Loss Aversion
A cognitive bias where the pain of losing is psychologically more powerful than the pleasure of gaining. Important for designing user experiences that account for and mitigate loss aversion.
164 topics found for:
“design biases”
Peak End Rule
A cognitive bias where people judge an experience largely based on how they felt at its peak (most intense point) and its end, rather than the total sum of the experience. Crucial for designing memorable and satisfying user experiences.
Ostrich Effect
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.
Mental Accounting
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.
Baader-Meinhof Phenomenon
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.
Effort Justification
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.
Information Aversion
The tendency to avoid information that one perceives as potentially negative or anxiety-inducing. Important for designing experiences that encourage information-seeking behavior.
Decoy Effect
A cognitive bias where consumers change their preference between two options when presented with a third, less attractive option. Useful for designers to create choice architectures that effectively influence user decisions.
Critical Theory
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.
Mere Exposure Effect
A psychological phenomenon where people develop a preference for things simply because they are familiar with them. Crucial for designing user experiences that leverage familiarity to increase user comfort and satisfaction.
Primacy Effect
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.
Endowment Effect
A cognitive bias where people ascribe more value to things merely because they own them. Useful for understanding user attachment and designing persuasive experiences.
Serial Position Effect
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.
Backfire Effect
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.
Scaling Fallacy
A cognitive bias where people underestimate the complexity and challenges involved in scaling systems, processes, or businesses. Important for understanding the difficulties of scaling and designing systems that address these challenges.
Anchoring
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.
Dunning-Kruger Effect
A cognitive bias where individuals with low ability at a task overestimate their ability, while experts underestimate their competence. Crucial for designers to create educational content and user interfaces that accommodate varying levels of user expertise.
Selective Exposure
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.
Paradox of Choice
The phenomenon where having too many options leads to anxiety and difficulty making a decision, reducing overall satisfaction. Important for designing user experiences that balance choice and simplicity to enhance satisfaction.
Repetition Blindness
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.
Centre-Stage Effect
The tendency for people to pay more attention to items placed in the center of a visual field. Crucial for designing layouts that maximize visibility and impact of key elements.
Fallacy of Composition
A logical fallacy that occurs when one assumes that what is true for a part is also true for the whole. Important for avoiding incorrect assumptions in design and decision-making.
Risk Compensation
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.
Decision Theory
The study of how individuals make choices among alternatives and the principles that guide these choices. Important for designing decision-making processes and interfaces that help users make informed choices.
Occam’s Razor
A principle that suggests the simplest explanation is often the correct one, favoring solutions that make the fewest assumptions. Crucial for problem-solving and designing straightforward, efficient solutions.
Conjunction Fallacy
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.
Take the Best Heuristic
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.
Semmelweis Reflex
A cognitive bias where new evidence or knowledge is automatically rejected because it contradicts established norms or beliefs. Important for recognizing resistance to change and designing strategies to encourage openness to new ideas among designers.
Satisficing
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.
Continued Influence Effect
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.
Probability Matching
A decision-making strategy where individuals allocate resources proportionally to the probability of an outcome occurring, rather than optimizing the most likely outcome. Important for understanding decision-making behaviors and designing systems that guide better resource allocation.
Pessimism Bias
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.
Actor-Observer Bias
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.
Machine Learning 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.
Intentionality Bias
The tendency to attribute intentional actions to others' behaviors, often overestimating their intent. Important for understanding and mitigating biases in user interactions and feedback.
Optimism Bias
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.
Present Bias
A cognitive bias where individuals give stronger weight to payoffs that are closer to the present time compared to those in the future. Important for understanding user time-related decision-making and designing systems that encourage long-term thinking.
Expectation Bias
A cognitive bias where individuals' expectations influence their perceptions and judgments. Relevant for understanding how expectations skew perceptions and decisions among users.
Zero-Risk Bias
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.
Attention Bias
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.
Diversification Bias
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.
Numerosity 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.
Consistency 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.
Bundling Bias
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.
Category Size Bias
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.
Choice-Supportive Bias
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.
Pro-Innovation Bias
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.
Group Attribution Error
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.
Hot Hand Fallacy
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.
Illusion of Validity
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.
Illusion of Control
The tendency for people to overestimate their ability to control events. Important for understanding user behavior and designing experiences that manage expectations.
Focusing Effect
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.
Hyperbolic Discounting
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.
Licensing Effect
A cognitive bias where people allow themselves to indulge after doing something positive, believing they have earned it. Important for understanding user behavior and designing systems that account for self-regulation.
Illusory Correlation
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.
Myopic Loss Aversion
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.
Denomination Effect
A cognitive bias where people are less likely to spend large denominations of money compared to an equivalent amount in smaller denominations. Useful for designers to understand consumer behavior and design pricing strategies that consider spending biases.
Winner’s Curse
A phenomenon where the winner of an auction tends to overpay due to emotional competition, leading to a less favorable outcome than anticipated. Important for understanding decision-making biases and designing systems that mitigate overbidding risks.
Extension Neglect
A cognitive bias where people ignore the relevance of sample size in making judgments, often leading to erroneous conclusions. Crucial for designers to account for appropriate sample sizes in research and analysis.
Sunk Cost Fallacy
The tendency for individuals to continue a behavior or endeavor as a result of previously invested resources (time, money, or effort) rather than future potential benefits. Important for understanding decision-making biases and designing systems that help users avoid irrational persistence.