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Forgetting

Forgetting or disremembering is the apparent loss or modification of information already encoded and stored in an individual's short or long-term memory. It is a spontaneous or gradual process in which old memories are unable to be recalled from memory storage. Problems with remembering, learning and retaining new information are a few of the most common complaints of older adults. Studies show that retention improves with increased rehearsal. This improvement occurs because rehearsal helps to transfer information into long-term memory.

Forgetting curves (amount remembered as a function of time since an event was first experienced) have been extensively analyzed. The most recent evidence suggests that a power function provides the closest mathematical fit to the forgetting function.

Overview
Failing to retrieve an event does not mean that this specific event has been forever forgotten. Research has shown that there are a few health behaviors that to some extent can prevent forgetting from happening so often. One of the simplest ways to keep the brain healthy and prevent forgetting is to stay active and exercise. Staying active is important because overall it keeps the body healthy. When the body is healthy the brain is healthy and less inflamed as well. Older adults who were more active were found to have had less episodes of forgetting compared to those older adults who were less active. A healthy diet can also contribute to a healthier brain and aging process which in turn results in less frequent forgetting.

History
One of the first to study the mechanisms of forgetting was the German psychologist Hermann Ebbinghaus (1885). Using himself as the sole subject in his experiment, he memorized lists of three letter nonsense syllable words—two consonants and one vowel in the middle. He then measured his own capacity to relearn a given list of words after a variety of given time period. He found that forgetting occurs in a systematic manner, beginning rapidly and then leveling off. Although his methods were primitive, his basic premises have held true today and have been reaffirmed by more methodologically sound methods. The Ebbinghaus forgetting curve is the name of his results which he plotted out and made 2 conclusions. The first being that much of what we forget is lost soon after it is originally learned. The second being that the amount of forgetting eventually levels off.

Around the same time Ebbinghaus developed the forgetting curve, psychologist Sigmund Freud theorized that people intentionally forgot things in order to push bad thoughts and feelings deep into their unconscious, a process he called "repression". There is debate as to whether (or how often) memory repression really occurs and mainstream psychology holds that true memory repression occurs only very rarely.

One process model for memory was proposed by Richard Atkinson and Richard Shiffrin in the 1960s as a way to explain the operation of memory. This modal model of memory, also known as the Atkinson-Shiffrin model of memory, suggests there are three types of memory: sensory memory, short-term memory, and long-term memory. Each type of memory is separate in its capacity and duration. In the modal model, how quickly information is forgotten is related to the type of memory where that information is stored. Information in the first stage, sensory memory, is forgotten after only a few seconds. In the second stage, short-term memory, information is forgotten after about 20 years. While information in long-term memory can be remembered for minutes or even decades, it may be forgotten when the retrieval processes for that information fail.

Concerning unwanted memories, modern terminology divides motivated forgetting into unconscious repression (which is disputed) and conscious thought suppression.

Measurements
Forgetting can be measured in different ways all of which are based on recall:

Recall
For this type of measurement, a participant has to identify material that was previously learned. The participant is asked to remember a list of material. Later on they are shown the same list of material with additional information and they are asked to identify the material that was on the original list. The more they recognize, the less information is forgotten.

Free recall and variants
Free recall is a basic paradigm used to study human memory. In a free recall task, a subject is presented a list of to-be-remembered items, one at a time. For example, an experimenter might read a list of 20 words aloud, presenting a new word to the subject every 4 seconds. At the end of the presentation of the list, the subject is asked to recall the items (e.g., by writing down as many items from the list as possible). It is called a free recall task because the subject is free to recall the items in any order that he or she desires.

Prompted (cued) recall
Prompted recall is a slight variation of free recall that consists of presenting hints or prompts to increase the likelihood that the behavior will be produced. Usually these prompts are stimuli that were not there during the training period. Thus in order to measure the degree of forgetting, one can see how many prompts the subject misses or the number of prompts required to produce the behavior.

Relearning method
This method measures forgetting by the amount of training required to reach the previous level of performance. German psychologist Hermann Ebbinghaus (1885) used this method on himself. He memorized lists of nonsensical syllables until he could repeat the list two times without error. After a certain interval, he relearned the list and saw how long it would take him to do this task. If it took fewer times, then there had been less forgetting. His experiment was one of the first to study forgetting.

Recognition
Participants are given a list of words and that they have to remember. Then they are shown the same list of material with additional information and they are asked to identify the material that was on the original list. The more they recognize, the less information is forgotten.

Theories
The four main theories of forgetting apparent in the study of psychology are as follows:

Cue-dependent forgetting
Cue-dependent forgetting (also, context-dependent forgetting) or retrieval failure, is the failure to recall a memory due to missing stimuli or cues that were present at the time the memory was encoded. Encoding is the first step in creating and remembering a memory. How well something has been encoded in the memory can be measured by completing specific tests of retrieval. Examples of these tests would be explicit ones like cued recall or implicit tests like word fragment completion. Cue-dependent forgetting is one of five cognitive psychology theories of forgetting. This theory states that a memory is sometimes temporarily forgotten purely because it cannot be retrieved, but the proper cue can bring it to mind. A good metaphor for this is searching for a book in a library without the reference number, title, author or even subject. The information still exists, but without these cues retrieval is unlikely. Furthermore, a good retrieval cue must be consistent with the original encoding of the information. If the sound of the word is emphasized during the encoding process, the cue that should be used should also put emphasis on the phonetic quality of the word. Information is available however, just not readily available without these cues. Depending on the age of a person, retrieval cues and skills may not work as well. This is usually common in older adults but that is not always the case. When information is encoded into the memory and retrieved with a technique called spaced retrieval, this helps older adults retrieve the events stored in the memory better. There is also evidence from different studies that show age related changes in memory. These specific studies have shown that episodic memory performance does in fact decline with age and have made known that older adults produce vivid rates of forgetting when two items are combined and not encoded.

Organic causes
Forgetting that occurs through physiological damage or dilapidation to the brain are referred to as organic causes of forgetting. These theories encompass the loss of information already retained in long-term memory or the inability to encode new information again. Examples include Alzheimer's, amnesia, dementia, consolidation theory and the gradual slowing down of the central nervous system due to aging.

Interference theories
Interference theory refers to the idea that when the learning of something new causes forgetting of older material on the basis of competition between the two. This essentially states that memory's information may become confused or combined with other information during encoding, resulting in the distortion or disruption of memories. In nature, the interfering items are said to originate from an overstimulating environment. Interference theory exists in three branches: Proactive, Retroactive and Output. Retroactive and Proactive inhibition each referring in contrast to the other. Retroactive interference is when new information (memories) interferes with older information. On the other hand, proactive interference is when old information interferes with the retrieval of new information. This is sometimes thought to occur especially when memories are similar. Output Interference occurs when the initial act of recalling specific information interferes with the retrieval of the original information. Another reason why retrieval failure occurs is due to encoding failure. The information never made it to long-term memory storage. According to the level of processing theory, how well information is encoded depends on the level of processing a piece of information receives. Certain parts of information are better encoded than others; for example, information this visual imagery or that has a survival value is more easily transferred to the long-term memory storage. This theory shows a contradiction: an extremely intelligent individual is expected to forget more hastily than one who has a slow mentality. For this reason, an intelligent individual has stored up more memory in his mind which will cause interferences and impair their ability to recall specific information. Based on current research, testing interference has only been carried out by recalling from a list of words rather than using situation from daily lives, thus it is hard to generalize the findings for this theory. It has been found that interference related tasks decreased memory performance by up to 20%, with negative effects at all interference time points and large variability between participants concerning both the time point and the size of maximal interference. Furthermore, fast learners seem to be more affected by interference than slow learners. People are also less likely to recall items when intervening stimuli are presented within the first ten minutes after learning. Recall performance is better without interference. Peripheral processes such as encoding time, recognition memory and motor execution decline with age. However proactive interference is similar. Suggesting contrary to earlier reports that the inhibitory processes observed with this paradigm remain intact in older adults.

Trace decay theory
Decay theory states that when something new is learned, a neurochemical, physical "memory trace" is formed in the brain and over time this trace tends to disintegrate, unless it is occasionally used. Decay theory states the reason we eventually forget something or an event is because the memory of it fades with time. If we do not attempt to look back at an event, the greater the interval time between the time when the event from happening and the time when we try to remember, the memory will start to fade. Time is the greatest impact in remembering an event.

Trace decay theory explains memories that are stored in both short-term and long-term memory system, and assumes that the memories leave a trace in the brain. According to this theory, short-term memory (STM) can only retain information for a limited amount of time, around 15 to 30 seconds unless it is rehearsed. If it is not rehearsed, the information will start to gradually fade away and decay. Donald Hebb proposed that incoming information causes a series of neurons to create a neurological memory trace in the brain which would result in change in the morphological and/or chemical changes in the brain and would fade with time. Repeated firing causes a structural change in the synapses. Rehearsal of repeated firing maintains the memory in STM until a structural change is made. Therefore, forgetting happens as a result of automatic decay of the memory trace in brain. This theory states that the events between learning and recall have no effects on recall; the important factor that affects is the duration that the information has been retained. Hence, as longer time passes more of traces are subject to decay and as a result the information is forgotten.

One major problem about this theory is that in real-life situation, the time between encoding a piece of information and recalling it, is going to be filled with all different kinds of events that might happen to the individual. Therefore, it is difficult to conclude that forgetting is a result of only the time duration. It is also important to consider the effectiveness of this theory. Although it seems very plausible, it is about impossible to test. It is difficult to create a situation where there is a blank period of time between presenting the material and recalling it later.

This theory is supposedly contradicted by the fact that one is able to ride a bike even after not having done so for decades. "Flashbulb memories" are another piece of seemingly contradicting evidence. It is believed that certain memories "trace decay" while others do not. Sleep is believed to play a key role in halting trace decay, although the exact mechanism of this is unknown.

Physical and chemical changes in our brain lead to a memory trace, and this is based on the idea of the trace theory of memory. Information that gets into our short-term memory lasts a few seconds (15–20 seconds), and it fades away if it is not rehearsed or practiced as the neurochemical memory trace disappears rapidly. According to the trace decay theory of forgetting, what occurs between the creation of new memories and the recall of these memories is not influenced by the recall. However, the time between these events (memory formation and recalling) decides whether the information can be kept or forgotten. As there is an inverse correlation that if the time is short, more information can be recalled. On the other hand, if the time is long less information can be recalled or more information will be forgotten. This theory can be criticized for not sharing ideas on how some memories can stay and others can fade, though there was a long time between the formation and recall. Newness to something plays a crucial role in this situation. For instance, people are more likely to recall their very first day abroad than all of the intervening days between it and living there. Emotions also play a crucial role in this situation.

Impairments and lack of forgetting
Forgetting can have very different causes than simply removal of stored content. Forgetting can mean access problems, availability problems, or can have other reasons such as amnesia caused by an accident.

An inability to forget can cause distress, as with post-traumatic stress disorder and hyperthymesia (in which people have an extremely detailed autobiographical memory).

Social forgetting
Psychologists have called attention to "social aspects of forgetting". Though often loosely defined, social amnesia is generally considered to be the opposite of collective memory. "Social amnesia" was first discussed by Russell Jacoby, yet his use of the term was restricted to a narrow approach, which was limited to what he perceived to be a relative neglect of psychoanalytical theory in psychology. The cultural historian Peter Burke suggested that "it may be worth investigating the social organization of forgetting, the rules of exclusion, suppression or repression, and the question of who wants whom to forget what". In an in-depth historical study spanning two centuries, Guy Beiner proposed the term "social forgetting", which he distinguished from crude notions of "collective amnesia" and "total oblivion", arguing that "social forgetting is to be found in the interface of public silence and more private remembrance". The philosopher Walter Benjamin sees social forgetting closely linked to the question of present-day interests, arguing that "every image of the past that is not recognized by the present as one of its own concerns threatens to disappear irretrievably". Building on this, the sociologist David Leupold argued in the context of competing national narratives that what is suppressed and forgotten in one national narrative "might appear at the core of past narrations by the other" - thus often leading to diametrically opposed, mutually exclusive accounts on the past.

title: Forgetting


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Fay Wray

Vina Fay Wray (September 15, 1907 – August 8, 2004) was a Canadian-American actress best known for starring as Ann Darrow in the 1933 film King Kong. Through an acting career that spanned nearly six decades, Wray attained international recognition as an actress in horror films. She has been dubbed one of the early "scream queens".

After appearing in minor film roles, Wray gained media attention after being selected as one of the "WAMPAS Baby Stars" in 1926. This led to her being contracted to Paramount Pictures as a teenager, where she made more than a dozen feature films. After leaving Paramount, she signed deals with various film companies, being cast in her first horror film roles, in addition to many other types of roles, including in The Bowery (1933) and Viva Villa! (1934), both of which starred Wallace Beery. For RKO Radio Pictures, Inc., Wray starred in the film she is most identified with, King Kong (1933). After the success of King Kong, she made numerous appearances in both film and television, retiring in 1980.

Early life
Wray was born on a ranch near Cardston, Alberta, to parents who were members of the Church of Jesus Christ of Latter-day Saints, Elvina Marguerite Jones, who was from Salt Lake City, Utah, and Joseph Heber Wray, who was from Kingston upon Hull, England. She was one of six children and was a granddaughter of LDS pioneer Daniel Webster Jones. Her ancestors came from England, Scotland, Ireland, Northern Ireland, and Wales.

Her family returned to the United States a few years after she was born; they moved to Salt Lake City in 1912 and moved to Lark, Utah, in 1914. In 1919, the Wray family returned to Salt Lake City, and then relocated to Hollywood, where Fay attended Hollywood High School.

Early acting career
In 1923, Wray appeared in her first film at the age of 16, when she landed a role in a short historical film sponsored by a local newspaper. In the 1920s, Wray appeared in the silent film The Coast Patrol (1925), as well as uncredited bit parts at the Hal Roach Studios.

In 1926, the Western Association of Motion Picture Advertisers selected Wray as one of the "WAMPAS Baby Stars", a group of women whom they believed to be on the threshold of movie stardom. She was at the time under contract to Universal Studios, mostly co-starring in low-budget Westerns opposite Buck Jones.

The following year, Wray was signed to a contract with Paramount Pictures. In 1926, director Erich von Stroheim cast her as the main female lead in his film The Wedding March, released by Paramount two years later. While the film was noted for its high budget and production values, it was a financial failure. It also gave Wray her first lead role. Wray stayed with Paramount to make more than a dozen films and made the transition from silent films to "talkies".

Horror films and King Kong
After leaving Paramount, Wray signed with other film studios. Under these deals, Wray was cast in several horror films, including Doctor X (1932) and Mystery of the Wax Museum (1933). However, her best known films were produced under her deal with RKO Radio Pictures. Her first film with RKO was The Most Dangerous Game (1932), co-starring Joel McCrea. The production was filmed at night on the same jungle sets that were being used for King Kong during the day, and with Wray and Robert Armstrong starring in both movies.

The Most Dangerous Game was followed by the release of Wray's best remembered film, King Kong. According to Wray, Jean Harlow had been RKO's original choice, but because MGM put Harlow under exclusive contract during the pre-production phase of the film, she became unavailable. Wray was approached by director Merian C. Cooper to play the blonde captive of King Kong; the role of Ann Darrow for which she was paid $10,000 ($0 in dollars) to portray. The film was a commercial success and Wray was reportedly proud that the film saved RKO from bankruptcy.

Later career
Wray continued to star in films, including The Richest Girl in the World, but by the early 1940s, her appearances became less frequent. She retired in 1942 after her second marriage but due to financial exigencies she soon resumed her acting career, and over the next three decades, Wray appeared in several films and appeared frequently on television. Wray portrayed Catherine Morrison in the 1953–54 sitcom The Pride of the Family with Natalie Wood playing her daughter. Wray appeared in Queen Bee and The Cobweb, both released in 1955.

Wray appeared in three episodes of Perry Mason: "The Case of the Prodigal Parent" (1958); "The Case of the Watery Witness" (1959), as murder victim Lorna Thomas; and "The Case of the Fatal Fetish" (1965), as voodoo practitioner Mignon Germaine. In 1959, Wray was cast as Tula Marsh in the episode "The Second Happiest Day" of Playhouse 90. Other roles around this time were in the episodes "Dip in the Pool" (1958) and "The Morning After" of CBS's Alfred Hitchcock Presents. In 1960, she appeared as Clara in an episode of 77 Sunset Strip, "Who Killed Cock Robin?" Another 1960 role was that of Mrs. Staunton, with Gigi Perreau as her daughter, in the episode "Flight from Terror" of The Islanders.

Wray appeared in a 1961 episode of The Real McCoys titled "Theatre in the Barn". In 1963, she played Mrs. Brubaker in The Eleventh Hour episode "You're So Smart, Why Can't You Be Good?". She ended her acting career with the 1980 made-for-television film Gideon's Trumpet. In 1988, she published her autobiography On the Other Hand. In her later years, Wray continued to make public appearances. In 1991, she was crowned Queen of the Beaux Arts Ball, presiding with King Herbert Huncke.

She was approached by James Cameron to play the part of Rose Dawson Calvert for his blockbuster Titanic (1997) with Kate Winslet to play her younger self, but she turned down the role, which was subsequently portrayed by Gloria Stuart in an Oscar-nominated performance. She was a special guest at the 70th Academy Awards, where the show's host Billy Crystal introduced her as the "Beauty who charmed the Beast." She was the only 1920s Hollywood actress in attendance that evening. On October 3, 1998, she appeared at the Pine Bluff Film Festival, which showed The Wedding March with live orchestral accompaniment.

In January 2003, the 95-year-old Wray appeared at the 2003 Palm Beach International Film Festival to celebrate the Rick McKay documentary film Broadway: The Golden Age, by the Legends Who Were There, where she was honored with a "Legend in Film" award. In her later years, she visited the Empire State Building frequently; in 1991, she was a guest of honor at the building's 60th anniversary, and in May 2004, she made one of her last public appearances at the ESB. Her final public appearance was at the premiere of the documentary film Broadway: The Golden Age, by the Legends Who Were There in June 2004.

Personal life
Wray married three times – to writers John Monk Saunders and Robert Riskin and the neurosurgeon Sanford Rothenberg (January 28, 1919 – January 4, 1991). She had three children: Susan Saunders, Victoria Riskin, and Robert Riskin Jr.

After returning to the US after finishing The Clairvoyant she became a naturalized citizen of the United States in May 1935.

Death
Wray died in her sleep of natural causes in the night of August 8, 2004, in her apartment on Fifth Avenue Manhattan. She is interred at the Hollywood Forever Cemetery in Hollywood, California.

Two days after her death, the lights of the Empire State Building were lowered for 15 minutes in her memory.

Honors
In 1989, Wray was awarded the Women in Film Crystal Award. Wray was honored with a Legend in Film award at the 2003 Palm Beach International Film Festival. For her contribution to the motion picture industry, Wray was honored with a star on the Hollywood Walk of Fame at 6349 Hollywood Blvd. She received a star posthumously on Canada's Walk of Fame in Toronto on June 5, 2005. A small park near Lee's Creek on Main Street in Cardston, Alberta, her birthplace, was named Fay Wray Park in her honor. The small sign at the edge of the park on Main Street has a silhouette of King Kong on it, remembering her role in King Kong. A large oil portrait of Wray by Alberta artist Neil Boyle is on display in the Empress Theatre in Fort Macleod, Alberta. In May 2006, Wray became one of the first four entertainers to be honored by Canada Post by being featured on a postage stamp.

Partial filmography

 * Gasoline Love (1923 short subject)
 * Just A Good Guy (1924) as Girl Getting Into Car
 * The Coast Patrol (1925) as Beth Slocum
 * Sure-Mike (1925 short) as Salesgirl at Department Store
 * What Price Goofy (1925 short) as Concerned Girl with Perfume (uncredited)
 * Isn't Life Terrible? (1925 short) as Potential Pen-Buyer (uncredited)
 * Thundering Landlords (1925 short) as The Wife
 * Chasing the Chaser (1925 short) as Nursemaid
 * Madame Sans Jane (1925 short)
 * No Father to Guide Him (1925 short) as Beach House Cashier (uncredited)
 * Unfriendly Enemies (1925 short) as The Girl
 * Your Own Back Yard (1925 short) as Woman in Quarrelsome Couple
 * A Lover's Oath (1925) (uncredited) *lost film
 * Moonlight and Noses (1925 short) as Miss Sniff, the Professor's Daughter
 * Should Sailors Marry? (1925 short) as Herself
 * Ben-Hur: A Tale of the Christ (1925) as Slave Girl (unconfirmed, uncredited)
 * WAMPAS Baby Stars of 1926 (1926 short) as Herself
 * One Wild Time (1926 short)
 * Don Key (A Son of a Burro) (1926 short)
 * The Man in the Saddle (1926) as Pauline Stewart *lost film
 * Don't Shoot (1926 short) as Nancy Burton
 * The Wild Horse Stampede (1926) as Jessie Hayden
 * The Saddle Tramp (1926 short)
 * The Show Cowpuncher (1926 short)
 * Lazy Lightning (1926) as Lila Rogers
 * Loco Luck (1927) as Molly Vernon
 * A One Man Game (1927) as Roberta
 * Spurs and Saddles (1927) as Mildred Orth
 * A Trip Through the Paramount Studio (1927 short) as Herself
 * The Legion of the Condemned (1928) as Christine Charteris *lost film
 * The Street of Sin (1928) as Elizabeth *lost film
 * The First Kiss (1928) as Anna Lee *lost film
 * The Wedding March (1928) as Mitzi / Mitzerl Schrammell
 * The Four Feathers (1929) as Ethne Eustace
 * Thunderbolt (1929) as Ritzie
 * Pointed Heels (1929) as Lora Nixon
 * Behind the Make-Up (1930) as Marie Gardoni
 * Paramount on Parade (1930) as Sweetheart (Dream Girl)
 * The Texan (1930) as Consuelo
 * The Border Legion (1930) as Joan Randall
 * The Sea God (1930) as Daisy
 * The Honeymoon (1930, unreleased) as Mitzi
 * Captain Thunder (1930) as Ynez
 * Stub Man (1931)
 * The Slippery Pearls (1931 short) as Herself
 * Dirigible (1931) as Helen Pierce
 * The Conquering Horde (1931) as Taisie Lockhart
 * Not Exactly Gentlemen (1931) as Lee Carleton
 * The Finger Points (1931) as Marcia Collins
 * The Lawyer's Secret (1931) as Kay Roberts
 * The Unholy Garden (1931) as Camille de Jonghe
 * Hollywood on Parade (1932 short subject) as Herself
 * Stowaway (1932) as Mary Foster
 * Doctor X (1932) as Joanne Xavier
 * The Most Dangerous Game (1932) as Eve Trowbridge
 * The Vampire Bat (1933) as Ruth Bertin
 * Mystery of the Wax Museum (1933) as Charlotte Duncan
 * King Kong (1933) as Ann Darrow
 * Below the Sea (1933) as Diana
 * Ann Carver's Profession (1933) as Ann Carver Graham
 * The Woman I Stole (1933) as Vida Carew
 * Shanghai Madness (1933) as Wildeth Christie
 * The Big Brain (1933) as Cynthia Glennon
 * One Sunday Afternoon (1933) as Virginia Brush
 * The Bowery (1933) as Lucy Calhoun
 * Master of Men (1933) as Kay Walling
 * Madame Spy (1934) as Marie Franck
 * The Countess of Monte Cristo (1934) as Janet Krueger
 * Once to Every Woman (1934) as Mary Fanshane
 * Viva Villa! (1934) as Teresa
 * Black Moon (1934) as Gail Hamilton
 * The Affairs of Cellini (1934) as Angela
 * The Richest Girl in the World (1934) as Sylvia Lockwood
 * Cheating Cheaters (1934) as Nan Brockton
 * Woman in the Dark (1934) as Louise Loring
 * Mills of the Gods (1934) as Jean Hastings
 * The Clairvoyant (1935) (US title: The Evil Mind) as Rene
 * Bulldog Jack (1935) as Ann Manders
 * Come Out of the Pantry (1935) as Hilda Beach-Howard
 * White Lies (1935) as Joan Mitchell
 * When Knights Were Bold (1936) as Lady Rowena
 * Roaming Lady (1936) as Joyce Reid
 * They Met in a Taxi (1936) as Mary Trenton
 * It Happened in Hollywood (1937) as Gloria Gay
 * Murder in Greenwich Village (1937) as Kay Cabot aka Lucky
 * The Jury's Secret (1938) as Linda Ware
 * Smashing the Spy Ring (1938) as Eleanor Dunlap
 * Navy Secrets (1939) as Carol Mathews – Posing as Carol Evans
 * Wildcat Bus (1940) as Ted Dawson
 * Adam Had Four Sons (1941) as Molly Stoddard
 * Melody for Three (1941) as Mary Stanley
 * Not a Ladies' Man (1942) as Hester Hunter
 * This Is the Life (1944, co-author of play with Sinclair Lewis)
 * Treasure of the Golden Condor (1953) as Annette, Marquise de St. Malo
 * Small Town Girl (1953) as Mrs. Kimbell
 * The Cobweb (1955) as Edna Devanal
 * Queen Bee (1955) as Sue McKinnon
 * Hell on Frisco Bay (1956) as Kay Stanley
 * Rock, Pretty Baby (1956) as Beth Daley
 * Crime of Passion (1957) as Alice Pope
 * Tammy and the Bachelor (1957) as Mrs. Brent
 * Alfred Hitchcock Presents (1958) (Season 3 Episode 35: "Dip in the Pool") as Mrs. Renshaw
 * Summer Love (1958) as Beth Daley
 * Dragstrip Riot (1958) as Norma Martin / Mrs. Martin
 * Alfred Hitchcock Presents (1959) (Season 4 Episode 14: "The Morning After") as Mrs. Nelson
 * Wagon Train (1962) as Mrs. Edward's, The Cole Crawford Story
 * Gideon's Trumpet (1980) as Edna Curtis
 * Off the Menu: The Last Days of Chasen's (1997 documentary) as Herself
 * Broadway: The Golden Age, by the Legends Who Were There (2003 documentary) as Herself

Cultural references

 * In The Rocky Horror Picture Show, Fay Wray is referenced by name in two songs. At the beginning of the movie, "Science Fiction/Double Feature" contains "Then something went wrong / For Fay Wray and King Kong / They got caught in a celluloid jam", and near the end extraterrestrial transvestite mad scientist Frank N. Furter sings in the song, "Don't Dream It": "Whatever happened to Fay Wray? / That delicate satin-draped frame / As it clung to her thigh / How I started to cry / 'Cause I wanted to be dressed just the same".
 * Mentioned in the chorus of the Jimmy Ray song, "Are You Jimmy Ray?"
 * Type O Negative, on their album Bloody Kisses, has a track titled "Fay Wray Come Out to Play."
 * Mentioned repeatedly in Thomas Pynchon's Gravity's Rainbow.
 * Fay Wray is briefly mentioned in the Bruce Cockburn song "Mama Just Wants to Barrelhouse All Night Long" on the 1973 Night Vision album in its second verse: "I hear the city singing like a siren choir / Some fool tried to set this town on fire / TV preacher screams 'come on along' / I feel like Fay Wray face to face with King Kong / But Mama just wants to barrelhouse all night long."
 * Fay Wray has been mentioned in the Peanuts comic strip a couple of times, mostly involving characters Snoopy and Woodstock in reenacting the iconic scenes from King Kong. The first one was in a September 11, 1976 strip with Snoopy playing as King Kong while holding Woodstock as Ann Darrow (with a brief mention of her co-star Bruce Cabot); and in an August 29, 1976 Sunday strip format where Snoopy is dreaming that his nose is the Empire State Building and Woodstock, as King Kong, is climbing atop of it in real time. Waking up from it, Snoopy complains afterwards that "Fay Wray didn't even show up" in it.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Fay Wray


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Forgetting curve

The forgetting curve hypothesizes the decline of memory retention in time. This curve shows how information is lost over time when there is no attempt to retain it. A related concept is the strength of memory that refers to the durability that memory traces in the brain. The stronger the memory, the longer period of time that a person is able to recall it. A typical graph of the forgetting curve purports to show that humans tend to halve their memory of newly learned knowledge in a matter of days or weeks unless they consciously review the learned material.

The forgetting curve supports one of the seven kinds of memory failures: transience, which is the process of forgetting that occurs with the passage of time.

History
From 1880 to 1885, Hermann Ebbinghaus ran a limited, incomplete study on himself and published his hypothesis in 1885 as Über das Gedächtnis (later translated into English as Memory: A Contribution to Experimental Psychology). Ebbinghaus studied the memorisation of nonsense syllables, such as "WID" and "ZOF" (CVCs or Consonant–Vowel–Consonant) by repeatedly testing himself after various time periods and recording the results. He plotted these results on a graph creating what is now known as the "forgetting curve". Ebbinghaus investigated the rate of forgetting, but not the effect of spaced repetition on the increase in retrievability of memories.

Ebbinghaus's publication also included an equation to approximate his forgetting curve:

$$b = \frac{100k}{(\log(t))^c +k}$$

Here, $$b$$ represents 'Savings' expressed as a percentage, and $$t$$ represents time in minutes, counting from one minute before end of learning. The constants c and k are 1.25 and 1.84 respectively. Savings is defined as the relative amount of time saved on the second learning trial as a result of having had the first. A savings of 100% would indicate that all items were still known from the first trial. A 75% savings would mean that relearning missed items required 25% as long as the original learning session (to learn all items). 'Savings' is thus, analogous to retention rate.

In 2015, an attempt to replicate the forgetting curve with one study subject has shown the experimental results similar to Ebbinghaus' original data.

Ebbinghaus' experiment has significantly contributed to experimental psychology. He was the first to carry out a series of well-designed experiments on the subject of forgetting, and he was one of the first to choose artificial stimuli in the research of experimental psychology. Since his introduction of nonsense syllables, a large number of experiments in experimental psychology has been based on highly controlled artificial stimuli.

Increasing rate of learning
Hermann Ebbinghaus hypothesized that the speed of forgetting depends on a number of factors such as the difficulty of the learned material (e.g. how meaningful it is), its representation and other physiological factors such as stress and sleep. He further hypothesized that the basal forgetting rate differs little between individuals. He concluded that the difference in performance can be explained by mnemonic representation skills.

He went on to hypothesize that basic training in mnemonic techniques can help overcome those differences in part. He asserted that the best methods for increasing the strength of memory are:
 * 1) better memory representation (e.g. with mnemonic techniques)
 * 2) repetition based on active recall (especially spaced repetition).

His premise was that each repetition in learning increases the optimum interval before the next repetition is needed (for near-perfect retention, initial repetitions may need to be made within days, but later they can be made after years). He discovered that information is easier to recall when it's built upon things you already know, and the forgetting curve was flattened by every repetition. It appeared that by applying frequent training in learning, the information was solidified by repeated recalling.

Later research also suggested that, other than the two factors Ebbinghaus proposed, higher original learning would also produce slower forgetting. The more information was originally learned, the slower the forgetting rate would be.

Spending time each day to remember information will greatly decrease the effects of the forgetting curve. Some learning consultants claim reviewing material in the first 24 hours after learning information is the optimum time to actively recall the content and reset the forgetting curve. Evidence suggests waiting 10–20% of the time towards when the information will be needed is the optimum time for a single review.

Some memories remain free from the detrimental effects of interference and do not necessarily follow the typical forgetting curve as various noise and outside factors influence what information would be remembered. There is debate among supporters of the hypothesis about the shape of the curve for events and facts that are more significant to the subject. Some supporters, for example, suggest that memories of shocking events such as the Kennedy Assassination or 9/11 are vividly imprinted in memory (flashbulb memory). Others have compared contemporaneous written recollections with recollections recorded years later, and found considerable variations as the subject's memory incorporates after-acquired information. There is considerable research in this area as it relates to eyewitness identification testimony, and eyewitness accounts are found demonstrably unreliable.

Equations
Many equations have since been proposed to approximate forgetting, perhaps the simplest being an exponential curve described by the equation

$$R = e^{-\frac{t}{S}},$$

where $$R$$ is retrievability (a measure of how easy it is to retrieve a piece of information from memory), $$S$$ is stability of memory (determines how fast $$R$$ falls over time in the absence of training, testing or other recall), and $$t$$ is time.

Simple equations such as this one were not found to provide a good fit to the available data.

title: Forgetting curve


text:
Talk:Forgetting curve

Untitled
Could we have some meaningful detail here? 'halve' over 'days or weeks'? It sounds like the explanatory use of the "Factor of 10" Not helpful.

I beg to disagree with "not helpful". Most people have no idea what is the true forgetting rate. 50% in days or weeks is roughly true and the best short expression of the rate I can come up with. I added more data to the entry but this verges on theorizing. The best argument against "not helpful" is that, following some not-so-honest mnemonists, people tend to belive that we can learn once and remember for ever. In other words, they belive in forgetting rate of 0. Some reputable scientists also embarked on an effort of proving that true, but the only "zero rate" possible comes from implicit repetition - Well. In distinction from 0 (zero) I suppose it's helpful. Otherwise you have to admit it's rather broad. - "Gedaechtnis" should be spelt "Gedachtnis" with an umlaut (two dots) above the 'a'. I don't know how to to this in Wiki - can anyone help out and make the change?

- Dear author I am very interested to know how you define S in your equation! how would you, numerically define the information strenght? or is that open to discussion! - Of course it should be taken with a pinch of salt. It is excelent example of "math envy". That guy might have just tried to be interesting doing math. Sure it looks cool and "sophisticated" and more than 78,3% of people won't understand it :). —Preceding unsigned comment added by 86.61.232.26 (talk) 11:24, 16 April 2009 (UTC)
 * Yes, I would like to know this as well. I would also like to know where the entire equation came from. I cannot find it in any of Ebbinghaus' writings. It does not seem to agree with his data.
 * Is this section dead? There's doesn't appear to be any activity in 2 years. This was posted on 7/13/11. — Preceding unsigned comment added by Griswold62 (talk • contribs) 11:37, 13 July 2011 (UTC)
 * NOT the author: the equation seems do be from the Wozniak paper and I'm guessing it's just a guess at the simplest possible equation to model exponential decay of memory. The equation is similar to a half-life equation, but instead of a half-life, it's a 1/e life... e being about 2.7 something. I take it e was chosen to allow for using the natural log (ln). S then, is essentially the (1/e)-life of the memory; the amount of time it takes before the recall strength (R) is 1/e. — Preceding unsigned comment added by 68.7.175.67 (talk) 20:26, 14 February 2019 (UTC)

Wiki Education Foundation-supported course assignment
This article was the subject of a Wiki Education Foundation-supported course assignment, between 25 February 2020 and 2 May 2020. Further details are available on the course page. Student editor(s): Danieltaocf. Peer reviewers: Ask.krier, Gdg1500, Brynneosh, Johnaguilar007, Czaharris.

Above undated message substituted from Template:Dashboard.wikiedu.org assignment by PrimeBOT (talk) 21:41, 16 January 2022 (UTC)

graph
This graph isn't a very good depiction of the forgetting curve, which is presented in the textbooks as much steeper in the early period and then leveling off fairly fast, not unlike an "L" lying on its side. Also, Ebbinghaus found that the most rapid drop could be measured in hours, not days or weeks, at least in the case of nonsense syllables. --Jcbutler (talk) 20:23, 9 April 2008 (UTC)

 * Yes, AND the graph presents multiple forgetting curves, which sort of implies that the forgetting curve itself somehow predicts the change in decay of a memory after review.... something it certainly does NOT predict. I vote to replace the image with one taken from actual data such as Ebbinghaus's own data. Perhaps there's an image we can take from his own work... — Preceding unsigned comment added by 68.7.175.67 (talk) 20:30, 14 February 2019 (UTC)
 * Agree, I believe this may not be a representative graph of the forgetting curve and rather confusing. The green curves, I believe, are the forgetting curve after reviewing the material with | spaced learning. I will look into this and substitute it with a better image. Danieltaocf (talk) 17:40, 5 April 2020 (UTC)

I think the graph that was added by Danieltaocf greatly improves the strength of this article. It is much easier to understand than the one that was there prior and is overall a more accurate depiction. I know it is supposed to be a simple rendering, but I think the lack of units on the axis make it somewhat misleading. The graph looks like it is very close to approaching zero, but this is not necessarily the case for the curve. The plateau should be more pronounced, in order to effectively understand the concept. Schachterjo19 (talk) 19:30, 24 April 2020 (UTC)

Personal Conversation retention ability question
During a conversation where you are told what to do one time and then the conversation continues about other things. What is the lokely hood that you will remember the task, or that sttement? —Preceding unsigned comment added by 71.156.59.120 (talk) 16:25, 20 March 2009 (UTC)

Description section
Section refers to what sounds like someone's recollection of a study or commentary, but doesn't say where from. If I don't get to this, someone else might like into it. WWB (talk) 03:56, 29 May 2009 (UTC)

Description could be improved, I think the stregth of memory as a related concept can be explained later, after more information on the forgetting curve is introduced. "hypothesizes decline of memory retention in time" is a rather short and broad description for an important concept. Ecehanyurukoglu (talk) 11:58, 24 April 2020 (UTC)

Units
What are the units for R & S in the forgetting curve formula?

R = e^(-t/S)

This formula is a variation of the standard exponential half-life decay function

y = y0 * e^(-(ln(2)/h)t)

where

S = h/ln(2)

In that formula, h is in the same units as t (time), so I assume that S is also in the same units as t (time).

But what is R?

In the half-life formula, y and y0 are usually mass units. Since there is no R0, I am assuming that it is "1".

R = 1 * e^(-t/S)

This means that R goes from 1 to 0, which suggests that it's a probability. Is it the probability of recall?

It would be helpful to have this added to the article. — Preceding unsigned comment added by Griswold62 (talk • contribs) 00:40, 11 July 2011 (UTC)

 * Yes, s would have the same units as t; s is essentially the amount of time before R = 1/e. Time units cancel out and R has no units; it's a probability. Yes, the probability of recall. — Preceding unsigned comment added by 68.7.175.67 (talk) 20:33, 14 February 2019 (UTC)

Article is wrong -- Ebbinghaus' decay process is logarithmic, not exponential
This article is incorrect about the Ebbinghaus data fitting an exponential decay function. Ebbinghaus never mentions the R = e^(-t/S) function. In sections 28-30, his data fits a logarithmic function, which Ebbinghaus mentions. It also fits a power function almost as well. I ran his data through a curve fitting program and got a this power function

R=0.317778t^(-0.1268656)

and this logarithmic function

R=-0.0455882×ln(t)+0.333966

There is no exponential function that come close to fitting this data. — Preceding unsigned comment added by Griswold62 (talk • contribs) 00:52, 20 July 2011 (UTC)

Update 4 December 2017: it's pretty disappointing to see this section entitled "Article is wrong" to be unactioned six years after the comments were made. These comments are correct: the exponential formula given here is not correct. It links to a paper by Wozniak and Gorzelanczyk (1995), but this formula is not a finding or conclusion of that paper. In fact the formula is preceded by the words "Assuming the negatively exponential decrease of retrievability". So basically I consider this completely spurious. In short, the reference given does not support the claim made, it just happens to mention the same formula. Kitjohnson9 (talk) 09:07, 4 December 2017 (UTC)

2019-02-13: I agree completely with both commenters and changed the article to include Ebbinghaus's 1885 equation. I moved the R=e^(-t/s) equation to an 'equations' section and removed the wording implying that 'today we use this equation'... as it not true; I also cite a source, a 1999 article that found simple equations such as this do **not** fit the data well. The article, as well as the existing link to the Murre and Dros paper provide a good discussion of some other equations if anyone is interested, but I haven't bothered to add any to the 'equations' section. — Preceding unsigned comment added by 68.7.175.67 (talk) 20:40, 14 February 2019 (UTC)

Source
This Wired article has a lot of good information that should be incorporated into the article. JKeck (talk) 04:06, 20 May 2012 (UTC)

More on history
As Hermann Ebbinghaus being one of the first to carry out a series of rigorous experiments on the shape of forgetting, the history section of the wiki page could be expanded with facts such as why he chose to use meaningless syllables.Danieltaocf (talk) 18:23, 5 April 2020 (UTC)

Daniel, I agree with your idea to improve upon the history section of this. I think your edits are good and informative. However, I think this must be continued. The last sentence in the history section mentions a "large number of experiments in experimental psychology... based on highly controlled artificial stimuli"; these experiments should be explained below your addition. Regardless, this is a great start! Gdg1500 (talk) 23:43, 23 April 2020 (UTC)

Spacing effect
I suggest that it is worthwhile to add a section about beating the forgetting curve, namely the spaced learning and spacing effect. Danieltaocf (talk) 19:08, 5 April 2020 (UTC)

Daniel, I like how you expanded on the forgetting curve by introducing other psychological concepts. I am somewhat concerned, however, that the references you listed at the bottom of this section (9/11, Kennedy murder) cannot necessarily be compared to other examples of the curve, just because of the amount of coverage these events get. Thus, this makes it harder for one to forget because they are so often reminded of them in the news and popular culture. Therefore, I think they are quite extraneous situations that are not too comparable. Schachterjo19 (talk) 19:27, 24 April 2020 (UTC)

The Spacing Effect already has its own entry... no need to clutter this article with that IMHO. A link to the wikipedia entry and short explanation that the shape of the curve seems to be influenced by repeated study bouts is all that would be necessary, as the spacing effect is very much a separate issue -- Ed, 16:19, 2020-08-09

Peer Review
Daniel, I think you have a great start with edits to be made in this article. I agree that there is potential for a more effective image to be used to represent the forgetting curve, as it can be an easily understood concept if portrayed as such. I agree that added further information on how Ebbinghaus informed his study of the curve would help you to build out the background of the phenomenon. I think you could also add previous studies that showed application of the forgetting curve that have resulted from Ebbinghaus' findings. Finally, while I do not know necessarily if it is possible to "beat the forgetting curve" but you could potentially link articles to other mnemonics and strategies that help to improve long-term memory. Czaharris (talk) 22:10, 23 April 2020 (UTC)

Daniel, I too think that the updated picture was a good start in terms of your edits. It is critical to have data, such as pictures and graphs, that accurately correlate to one's argument. Therefore, you have really strengthened this Wikipedia page in more ways than one. It would be very interesting to see if this curve has evolved at all since one's lifestyle today is a lot different than what the curve was measured out to be whenever it was invented. Also, as Czaharris said, it would be interesting to see if the outliers of the curve experience a similar trend in terms of forgetting over time. User: johnaguilar007 —Preceding undated comment added 03:01, 24 April 2020 (UTC)

I agree with the above that the contributions made have been valuable to the page, in particular with regard to a more useful image and a description of what "savings" means in the context of the study that led to the initial equation. The citations also helped make a more complete scientific picture of how the forgetting curve has been studied. A couple minor suggestions, I think it would be useful to replace the equals sign in the first equation with an "≈" symbol to express that this is an approximation if someone is to lift that equation from this article without reading the context. Also, potentially changing the wording of "Battle the Curve" to something more academic in nature. But overall still great job! Ask.krier (talk) 19:05, 25 April 2020 (UTC)

Peer Review
I think you did a great job on additions and rewording to make it flow better. Brynneosh (talk) 00:48, 25 April 2020 (UTC)

title: Talk:Forgetting curve


text:
Field-programmable gate array

A field-programmable gate array (FPGA) is a type of configurable integrated circuit that can be repeatedly programmed after manufacturing. FPGAs are a subset of logic devices referred to as programmable logic devices (PLDs). They consist of an array of programmable logic blocks with a connecting grid, that can be configured "in the field" to interconnect with other logic blocks to perform various digital functions. FPGAs are often used in limited (low) quantity production of custom-made products, and in research and development, where the higher cost of individual FPGAs is not as important, and where creating and manufacturing a custom circuit wouldn't be feasible. Other applications for FPGAs include the telecommunications, automotive, aerospace, and industrial sectors, which benefit from their flexibility, high signal processing speed, and parallel processing abilities.

A FPGA configuration is generally written using a hardware description language (HDL) e.g. VHDL, similar to the ones used for application-specific integrated circuits (ASICs). Circuit diagrams were formerly used to write the configuration.

The logic blocks of an FPGA can be configured to perform complex combinational functions, or act as simple logic gates like AND and XOR. In most FPGAs, logic blocks also include memory elements, which may be simple flip-flops or more sophisticated blocks of memory. Many FPGAs can be reprogrammed to implement different logic functions, allowing flexible reconfigurable computing as performed in computer software.

FPGAs also have a role in embedded system development due to their capability to start system software development simultaneously with hardware, enable system performance simulations at a very early phase of the development, and allow various system trials and design iterations before finalizing the system architecture.

FPGAs are also commonly used during the development of ASICs to speed up the simulation process.

History
The FPGA industry sprouted from programmable read-only memory (PROM) and programmable logic devices (PLDs). PROMs and PLDs both had the option of being programmed in batches in a factory or in the field (field-programmable).

Altera was founded in 1983 and delivered the industry's first reprogrammable logic device in 1984 – the EP300 – which featured a quartz window in the package that allowed users to shine an ultra-violet lamp on the die to erase the EPROM cells that held the device configuration.

Xilinx produced the first commercially viable field-programmable gate array in 1985 – the XC2064. The XC2064 had programmable gates and programmable interconnects between gates, the beginnings of a new technology and market. The XC2064 had 64 configurable logic blocks (CLBs), with two three-input lookup tables (LUTs).

In 1987, the Naval Surface Warfare Center funded an experiment proposed by Steve Casselman to develop a computer that would implement 600,000 reprogrammable gates. Casselman was successful and a patent related to the system was issued in 1992.

Altera and Xilinx continued unchallenged and quickly grew from 1985 to the mid-1990s when competitors sprouted up, eroding a significant portion of their market share. By 1993, Actel (later Microsemi, now Microchip) was serving about 18 percent of the market.

The 1990s were a period of rapid growth for FPGAs, both in circuit sophistication and the volume of production. In the early 1990s, FPGAs were primarily used in telecommunications and networking. By the end of the decade, FPGAs found their way into consumer, automotive, and industrial applications.

By 2013, Altera (31 percent), Actel (10 percent) and Xilinx (36 percent) together represented approximately 77 percent of the FPGA market.

Companies like Microsoft have started to use FPGAs to accelerate high-performance, computationally intensive systems (like the data centers that operate their Bing search engine), due to the performance per watt advantage FPGAs deliver. Microsoft began using FPGAs to accelerate Bing in 2014, and in 2018 began deploying FPGAs across other data center workloads for their Azure cloud computing platform.

Growth
The following timelines indicate progress in different aspects of FPGA design.

Gates

 * 1987: 9,000 gates, Xilinx
 * 1992: 600,000, Naval Surface Warfare Department
 * Early 2000s: millions
 * 2013: 50 million, Xilinx

Market size

 * 1985: First commercial FPGA : Xilinx XC2064
 * 1987: $14 million
 * c. 1993: >$385 million
 * 2005: $1.9 billion
 * 2010 estimates: $2.75 billion
 * 2013: $5.4 billion
 * 2020 estimate: $9.8 billion
 * 2030 estimate: $23.34 billion

Design starts
A design start is a new custom design for implementation on an FPGA.
 * 2005: 80,000
 * 2008: 90,000

Design
Contemporary FPGAs have ample logic gates and RAM blocks to implement complex digital computations. FPGAs can be used to implement any logical function that an ASIC can perform. The ability to update the functionality after shipping, partial re-configuration of a portion of the design and the low non-recurring engineering costs relative to an ASIC design (notwithstanding the generally higher unit cost), offer advantages for many applications.

As FPGA designs employ very fast I/O rates and bidirectional data buses, it becomes a challenge to verify correct timing of valid data within setup time and hold time. Floor planning helps resource allocation within FPGAs to meet these timing constraints.

Some FPGAs have analog features in addition to digital functions. The most common analog feature is a programmable slew rate on each output pin, allowing the engineer to set low rates on lightly loaded pins that would otherwise ring or couple unacceptably, and to set higher rates on heavily loaded high-speed channels that would otherwise run too slowly. Also common are quartz-crystal oscillator driver circuitry, on-chip RC oscillators, and phase-locked loops with embedded voltage-controlled oscillators used for clock generation and management as well as for high-speed serializer-deserializer (SERDES) transmit clocks and receiver clock recovery. Fairly common are differential comparators on input pins designed to be connected to differential signaling channels. A few mixed signal FPGAs have integrated peripheral analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) with analog signal conditioning blocks, allowing them to operate as a system-on-a-chip (SoC). Such devices blur the line between an FPGA, which carries digital ones and zeros on its internal programmable interconnect fabric, and field-programmable analog array (FPAA), which carries analog values on its internal programmable interconnect fabric.

Logic blocks

The most common FPGA architecture consists of an array of logic blocks called configurable logic blocks (CLBs) or logic array blocks (LABs) (depending on vendor), I/O pads, and routing channels. Generally, all the routing channels have the same width (number of signals). Multiple I/O pads may fit into the height of one row or the width of one column in the array.

"An application circuit must be mapped into an FPGA with adequate resources. While the number of logic blocks and I/Os required is easily determined from the design, the number of routing channels needed may vary considerably even among designs with the same amount of logic. For example, a crossbar switch requires much more routing than a systolic array with the same gate count. Since unused routing channels increase the cost (and decrease the performance) of the FPGA without providing any benefit, FPGA manufacturers try to provide just enough channels so that most designs that will fit in terms of lookup tables (LUTs) and I/Os can be routed. This is determined by estimates such as those derived from Rent's rule or by experiments with existing designs."

In general, a logic block consists of a few logical cells. A typical cell consists of a 4-input LUT, a full adder (FA) and a D-type flip-flop. The LUT might be split into two 3-input LUTs. In normal mode those are combined into a 4-input LUT through the first multiplexer (mux). In arithmetic mode, their outputs are fed to the adder. The selection of mode is programmed into the second mux. The output can be either synchronous or asynchronous, depending on the programming of the third mux. In practice, the entire adder or parts of it are stored as functions into the LUTs in order to save space.

Hard blocks
Modern FPGA families expand upon the above capabilities to include higher-level functionality fixed in silicon. Having these common functions embedded in the circuit reduces the area required and gives those functions increased performance compared to building them from logical primitives. Examples of these include multipliers, generic DSP blocks, embedded processors, high-speed I/O logic and embedded memories.

Higher-end FPGAs can contain high-speed multi-gigabit transceivers and hard IP cores such as processor cores, Ethernet medium access control units, PCI or PCI Express controllers, and external memory controllers. These cores exist alongside the programmable fabric, but they are built out of transistors instead of LUTs so they have ASIC-level performance and power consumption without consuming a significant amount of fabric resources, leaving more of the fabric free for the application-specific logic. The multi-gigabit transceivers also contain high-performance signal conditioning circuitry along with high-speed serializers and deserializers, components that cannot be built out of LUTs. Higher-level physical layer (PHY) functionality such as line coding may or may not be implemented alongside the serializers and deserializers in hard logic, depending on the FPGA.

Soft core

An alternate approach to using hard macro processors is to make use of soft processor IP cores that are implemented within the FPGA logic. Nios II, MicroBlaze and Mico32 are examples of popular softcore processors. Many modern FPGAs are programmed at run time, which has led to the idea of reconfigurable computing or reconfigurable systems – CPUs that reconfigure themselves to suit the task at hand. Additionally, new non-FPGA architectures are beginning to emerge. Software-configurable microprocessors such as the Stretch S5000 adopt a hybrid approach by providing an array of processor cores and FPGA-like programmable cores on the same chip.

Integration
In 2012 the coarse-grained architectural approach was taken a step further by combining the logic blocks and interconnects of traditional FPGAs with embedded microprocessors and related peripherals to form a complete system on a programmable chip. Examples of such hybrid technologies can be found in the Xilinx Zynq-7000 all Programmable SoC, which includes a 1.0 GHz dual-core ARM Cortex-A9 MPCore processor embedded within the FPGA's logic fabric, or in the Altera Arria V FPGA, which includes an 800 MHz dual-core ARM Cortex-A9 MPCore. The Atmel FPSLIC is another such device, which uses an AVR processor in combination with Atmel's programmable logic architecture. The Microsemi SmartFusion devices incorporate an ARM Cortex-M3 hard processor core (with up to 512 kB of flash and 64 kB of RAM) and analog peripherals such as a multi-channel analog-to-digital converters and digital-to-analog converters in their flash memory-based FPGA fabric.

Clocking
Most of the logic inside of an FPGA is synchronous circuitry that requires a clock signal. FPGAs contain dedicated global and regional routing networks for clock and reset, typically implemented as an H tree, so they can be delivered with minimal skew. FPGAs may contain analog phase-locked loop or delay-locked loop components to synthesize new clock frequencies and manage jitter. Complex designs can use multiple clocks with different frequency and phase relationships, each forming separate clock domains. These clock signals can be generated locally by an oscillator or they can be recovered from a data stream. Care must be taken when building clock domain crossing circuitry to avoid metastability. Some FPGAs contain dual port RAM blocks that are capable of working with different clocks, aiding in the construction of building FIFOs and dual port buffers that bridge clock domains.

3D architectures
To shrink the size and power consumption of FPGAs, vendors such as Tabula and Xilinx have introduced 3D or stacked architectures. Following the introduction of its 28 nm 7-series FPGAs, Xilinx said that several of the highest-density parts in those FPGA product lines will be constructed using multiple dies in one package, employing technology developed for 3D construction and stacked-die assemblies.

Xilinx's approach stacks several (three or four) active FPGA dies side by side on a silicon interposer – a single piece of silicon that carries passive interconnect. The multi-die construction also allows different parts of the FPGA to be created with different process technologies, as the process requirements are different between the FPGA fabric itself and the very high speed 28 Gbit/s serial transceivers. An FPGA built in this way is called a heterogeneous FPGA.

Altera's heterogeneous approach involves using a single monolithic FPGA die and connecting other dies and technologies to the FPGA using Intel's embedded multi_die interconnect bridge (EMIB) technology.

Programming
To define the behavior of the FPGA, the user provides a design in a hardware description language (HDL) or as a schematic design. The HDL form is more suited to work with large structures because it's possible to specify high-level functional behavior rather than drawing every piece by hand. However, schematic entry can allow for easier visualization of a design and its component modules.

Using an electronic design automation tool, a technology-mapped netlist is generated. The netlist can then be fit to the actual FPGA architecture using a process called place and route, usually performed by the FPGA company's proprietary place-and-route software. The user will validate the results using timing analysis, simulation, and other verification and validation techniques. Once the design and validation process is complete, the binary file generated, typically using the FPGA vendor's proprietary software, is used to (re-)configure the FPGA. This file is transferred to the FPGA via a serial interface (JTAG) or to an external memory device such as an EEPROM.

The most common HDLs are VHDL and Verilog. National Instruments' LabVIEW graphical programming language (sometimes referred to as G) has an FPGA add-in module available to target and program FPGA hardware. Verilog was created to simplify the process making HDL more robust and flexible. Verilog has a C-like syntax, unlike VHDL.

To simplify the design of complex systems in FPGAs, there exist libraries of predefined complex functions and circuits that have been tested and optimized to speed up the design process. These predefined circuits are commonly called intellectual property (IP) cores, and are available from FPGA vendors and third-party IP suppliers. They are rarely free, and typically released under proprietary licenses. Other predefined circuits are available from developer communities such as OpenCores (typically released under free and open source licenses such as the GPL, BSD or similar license). Such designs are known as open-source hardware.

In a typical design flow, an FPGA application developer will simulate the design at multiple stages throughout the design process. Initially the RTL description in VHDL or Verilog is simulated by creating test benches to simulate the system and observe results. Then, after the synthesis engine has mapped the design to a netlist, the netlist is translated to a gate-level description where simulation is repeated to confirm the synthesis proceeded without errors. Finally, the design is laid out in the FPGA at which point propagation delay values can be back-annotated onto the netlist, and the simulation can be run again with these values.

More recently, OpenCL (Open Computing Language) is being used by programmers to take advantage of the performance and power efficiencies that FPGAs provide. OpenCL allows programmers to develop code in the C programming language. For further information, see high-level synthesis and C to HDL.

Most FPGAs rely on an SRAM-based approach to be programmed. These FPGAs are in-system programmable and re-programmable, but require external boot devices. For example, flash memory or EEPROM devices may load contents into internal SRAM that controls routing and logic. The SRAM approach is based on CMOS.

Rarer alternatives to the SRAM approach include:
 * Fuse: one-time programmable. Bipolar. Obsolete.
 * Antifuse: one-time programmable. CMOS. Examples: Actel SX and Axcelerator families; Quicklogic Eclipse II family.
 * PROM: programmable read-only memory technology. One-time programmable because of plastic packaging. Obsolete.
 * EPROM: erasable programmable read-only memory technology. One-time programmable but with window, can be erased with ultraviolet (UV) light. CMOS. Obsolete.
 * EEPROM: electrically erasable programmable read-only memory technology. Can be erased, even in plastic packages. Some but not all EEPROM devices can be in-system programmed. CMOS.
 * Flash: flash-erase EPROM technology. Can be erased, even in plastic packages. Some but not all flash devices can be in-system programmed. Usually, a flash cell is smaller than an equivalent EEPROM cell and is, therefore, less expensive to manufacture. CMOS. Example: Actel ProASIC family.

Manufacturers
In 2016, long-time industry rivals Xilinx (now part of AMD) and Altera were the FPGA market leaders. At that time, they controlled nearly 90 percent of the market.

Both Xilinx (now AMD) and Altera (now Intel) provide proprietary electronic design automation software for Windows and Linux (ISE/Vivado and Quartus) which enables engineers to design, analyze, simulate, and synthesize (compile) their designs.

In March 2010, Tabula announced their FPGA technology that uses time-multiplexed logic and interconnect that claims potential cost savings for high-density applications. On March 24, 2015, Tabula officially shut down.

On June 1, 2015, Intel announced it would acquire Altera for approximately $16.7 billion and completed the acquisition on December 30, 2015.

On October 27, 2020, AMD announced it would acquire Xilinx and completed the acquisition valued at about $50 billion in February 2022.

In February 2024 Altera became an independent company again with its parent company Intel.

Other manufacturers include:

 * Achronix, manufacturing SRAM based FPGAs with 1.5 GHz fabric speed
 * Altium, provides system-on-FPGA hardware-software design environment.
 * Cologne Chip, German Government backed designer and producer of FPGAs
 * Efinix offers small to medium-sized FPGAs. They combine logic and routing interconnects into a configurable XLR cell.
 * GOWIN Semiconductors, manufacturing small and medium-sized SRAM and Flash-based FPGAs. They also offer pin-compatible replacements for a few Xilinx, Altera and Lattice products.
 * Lattice Semiconductor, which manufactures low-power SRAM-based FPGAs featuring integrated configuration flash, instant-on and live reconfiguration
 * SiliconBlue Technologies, which provides extremely low-power SRAM-based FPGAs with optional integrated nonvolatile configuration memory; acquired by Lattice in 2011
 * Microchip:
 * Microsemi (previously Actel), producing antifuse, flash-based, mixed-signal FPGAs; acquired by Microchip in 2018
 * Atmel, a second source of some Altera-compatible devices; also FPSLIC mentioned above; acquired by Microchip in 2016
 * QuickLogic, which manufactures Ultra Low Power Sensor Hubs, extremely low powered, low-density SRAM-based FPGAs, with display bridges MIPI & RGB inputs, MIPI, RGB and LVDS outputs

Applications
An FPGA can be used to solve any problem which is computable. This is trivially proven by the fact that FPGAs can be used to implement a soft microprocessor, such as the Xilinx MicroBlaze or Altera Nios II. Their advantage lies in that they are significantly faster for some applications because of their parallel nature and optimality in terms of the number of gates used for certain processes.

FPGAs originally began as competitors to CPLDs to implement glue logic for printed circuit boards. As their size, capabilities, and speed increased, FPGAs took over additional functions to the point where some are now marketed as full systems on chips (SoCs). Particularly with the introduction of dedicated multipliers into FPGA architectures in the late 1990s, applications which had traditionally been the sole reserve of digital signal processor hardware (DSPs) began to incorporate FPGAs instead.

The evolution of FPGAs has motivated an increase in the use of these devices, whose architecture allows the development of hardware solutions optimized for complex tasks, such as 3D MRI image segmentation, 3D discrete wavelet transform, tomographic image reconstruction, or PET/MRI systems. The developed solutions can perform intensive computation tasks with parallel processing, are dynamically reprogrammable, and have a low cost, all while meeting the hard real-time requirements associated with medical imaging.

Another trend in the use of FPGAs is hardware acceleration, where one can use the FPGA to accelerate certain parts of an algorithm and share part of the computation between the FPGA and a generic processor. The search engine Bing is noted for adopting FPGA acceleration for its search algorithm in 2014. , FPGAs are seeing increased use as AI accelerators including Microsoft's so-termed "Project Catapult" and for accelerating artificial neural networks for machine learning applications.

Traditionally, FPGAs have been reserved for specific vertical applications where the volume of production is small. For these low-volume applications, the premium that companies pay in hardware cost per unit for a programmable chip is more affordable than the development resources spent on creating an ASIC. , new cost and performance dynamics have broadened the range of viable applications.

Where personal computer peripherals exist in niche markets or are struggling to make inroads into a mass market (sometimes despite heavy promotion), it can be more cost-effective to utilise FPGAs for small production runs (e.g. 1,000 units). Examples include exotic products such as e.g. ArVid, a VHS tape archiver (only some versions of which were FPGA-based) and Gigabyte Technology's i-RAM budget pseudo-SSD drive, which used a Xilinx FPGA. Often a custom-made chip would be cheaper if made in larger quantities, but FPGAs may be chosen to quickly bring a product to market. Again, to the extent the availability of lower-cost FPGAs is increasing, it can become justifiable to include them even in larger production runs.

Other uses for FPGAs include:

 * Space (with radiation hardening )
 * Hardware security modules
 * High-speed financial transactions
 * Retrocomputing (e.g. the MARS and MiSTer FPGA projects)

Usage by United States Military
FPGAs play a crucial role in modern military communications, especially in systems like the Joint Tactical Radio System (JTRS) and in devices from companies such as Thales and Harris Corporation. Their flexibility and programmability make them ideal for military communications, offering customizable and secure signal processing. In the JTRS, used by the US military, FPGAs provide adaptability and real-time processing, crucial for meeting various communication standards and encryption methods. Thales leverages FPGA technology in designing communication devices that fulfill the rigorous demands of military use, including rapid reconfiguration and robust security. Similarly, Harris Corporation, now part of L3Harris Technologies, incorporates FPGAs in its defense and commercial communication solutions, enhancing signal processing and system security.

L3Harris

 * 1) Rapidly Adaptable Standards-compliant Radio (RASOR): A Modular Open System Approach (MOSA) solution supporting over 50 data links and waveforms.
 * 2) ASPEN Technology Platform: Consists of proven hardware modules with programmable software and FPGA options for advanced, configurable data links.
 * 3) AN/PRC-117F(C) Radios: Supported the U.S. Air Force Electronic Systems Command, strengthening Harris' role as a full-spectrum communications system supplier.

Thales

 * 1) SYNAPS Radio Family: Utilizes Software Defined Radio (SDR) technology, typically involving FPGA for enhanced flexibility and performance.
 * 2) AN/PRC-148 (Multiband Inter/Intra Team Radio - MBITR): A small-form-factor, multiband, multi-mode SDR used in Afghanistan and Iraq.
 * 3) JTRS Cluster 2 Handheld Radio: Currently in development, recently completed a successful early operational assessment.

Security
FPGAs have both advantages and disadvantages as compared to ASICs or secure microprocessors, concerning hardware security. FPGAs' flexibility makes malicious modifications during fabrication a lower risk. Previously, for many FPGAs, the design bitstream was exposed while the FPGA loads it from external memory (typically on every power-on). All major FPGA vendors now offer a spectrum of security solutions to designers such as bitstream encryption and authentication. For example, Altera and Xilinx offer AES encryption (up to 256-bit) for bitstreams stored in an external flash memory. Physical unclonable functions (PUFs) are integrated circuits that have their own unique signatures, due to processing, and can also be used to secure FPGAs while taking up very little hardware space.

FPGAs that store their configuration internally in nonvolatile flash memory, such as Microsemi's ProAsic 3 or Lattice's XP2 programmable devices, do not expose the bitstream and do not need encryption. In addition, flash memory for a lookup table provides single event upset protection for space applications. Customers wanting a higher guarantee of tamper resistance can use write-once, antifuse FPGAs from vendors such as Microsemi.

With its Stratix 10 FPGAs and SoCs, Altera introduced a Secure Device Manager and physical unclonable functions to provide high levels of protection against physical attacks.

In 2012 researchers Sergei Skorobogatov and Christopher Woods demonstrated that some FPGAs can be vulnerable to hostile intent. They discovered a critical backdoor vulnerability had been manufactured in silicon as part of the Actel/Microsemi ProAsic 3 making it vulnerable on many levels such as reprogramming crypto and access keys, accessing unencrypted bitstream, modifying low-level silicon features, and extracting configuration data.

In 2020 a critical vulnerability (named "Starbleed") was discovered in all Xilinx 7series FPGAs that rendered bitstream encryption useless. There is no workaround. Xilinx did not produce a hardware revision. Ultrascale and later devices, already on the market at the time, were not affected.

Similar technologies
Historically, FPGAs have been slower, less energy efficient and generally achieved less functionality than their fixed ASIC counterparts. A study from 2006 showed that designs implemented on FPGAs need on average 40 times as much area, draw 12 times as much dynamic power, and run at one third the speed of corresponding ASIC implementations.

Advantages of FPGAs include the ability to re-program when already deployed (i.e. "in the field") to fix bugs, and often include shorter time to market and lower non-recurring engineering costs. Vendors can also take a middle road via FPGA prototyping: developing their prototype hardware on FPGAs, but manufacture their final version as an ASIC so that it can no longer be modified after the design has been committed. This is often also the case with new processor designs. Some FPGAs have the capability of partial re-configuration that lets one portion of the device be re-programmed while other portions continue running.

The primary differences between complex programmable logic devices (CPLDs) and FPGAs are architectural. A CPLD has a comparatively restrictive structure consisting of one or more programmable sum-of-products logic arrays feeding a relatively small number of clocked registers. As a result, CPLDs are less flexible but have the advantage of more predictable timing delays and FPGA architectures, on the other hand, are dominated by interconnect. This makes them far more flexible (in terms of the range of designs that are practical for implementation on them) but also far more complex to design for, or at least requiring more complex electronic design automation (EDA) software. In practice, the distinction between FPGAs and CPLDs is often one of size as FPGAs are usually much larger in terms of resources than CPLDs. Typically only FPGAs contain more complex embedded functions such as adders, multipliers, memory, and serializer/deserializers. Another common distinction is that CPLDs contain embedded flash memory to store their configuration while FPGAs usually require external non-volatile memory (but not always). When a design requires simple instant-on (logic is already configured at power-up) CPLDs are generally preferred. For most other applications FPGAs are generally preferred. Sometimes both CPLDs and FPGAs are used in a single system design. In those designs, CPLDs generally perform glue logic functions and are responsible for "booting" the FPGA as well as controlling reset and boot sequence of the complete circuit board. Therefore, depending on the application it may be judicious to use both FPGAs and CPLDs in a single design.

title: Field-programmable gate array


text:
Free-running sleep

Free-running sleep is a rare sleep pattern whereby the sleep schedule of a person shifts later every day. It occurs as the sleep disorder non-24-hour sleep–wake disorder or artificially as part of experiments used in the study of circadian and other rhythms in biology. Study subjects are shielded from all time cues, often by a constant light protocol, by a constant dark protocol or by the use of light/dark conditions to which the organism cannot entrain such as the ultrashort protocol of one hour dark and two hours light. Also, limited amounts of food may be made available at short intervals so as to avoid entrainment to mealtimes. Subjects are thus forced to live by their internal circadian "clocks".

Background
The individual's or animal's circadian phase can be known only by the monitoring of some kind of output of the circadian system, the internal "body clock". The researcher can precisely determine, for example, the daily cycles of gene activity, body temperature, blood pressure, hormone secretion and/or sleep and activity/alertness. Alertness in humans can be determined by many kinds of verbal and non-verbal tests, whereas alertness in animals can usually be assessed by observing physical activity (for example, of wheel-running in rodents).

When animals or people free-run, experiments can be done to see what sort of signals, known as zeitgebers, are effective in entrainment. Also, much work has been done to see how long or short a circadian cycle can be entrained to various organisms. For example, some animals can be entrained to a 22-hour day, but they can not be entrained to a 20-hour day. In recent studies funded by the U.S. space industry, it has been shown that most humans can be entrained to a 23.5-hour day and to a 24.65-hour day.

The effect of unintended time cues is called masking and can totally confound experimental results. Examples of masking are morning rush traffic audible to the subjects, or researchers or maintenance staff visiting subjects on a regular schedule.

In humans
Non-24-hour sleep–wake disorder, also referred to as free-running disorder (FRD) or Non-24, is one of the circadian rhythm sleep disorders in humans. It affects more than half of people who are totally blind and a smaller number of sighted individuals.

Among blind people, the cause is the inability to register, and therefore to entrain to, light cues. The many blind people who do entrain to the 24-hour light/dark cycle have eyes with functioning retinas including operative non-visual light-sensitive cells, ipRGCs. These ganglion cells, which contain melanopsin, convey their signals to the "circadian clock" via the retinohypothalamic tract (branching off from the optic nerve), linking the retina to the pineal gland.

Among sighted individuals, Non-24 usually first appears in the teens or early twenties. As with delayed sleep phase disorder (DSPS or DSPD), in the absence of neurological damage due to trauma or stroke, cases almost never appear after the age of 30. Non-24 affects more sighted males than sighted females. A quarter of sighted individuals with Non-24 also have an associated psychiatric condition, and a quarter of them have previously shown symptoms of DSPS.

title: Free-running sleep