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 cognitive bias where people overestimate the importance of information that is readily available. Essential for designers to understand and mitigate how easily accessible information can disproportionately influence decisions.
A cognitive bias where people's decisions are influenced by how information is presented rather than just the information itself. Crucial for designers to minimize bias in how information is presented to users.
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.
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.
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.
Anchoring (also known as Focalism) is a cognitive bias where individuals rely heavily on the first piece of information (the "anchor") when making decisions. Crucial for understanding and mitigating initial information's impact on user decision-making processes.
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.
The experience of noticing something for the first time and then frequently encountering it shortly after, also known as frequency illusion. Important for understanding user perception and cognitive biases in information processing.
A statistical phenomenon where two independent events appear to be correlated due to a selection bias. Important for accurately interpreting data and avoiding misleading conclusions.
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.
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.
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 avoid information that one perceives as potentially negative or anxiety-inducing. Important for designing experiences that encourage information-seeking behavior.
A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
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.
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 tendency for respondents to answer questions in a manner that is not truthful or accurate, often influenced by social desirability or survey design. Important for understanding and mitigating biases in survey and research data.
A cognitive bias that occurs when conclusions are drawn from a non-representative sample, focusing only on successful cases and ignoring failures. Crucial for making accurate assessments and designing systems that consider both successes and failures.
The tendency for individuals to present themselves in a favorable light by overreporting good behavior and underreporting bad behavior in surveys or research. Crucial for designing research methods that mitigate biases and obtain accurate data.
A bias that occurs when the sample chosen for a study or survey is not representative of the population being studied, affecting the validity of the results. Important for ensuring the accuracy and reliability of research findings and avoiding skewed data.
A cognitive bias where people ignore general statistical information in favor of specific information. Critical for designers to use general statistical information to improve decision-making accuracy and avoid bias.
A cognitive bias where people place too much importance on one aspect of an event, causing errors in judgment. Important for understanding decision-making and designing interfaces that provide balanced information.
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 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 avoid negative information or situations, preferring to remain uninformed or ignore problems. Important for understanding user behavior and designing systems that encourage proactive engagement.
A cognitive bias where individuals better remember the most recent information they have encountered, influencing decision-making and memory recall. Important for designing user experiences that leverage or mitigate the impact of recent information.
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.
A cognitive bias where individuals believe that past random events affect the probabilities of future random events. Important for designers to understand user decision-making biases related to randomness.
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.
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 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 mental shortcut that relies on immediate examples that come to mind when evaluating a specific topic, concept, method, or decision. Crucial for understanding how people make decisions and the biases that influence their choices.
A cognitive bias where people tend to believe that others are more affected by media messages and persuasive communications than they are themselves. Important for understanding media influence and designing communications that account for this bias in user perception.
The persistence of misinformation in memory and influence on reasoning, even after it has been corrected. Crucial for understanding and mitigating the impact of misinformation in design and communication.
A self-reinforcing process in which a collective belief gains more plausibility through its increasing repetition in public discourse. Important for understanding how information spreads and influences public perception.
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 logical fallacy where people assume that specific conditions are more probable than a single general one. Important for understanding and addressing cognitive biases in user behavior.
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 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.
Systematic errors in AI models that arise from the data or algorithms used, leading to poor outcomes. Important for ensuring fairness and accuracy in AI systems.
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 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 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 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 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.
The tendency for individuals to give positive responses or feedback out of politeness, regardless of their true feelings. Crucial for obtaining honest and accurate user feedback.
A cognitive bias where individuals overestimate the likelihood of extreme events regressing to the mean. Crucial for understanding decision-making and judgment under uncertainty.
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.
The tendency to overestimate the duration or intensity of the emotional impact of future events. Important for understanding user expectations and satisfaction.
A cognitive bias that causes people to attribute their own actions to situational factors while attributing others' actions to their character. Essential for helping designers recognize their own situational influences on interpreting user behavior and feedback.
A cognitive bias where people prefer a greater variety of options when making simultaneous choices compared to sequential choices. Important for designers to consider user preferences for variety when designing choice architectures and product offerings.
A cognitive bias that leads individuals to prefer things to remain the same rather than change, often resisting new options or changes. Crucial for understanding resistance to change and designing strategies to overcome it among users.
The change in opinions or behavior that occurs when individuals conform to the information provided by others. Important for understanding social dynamics and designing systems that leverage social proof and peer influence.
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.
The tendency for individuals to recall information that is consistent with their current mood. Important for understanding how mood affects memory and designing experiences that account for emotional states.
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.
The tendency to overvalue new innovations and technologies while undervaluing existing or traditional approaches. Important for balanced decision-making and avoiding unnecessary risks in adopting new technologies.
Information Visualization (InfoVis) is the study and practice of visual representations of abstract data to reinforce human cognition. Crucial for transforming complex data into intuitive visual formats, enabling faster insights and better decision-making.
A cognitive bias where repeated statements are more likely to be perceived as true, regardless of their actual accuracy. Crucial for understanding how repetition influences beliefs and designing communication strategies for users.