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.
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“bias”
Negativity Bias
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.
Mood-Congruent Memory Bias
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.
Illusory Superiority
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.
Illusion of Transparency
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.
Pseudocertainty Effect
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.
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.
Escalation of Commitment
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.
Introspection Illusion
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.
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.
Curse Of Knowledge
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.
Representativeness
Representativeness is a heuristic in decision-making where individuals judge the probability of an event based on how much it resembles a typical case. Crucial for understanding biases in human judgment and improving decision-making processes.
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.
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.
Hard-Easy Effect
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
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.
Halo Effect
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.
Subadditivity Effect
A cognitive bias where the total probability assigned to a set of events is less than the sum of the probabilities assigned to each event individually. Important for understanding how users estimate probabilities and make decisions under uncertainty.
Law of the Instrument
Also known as "Maslow's Hammer," a cognitive bias where people rely too heavily on a familiar tool or method, often summarized as "if all you have is a hammer, everything looks like a nail.". Important for designers to recognize and avoid over-reliance on familiar methods in problem-solving and design.
Planning Fallacy
A cognitive bias where individuals underestimate the time, costs, and risks of future actions while overestimating the benefits. Important for realistic project planning and setting achievable goals for designers.
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.
Opportunity Cost Neglect
A cognitive bias where individuals overlook or underestimate the cost of opportunities they forego when making decisions. Crucial for understanding user decision-making behavior and designing systems that highlight opportunity costs.
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.
Clustering Illusion
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.
Certainty Effect
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.
Functional Fixedness
A cognitive bias that limits a person to using an object only in the way it is traditionally used. Important for designers to foster creative problem-solving and innovation.
Pseudo Set Framing
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.
Boundary Extension
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.
Belief Perseverance
The tendency to cling to one's beliefs even in the face of contradictory evidence. Important for understanding resistance to change and designing interventions that address this bias.
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.
Less Is Better Effect
A cognitive bias where people prefer a smaller set of higher-quality options over a larger set with lower overall quality. Useful for designing product offerings and experiences that emphasize quality over quantity for users.
Irrational Escalation
The phenomenon where people continue a failing course of action due to the amount of resources already invested. Important for recognizing and mitigating biased decision-making.
Overconfidence Effect
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.
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.
Affective Forecasting
The process of predicting how one will feel in the future, which often involves biases and inaccuracies. Important for understanding user behavior and decision-making, aiding in the design of better user experiences.
Illusion of Completeness
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.
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.
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.
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.
Positivity Effect
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.
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.
Empathy Gap
A cognitive bias where people underestimate the influence of emotional states on their own and others' behavior. Crucial for designers to account for varying user emotional states in experience design.
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.
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.
Illusory Truth Effect
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.
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.
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.
Recency Effect
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.
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.
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.
Availability Heuristic
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.
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.
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.
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.
Reference Dependence
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.
Bizarreness Effect
A cognitive bias where bizarre or unusual information is better remembered than common information. Useful for designers to create memorable and engaging user experiences by incorporating unique elements.
Perky Effect
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.
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.
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.
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.