Recency illusion. The recency illusion is the belief or impression that a word or language usage is of recent origin when it is in fact long-established. The term was invented by Arnold Zwicky, a linguist at Stanford University who was primarily interested in examples involving words, meanings, phrases, and grammatical constructions.[1] However, use of the term is not restricted to linguistic phenomena: Zwicky has defined it simply as, "the belief that things you have noticed only recently are in fact recent".[2] Linguistic items prone to the Recency Illusion include: "Singular they" - the use of they to reference a singular antecedent, as in someone said they liked the play.
According to Zwicky, the illusion is caused by selective attention.[2] References[edit] Jump up ^ Intensive and Quotative ALL: something old, something new, John R. External links[edit] SMART HEURISTICS. What interests me is the question of how humans learn to live with uncertainty. Before the scientific revolution determinism was a strong ideal. Religion brought about a denial of uncertainty, and many people knew that their kin or their race was exactly the one that God had favored. They also thought they were entitled to get rid of competing ideas and the people that propagated them. How does a society change from this condition into one in which we understand that there is this fundamental uncertainty? How do we avoid the illusion of certainty to produce the understanding that everything, whether it be a medical test or deciding on the best cure for a particular kind of cancer, has a fundamental element of uncertainty?
Introduction "Isn’t more information always better? " Gigerenzer provides an alternative to the view of the mind as a cognitive optimizer, and also to its mirror image, the mind as a cognitive miser. Gerd Gigernezer 's Edge Bio Page. Judgemental Heuristics and Biases: Try these cases yourself. A. A cab was involved in a hit-and-run accident. Two cab companies serve the city: the Green, which operates 85% of the cabs, and the Blue, which operates the remaining 15%.
A witness identifies the hit-and-run cab as Blue. When the court tests the reliability of the witness under circumstances similar to those on the night of the accident, he correctly identifies the color of the cab 80% of the time and misidentifies it 20% of the time. What is the probability that the cab involved in the accident was Blue, as the witness stated?
B. C. A. B. D. E. A. the large hospital b. the small hospital c. neither, it is equally probable F. G. A) a sure gain of $3000, and b) an 80% chance of winning $4000 and a 20% chance of winning nothing H. I. J. A) a sure loss of $3000, and b) an 80% chance of losing $4000 and a 20% chance of losing nothing Outline I. II. III. IV. V. I. A. B. II. A. III. A. IV. A. V. A. Heuristics “to learn by discovery” Heuristics. Heuristic Evaluation. July 6, 2014 Showing users things they can recognize improves usability over needing to recall items from scratch because the extra context helps users retrieve information from memory. January 1, 1997 Discount usability engineering is our only hope. We must evangelize methods simple enough that departments can do their own usability work, fast enough that people will take the time, and cheap enough that it's still worth doing.
The methods that can accomplish this are simplified user testing with one or two users per design and heuristic evaluation. June 27, 1995 Participants in a course on usability inspection methods were surveyed 7-8 months after the course. January 1, 1995 Heuristic evaluation is a good method of identifying both major and minor problems with an interface, but the lists of usability problems found by heuristic evaluation will tend to be dominated by minor problems, which is one reason severity ratings form a useful supplement to the method. Metaheuristic. Compared to optimization algorithms and iterative methods, metaheuristics do not guarantee that a globally optimal solution can be found on some class of problems. Many metaheuristics implement some form of stochastic optimization, so that the solution found is dependent on the set of random variables generated.[1] By searching over a large set of feasible solutions, metaheuristics can often find good solutions with less computational effort than can algorithms, iterative methods, or simple heuristics.
As such, they are useful approaches for optimization problems.[1] Several books and survey papers have been published on the subject.[1][3][4] Properties and classification[edit] Different classifications of metaheuristics. These are properties that characterize most metaheuristics: There are a wide variety of metaheuristics[1] and a number of properties along which to classify them. One approach is to characterize the type of search strategy. Applications[edit] Contributions[edit] 10 Heuristics for User Interface Design. Visibility of system status The system should always keep users informed about what is going on, through appropriate feedback within reasonable time. (Read full article on visibility of system status.)
Match between system and the real world The system should speak the users' language, with words, phrases and concepts familiar to the user, rather than system-oriented terms. Follow real-world conventions, making information appear in a natural and logical order. (Read full article on the match between the system and the real world.) User control and freedom Users often choose system functions by mistake and will need a clearly marked "emergency exit" to leave the unwanted state without having to go through an extended dialogue. Consistency and standards Users should not have to wonder whether different words, situations, or actions mean the same thing. Error prevention Even better than good error messages is a careful design which prevents a problem from occurring in the first place. See Also. Heuristic. A heuristic technique (/hjʉˈrɪstɨk/; Greek: "Εὑρίσκω", "find" or "discover"), sometimes called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical methodology not guaranteed to be optimal or perfect, but sufficient for the immediate goals.
Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgment, stereotyping, profiling, or common sense. More precisely, heuristics are strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines.[1] Example[edit] Here are a few other commonly used heuristics, from George Pólya's 1945 book, How to Solve It:[2] Psychology[edit] Well known[edit] Lesser known[edit] 10 More Common Faults in Human Thought.
Humans This list is a follow up to Top 10 Common Faults in Human Thought. Thanks for everyone’s comments and feedback; you have inspired this second list! It is amazing that with all these biases, people are able to actually have a rational thought every now and then. There is no end to the mistakes we make when we process information, so here are 10 more common errors to be aware of. The confirmation bias is the tendency to look for or interpret information in a way that confirms beliefs. The Availability heuristic is gauging what is more likely based on vivid memories. Illusion of Control is the tendency for individuals to believe they can control or at least influence outcomes that they clearly have no influence on. Interesting Fact: when playing craps in a casino, people will throw the dice hard when they need a high number and soft when they need a low number.
The Planning fallacy is the tendency to underestimate the time needed to complete tasks. Bonus Attribute Substitution.