
A psychological phenomenon where a person who has done a favor for someone is more likely to do another favor for that person than if they had received a favor from them. Useful for building positive relationships and encouraging cooperative behavior in design and user interactions.
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.
An organization that applies behavioral science to policy and practice to improve public services and outcomes. Important for understanding practical applications of behavioral science in policy and public services.
Modifications or additions to a system that encourage specific user behaviors. Important for guiding user actions and improving the effectiveness of interactions.
The study of how psychological influences affect financial behaviors and decision-making. Essential for understanding and influencing financial decision-making and behavior.
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 theory that suggests people learn behaviors, skills, and attitudes through observing and imitating others, as well as through direct experiences. Crucial for understanding how users acquire new behaviors and designing educational or training programs.
A cognitive bias where individuals strengthen their beliefs when presented with evidence that contradicts them. Important for understanding user resistance to change and designing strategies to address and mitigate this bias.
A cognitive bias where a person's subjective confidence in their judgments is greater than their objective accuracy. Crucial for understanding user decision-making and designing systems that account for overconfidence.
The tendency to search for, interpret, and remember information in a way that confirms one's preexisting beliefs or hypotheses. Crucial for understanding cognitive biases that affect user decision-making and designing interventions to mitigate them.
Easy, Attractive, Social, and Timely (EAST) is a behavioral insights framework used to influence behavior. Important for designing interventions and user experiences that effectively change behavior.
A strategic framework that designs user experiences to guide behavior and decisions towards desired outcomes. Crucial for creating effective and ethical influence in digital interfaces.
A strategy where engaging, preferred activities are used to motivate users to complete less engaging, necessary tasks. Useful for designing user interfaces and experiences that encourage desired behaviors by leveraging more enjoyable activities as rewards.
A cognitive bias where people avoid negative information or situations, preferring to remain uninformed or ignore problems. Important for understanding user behavior and designing systems that encourage proactive engagement.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.
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 tendency to recall past behavior in a way that aligns with current beliefs and attitudes. Crucial for understanding how memories and self-perception can be influenced by current perspectives.
A cognitive bias where individuals interpret others' behaviors as having hostile intent, even when the behavior is ambiguous or benign. Important for understanding user interactions and designing experiences that mitigate negative interpretations.
The tendency for people to overestimate their ability to control events. Important for understanding user behavior and designing experiences that manage expectations.
The tendency to forget information that can be easily found online, also known as digital amnesia. Important for understanding how access to information impacts memory and designing experiences accordingly.
A cognitive bias where people focus on the most noticeable or prominent information while ignoring less conspicuous details. Important for understanding user decision-making and ensuring balanced presentation of information.
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 cognitive bias that causes people to believe they are less likely to experience negative events and more likely to experience positive events than others. Crucial for understanding user risk perception and designing systems that account for unrealistic optimism.
A cognitive bias where people judge harmful actions as worse, or less moral, than equally harmful omissions (inactions). Important for understanding user decision-making and designing systems that mitigate this bias.
A cognitive bias where people attribute greater value to outcomes that required significant effort to achieve. Useful for designing experiences that recognize and reward user effort and persistence.
The rate at which customers stop using a product or service, often used as a metric to measure customer retention. Crucial for understanding customer behavior and improving retention strategies.
The tendency for the first items presented in a sequence to be remembered better than those in the middle. Crucial for designing information presentation and improving memory retention.
A cognitive bias where people tend to remember the first and last items in a series better than those in the middle, impacting recall and memory. Crucial for designing information presentation to optimize user memory and recall.
The tendency for individuals to favor information that aligns with their existing beliefs and to avoid information that contradicts them. Crucial for understanding how users engage with content and designing systems that present balanced perspectives.
A theory that suggests there is an optimal level of arousal for peak performance, and too much or too little arousal can negatively impact performance. Important for designing experiences that keep users engaged without overwhelming them.
The perception of a relationship between two variables when no such relationship exists. Crucial for understanding and avoiding biases in data interpretation and decision-making.
A rule-of-thumb or shortcut that simplifies decision-making and problem-solving processes. Essential for designing user-friendly interfaces that facilitate quick and efficient decision-making.
A cognitive bias where the perception of one positive trait influences the perception of other unrelated traits. Important for designers to manage and utilize this bias effectively in user experience design.
AI systems that can dynamically adjust their behavior based on new data or changes in the environment. Important for developing systems that can respond to real-time changes and improve over time.
A common pattern of eye movement where users scan web content in an "F" shape, focusing on the top and left side of the page. Crucial for designing web content that aligns with natural reading patterns to improve engagement.
An approach that places the user's needs, preferences, and behaviors at the forefront of all design and development activities. Important for fostering a design culture that prioritizes user satisfaction and engagement.
Also known as the 68-95-99.7 Rule, it states that for a normal distribution, nearly all data will fall within three standard deviations of the mean. Important for understanding the distribution of data and making predictions about data behavior in digital product design.
The psychological phenomenon where people prefer options that are not too extreme, but just right. Crucial for designing products and experiences that cater to the majority preference.
A business strategy where the product itself is the primary driver of customer acquisition, retention, and expansion, often through user experience and engagement. Essential for leveraging the product to drive business growth and achieve market success.
A principle stating that a system should be liberal in what it accepts and conservative in what it sends, meaning it should handle user input flexibly while providing clear, consistent output, similar to the principle of fault tolerance. Essential for designing robust and user-friendly interfaces that accommodate a wide range of user inputs and behaviors while maintaining reliability and clarity in responses.
A cognitive bias where individuals tend to focus on positive information or events more than negative ones, especially as they age. Useful for understanding user preferences and designing experiences that emphasize positive outcomes.
A principle stating that users spend most of their time on other websites and prefer your site to work the same way as all the other sites they already know. Crucial for designing user-friendly and familiar interfaces.
The tendency for people to believe that others are telling the truth, leading to a general assumption of honesty in communication. Important for understanding communication dynamics and designing systems that account for this bias.
The process by which a measure or metric comes to replace the underlying objective it is intended to represent, leading to distorted decision-making. Important for ensuring that metrics accurately reflect true objectives and designing systems that prevent metric manipulation.
A phenomenon where group members make decisions that are more extreme than the initial inclination of its members due to group discussions and interactions. Crucial for understanding and mitigating the risks of extreme decision-making in group settings.
A cognitive bias where group members tend to discuss information that everyone already knows rather than sharing unique information, leading to less effective decision-making. Important for understanding group dynamics and improving the quality of collaborative decision-making among designers.
A research method that involves forming a theory based on data systematically gathered and analyzed. Useful for developing design theories and solutions that are directly grounded in user research and data.
A user research technique where participants organize information into categories to inform information architecture and design. Essential for creating intuitive information architectures and improving user experience.
Guidelines that dictate how a brand should be presented across various media to ensure consistency. Crucial for maintaining brand integrity and ensuring uniformity in brand communications.
A set of rules and standards that define how a brand should be represented across all media and platforms. Crucial for ensuring brand consistency and maintaining brand integrity.
The process of designing and refining prompts to elicit accurate and relevant responses from AI models. Crucial for optimizing the performance of AI applications.
Quantitative measures used to track and assess the performance and success of a product, such as usage rates, customer satisfaction, and revenue. Essential for making data-driven decisions to improve product performance and achieve business goals.