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.
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.
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.
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.
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.
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 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 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.
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.
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.
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.
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 people disproportionately prefer smaller, immediate rewards over larger, later rewards. Important for understanding and designing around user decision-making and reward structures.
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 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.
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.
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.
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 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 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 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.
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.
A psychological phenomenon where the desire for harmony and conformity in a group results in irrational or dysfunctional decision-making. Crucial for recognizing and mitigating the risks of poor decision-making in teams.
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.
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 tendency to avoid making decisions that might lead to regret, influencing risk-taking and decision-making behaviors. Crucial for understanding decision-making processes and designing systems that minimize regret.
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.
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.
The psychological discomfort experienced when parting with money, influenced by the payment method and context. Crucial for understanding spending behavior and designing payment systems that mitigate discomfort.
The practice of developing artificial intelligence systems that are fair, transparent, and respect user privacy and rights. Crucial for ensuring that AI technologies are developed responsibly and ethically.
In AI and machine learning, a prompt that specifies what should be avoided or excluded in the generated output, guiding the system to produce more accurate and relevant results. Crucial for refining AI-generated content by providing clear instructions on undesired elements, improving output quality and relevance.
Guidelines and principles designed to ensure that AI systems are developed and used in a manner that is ethical and responsible. Crucial for building trust and ensuring the responsible use of AI technologies.
The study of how psychological influences affect financial behaviors and decision-making. Essential for understanding and influencing financial decision-making and behavior.
Explainable AI (XAI) are AI systems that provide clear and understandable explanations for their decisions and actions. This transparency is crucial for building trust and confidence in AI applications across various domains.
Large Language Model (LLM) is an advanced artificial intelligence system trained on vast amounts of text data to understand and generate human-like text. Essential for natural language processing tasks, content generation, and enhancing human-computer interactions across various applications in product design and development.