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.
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.
A cognitive bias where people perceive past events as having been more predictable than they actually were. Important for understanding and mitigating biases in user feedback and decision-making.
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 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.
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 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 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.
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 individuals underestimate their own abilities and performance relative to others, believing they are worse than average. Important for understanding self-perception biases among designers and designing systems that support accurate self-assessment.
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.
The cognitive bias where people treat a set of items as more significant when they are perceived as a cohesive group. Important for understanding user perception and decision-making.
A cognitive bias where people remember scenes as being more expansive than they actually were. Important for understanding how users perceive and recall visual information, aiding in better visual design decisions.
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 that causes people to overestimate the likelihood of negative outcomes. Important for understanding user risk perception and designing systems that address irrational pessimism.
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.
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.
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among 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 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 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.
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.
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
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 tendency to believe that things will always function the way they normally have, often leading to underestimation of disaster risks. Important for understanding risk perception and designing systems that effectively communicate potential changes.
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 individuals evaluate the value of bundled items differently than they would if the items were evaluated separately. Important for understanding user behavior and designing effective product bundles and pricing strategies.
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 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 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 cognitive bias where individuals overestimate their own abilities, qualities, or performance relative to others. Important for understanding user self-perception and designing systems that account for inflated self-assessments.
A cognitive bias where people wrongly believe they have direct insight into the origins of their mental states, while treating others' introspections as unreliable. Important for designing experiences that account for discrepancies between user self-perception and actual behavior.
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.
A cognitive bias where people overestimate the probability of success for difficult tasks and underestimate it for easy tasks. Useful for designers to understand user confidence and design
A cognitive bias where individuals overestimate how well their thoughts, feelings, and emotions are understood by others. Crucial for designing communication and user interfaces that account for and mitigate this 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.
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.
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 phenomenon where vivid mental images can interfere with actual perception, causing individuals to mistake imagined experiences for real ones. Important for ensuring that marketing and product design set realistic user expectations to avoid disappointment and maintain brand integrity.
A cognitive bias where people give greater weight to outcomes that are certain compared to those that are merely probable. Important for designers to consider how users weigh certain outcomes more heavily in their decision-making.
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 users believe they have explored all available content, even when more is present. Important for designing interfaces that clearly indicate the presence of additional content.
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.
A cognitive bias where people ascribe more value to things merely because they own them. Useful for understanding user attachment and designing persuasive experiences.
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 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.
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 principle often used in behavioral economics that suggests people evaluate options based on relative comparisons rather than absolute values. Important for understanding decision-making and designing choices that highlight beneficial comparisons.
A strategy where an additional, less attractive option is introduced to make other pricing options look more appealing, often steering customers towards a particular choice. Important for guiding user decisions and increasing the perceived value of targeted pricing tiers.
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 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 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.
A cognitive bias where people attribute group behavior to the characteristics of the group members rather than the situation. Crucial for understanding team dynamics and avoiding misattribution in collaborative settings.
A cognitive bias where individuals tend to avoid risks when they perceive potential losses more acutely than potential gains. Important for understanding decision-making behavior in users and designing systems that mitigate risk aversion.
The tendency to perceive and interpret information based on prior experiences and expectations, influencing how different users perceive design differently. Important for designing interfaces that meet user expectations, improving usability and intuitive navigation.
The mistaken belief that a person who has experienced success in a random event has a higher probability of further success in additional attempts. Crucial for understanding and designing around user decision-making biases.
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 cognitive bias where individuals evaluate outcomes relative to a reference point rather than on an absolute scale. Essential for understanding decision-making and consumer behavior.
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 behavioral economics concept where people categorize and treat money differently depending on its source or intended use. Crucial for understanding financial behavior and designing systems that align with users' mental accounting practices.