False memory

From Wikipedia, the free encyclopedia

 

A false memory is a psychological phenomenon where a person recalls something that did not happen. There is a growing body of evidence that false memories are created whenever memories are recalled.

 

False memory is often considered regarding childhood sexual abuse.^[5][6][7][8] This phenomenon was initially investigated by psychological pioneers Pierre Janet and Sigmund Freud. Freud wrote The Aetiology of Hysteria, where he discussed repressed memories of childhood sexual trauma in their relation to hysteria.^[9] Elizabeth Loftus has, since her debuting research project in 1974,^[10] been a lead researcher in memory recovery and false memories.

False memory syndrome recognizes false memory as a prevalent part of one's life in which it affects the person's mentality and day-to-day life. False memory syndrome differs from false memory in that the syndrome is heavily influential in the orientation of a person's life, while false memory can occur without this significant effect. The syndrome takes effect because the person believes the influential memory to be true.^[11] However, its research is controversial and the syndrome is excluded from identification as a mental disorder and, therefore, is also excluded from the Diagnostic and Statistical Manual of Mental Disorders. False memory is an important part of psychological research because of the ties it has to a large number of mental disorders, such as PTSD.^[12]

Manipulation of memory recall through language

In 1974, Elizabeth Loftus and John Palmer conducted a study to investigate the effects of language on the development of false memory. The experiment involved two separate studies.

In the first test, 45 participants were randomly assigned to watch different videos of a car accident, in which separate videos had shown collisions at 20 miles per hour, 30 miles per hour, and 40 miles per hour. Afterwards, participants filled out a survey. The survey asked the question, "About how fast were the cars going when they smashed into each other?" The question always asked the same thing, except the verb used to describe the collision varied. Rather than "smashed", other verbs used included "bumped", "collided", "hit", or "contacted". Participants estimated collisions of all speeds to average between 35 miles per hour to just below 40 miles per hour. If actual speed were the main factor in estimate, it could be assumed that participants would have lower estimates for lower speed collisions. Instead, the word being used to describe the collision seemed to better predict the estimate in speed rather than the speed itself.^[10]

The second experiment also showed participants videos of a car accident, but the critical thing was the verbiage of the follow-up questionnaire. 150 participants were randomly assigned to three conditions. Those in the first condition were asked the same question as the first study using the verb "smashed". The second group was asked the same question as the first study, replacing "smashed" with "hit". The final group was not asked about the speed of the crashed cars. The researchers then asked the participants if they had seen any broken glass, knowing that there was no broken glass in the video. The responses to this question had shown that the difference between whether broken glass was recalled or not heavily depended on the verb used. A larger sum of participants in the "smashed" group declared that there was broken glass.

In this study, the first point brought up in discussion is that the words used to phrase a question can heavily influence the response given.^[10] Second, the study indicates that the phrasing of a question can give expectations to previously ignored details, and therefore, a misconstruction of our memory recall. This indication supports false memory as an existing phenomenon.

Article adjustment on eyewitness report

Loftus' meta-analysis on language manipulation studies suggested the phenomenon effects taking hold on the recall process and products of the human memory. Even the smallest adjustment in a question, such as the article preceding the supposed memory, could alter the responses. For example, having asked someone if they'd seen "the" stop sign, rather than "a" stop sign, provided the respondent with a presupposition that there was a stop sign in the scene. This presupposition increased the number of people responding that they had indeed seen the stop sign.

Adjective implications on eyewitness report

Select adjectives can imply characteristics about an object. Including said adjectives in a prompt can alter participant responses. Harris' 1973^{[citation needed]} study looks at the differences in answers on the height of a basketball player. Respondents were randomly assigned to have either answered to, "How tall was the basketball player?" or "How short was the basketball player?" Rather than asking participants simply for the height of the basketball player, they used adjectives that had an implication for the numerical results. The difference in height averages that were predicted was 10 inches (250 mm). The adjective provided in a sentence can cause a respondent to exaggerate.

Word lists

One can trigger false memories by presenting subjects a continuous list of words. When subjects were presented with a second type of the list and asked if the words had appeared on the previous list, they find that the subjects did not recognize the list correctly. When the words on the two lists were semantically related to each other (e.g. sleep/bed), it was more likely that the subjects did not remember the first list correctly and created false memories (Anisfeld & Knapp)^[13]

Staged naturalistic events

Subjects were invited in an office and were told to wait there. After this they had to recall the inventory of the visited office. Subjects recognized objects consistent with the “office schema” although they did not appear in the office. (Brewer & Treyens, 1981)^[13]

Response to meta-analysis

It has been argued that Loftus and Palmer did not control for outside factors coming from individual participants, such as participants' emotions or alcohol intake, along with many other factors. Despite criticisms such as this, this particular study is extremely relevant to legal cases regarding false memory. The Loftus and Palmer automobile study allowed for the Devlin Committee to create the Devlin Report, which suggested that eyewitness testimony is not reliable standing on its own.

Reliability of memory recall

Presuppositions

Presuppositions are an implication through chosen language. If a person is asked, "What shade of blue was the wallet?" The questioner is, in translation, saying, "The wallet was blue. What shade was it?" The question's phrasing provides the respondent with a supposed "fact". This presupposition provides two separate effects: true effect and false effect.

True effect says that the object implied to have existed does exist. With that, the respondent's recall is strengthened, more readily available, and easier to extrapolate from. In true effect, presuppositions make a detail more readily recalled. For example, it would be less likely that a respondent would remember a wallet was blue if the prompt did not say that it was blue. False effect is that the object implied to have existed never was present. Despite this, the respondent is convinced otherwise and allows it to manipulate their memory. It can also alter responses to later questions to keep consistency. Regardless of the effect being true or false, the respondent is attempting to conform to the supplied information, because they assume it to be true.

Construction hypothesis

Construction hypothesis has major implications for explanations on the malleability of memory. Upon asking a respondent a question that provides a presupposition, the respondent will provide a recall in accordance with the presupposition (if accepted to exist in the first place). The respondent will recall the object or detail. The construction hypothesis says that if a true piece of information being provided can alter a respondent's answer, then so can a false piece of information.^[14]

Skeleton theory

Loftus developed the skeleton theory after having run an experiment involving 150 subjects from the University of Washington.^{[citation needed]} The skeleton theory explains the idea of how a memory is recalled, which is split into two categories: the acquisition processes and the retrieval processes.

The acquisition processes are in three separate steps. First, upon the original encounter, the observer selects a stimulus to focus on. The information that the observer can focus on compared to the information in the situation is very small. In other words, a lot is going on around us and we only pick up on a small portion. Therefore, the observer must make a selection on the focal point. Second, our visual perception must be translated into statements and descriptions. The statements represent a collection of concepts and objects; they are the link between the event occurrence and the recall. Third, the perceptions are subject to any "external" information being provided before or after the interpretation. This subsequent set of information can alter recall.

The retrieval processes come in two steps. First, the memory and imagery is regenerated. This perception is subject to what foci the observer has selected, along with the information provided before or after the observation. Second, the linking is initiated by a statement response, "painting a picture" to make sense of what was observed. This retrieval process results in either an accurate memory or a false memory.

Relational processing

Memory retrieval has been associated with the brain's relational processing. In associating two events (in reference to false memory, say tying a testimony to a prior event), there are verbatim and gist representations. Verbatim matches to the individual occurrences (i.e. I do not like dogs because when I was five a chihuahua bit me) and gist matches to general inferences (i.e. I do not like dogs because they are mean). Keeping in line with the fuzzy-trace theory, which suggests false memories are stored in gist representations (which retrieves both true and false recall), Storbeck & Clore (2005) wanted to see how change in mood affected the retrieval of false memories. After using the measure of a word association tool called the Deese–Roediger–McDermott paradigm, the subjects' moods were manipulated. Moods were either oriented towards being more positive, more negative, or were left unmanipulated. Findings suggested that a more negative mood made critical details, stored in gist representation, less accessible.^[15] This would imply that false memories are less likely to occur when a subject was in a worse mood.

Therapy-induced memory recovery

Recovery strategies

Memories recovered through therapy have become more difficult to distinguish between simply being repressed or having existed in the first place. Therapists have used strategies such as hypnotherapy, repeated questioning, and bibliotherapy. These strategies may provoke the recovery of nonexistent events or inaccurate memories.^{[7][16][17][18]} A recent report indicates that similar strategies may have produced false memories in several therapies in the century before the modern controversy on the topic which took place in the 1980s and 1990s.^[19] In The Myth of Repressed Memory: False memories and allegations of Sexual Abuse, Elizabeth Loftus writes about how easy it is for her as a therapist to mold people's memories, or prompt them to recall a nonexistent broken glass.^[20]

For her there are different possibilities to create false therapy-induced memory. One is the unintentional suggestions of therapists. For example, a therapist might tell their client that, on the basis of their symptoms, it is quite likely that they had been abused as a child. Once this "diagnosis" is made, the therapist sometimes urges the patient to pursue the recalcitrant memories. It is a problem resulting from the fact that people create their own social reality with external information.^[21]

Laurence and Perry conducted a study testing the ability to induce memory recall through hypnosis. Subjects were put into a hypnotic state and later woken up. Observers suggested that the subjects were woken up by a loud noise. Nearly half of the subjects being tested concluded that this was true, despite it being false. Though, by therapeutically altering the subject's state, they may have been led to believe that what they were being told was true.^[22] Because of this, the respondent has a false recall.

A 1989 study focusing on hypnotizability and false memory separated accurate and inaccurate memories recalled. In open-ended question formation, 11.5% of subjects recalled the false event suggested by observers. In a multiple-choice format, no participants claimed the false event had happened. This result led to the conclusion that hypnotic suggestions produce shifts in focus, awareness, and attention. Despite this, subjects do not mix fantasy up with reality.^[9]

Therapy-induced memory recovery is a prevalent subcategory of memory recall prompting discussion of false memory syndrome. This phenomenon is loosely defined, and not a part of the DSM. However, the syndrome suggests that false memory can be declared a syndrome when recall of a false or inaccurate memory takes great effect on your life. This false memory can completely alter the orientation of your personality and lifestyle.^[9]

The "lost-in-the-mall" technique is another recovery strategy. This is essentially a repeated suggestion pattern. The person whose memory is to be recovered is persistently said to have gone through an experience even if it may have not happened. This strategy can cause the person to recall the event as having occurred, despite its falsehood.^[23]

Legal cases

Therapy-induced memory recovery has made frequent appearances in legal cases, particularly those regarding sexual abuse.^{[citation needed]} Therapists can often aid in creating a false memory in a victim's mind, intentionally or unintentionally. They will associate a patient's behavior with the fact that they have been a victim of sexual abuse, thus helping the memory occur. They use memory enhancement techniques such as hypnosis dream analysis to extract memories of sexual abuse from victims. According to the FMSF (False Memory Syndrome Foundation), these memories are false and are produced in the very act of searching for and employing them in a life narrative. In Ramona v. Isabella,^{[citation needed]} two therapists wrongly prompted a recall that their patient, Holly Ramona, had been sexually abused by her father. It was suggested that the therapist, Isabella, had implanted the memory in Ramona after use of the hypnotic drug sodium amytal. After a nearly unanimous decision, Isabella had been declared negligent towards Holly Ramona. This 1994 legal issue played a massive role in shedding light on the possibility of false memories' occurrences.

In another legal case where false memories were used, they helped a man to be acquitted of his charges. Joseph Pacely had been accused of breaking into a woman's home with the intent to sexually assault her. The woman had given her description of the assailant to police shortly after the crime had happened. During the trial, memory researcher Elizabeth Loftus testified that memory is fallible and there were many emotions that played a part in the woman's description given to police. Loftus has published many studies consistent with her testimony.^[14][24][25] These studies suggest that memories can easily be changed around and sometimes eyewitness testimonies are not as reliable as many believe.

Another notable case of Maxine Berry, Maxine grew up in the custody of her mother, who opposed the father having contact with her (Berry & Berry, 2001). When the father expressed his desire to attend his daughter’s high school graduation, the mother enrolled Maxine in therapy, ostensibly to deal with the stress of seeing her father. The therapist pressed Maxine to recover memories of sex abuse by her father. Maxine broke down under the pressure and had to be psychiatrically hospitalized. She had her tubes tied, so she would not have children and repeat the cycle of abuse. With the support of her husband and primary care physician, Maxine eventually realized that her memories were false and filed a suit for malpractice. The suit brought to light the mother's manipulation of mental health professionals to convince Maxine that she had been sexually abused by her father. In February 1997 Jennifer Gerrietts, Argus Leader, South Dakota Maxine Berry, sued her therapists and clinic that treated her from 1992-1995 and, she says, made her falsely believe she had been sexually and physically abused as a child when no such abuse ever occurred. The lawsuit, filed in February 1997 in Minnehaha Co. Circuit Court South Dakota, states that therapist Lynda O'Connor-Davis had an improper relationship with Berry, both during and after her treatment. The suit also names psychologist Vail Williams, psychiatrist Dr. William Fuller and Charter Hospital and Charter Counseling Center as defendants. Berry and her husband settled out of court for a undisclosed amount of money.

Although there have been many legal cases in which false memory appears to have been a factor, this does not ease the process of distinguishing between false memory and real recall. Sound therapeutic strategy can help this differentiation, by either avoiding known controversial strategies or to disclosing controversy to a subject.^[7][9][26] In each case, the recovered memory therapy was declared inadmissible and not scientifically sound. The fact that recovered memories cannot necessarily distinguish between true and false meant the quality of evidence was weakened and the cases concluded against the therapists. The objection to therapeutic recovery techniques has been argued by comparing the ethics of memory elimination techniques such as electroconvulsive therapy.^[18]

Harold Merskey published a paper on the ethical issues of recovered-memory therapy.^[26] He suggests that if a patient had pre-existing severe issues in their life, it is likely that "deterioration" will occur to a relatively severe extent upon memory recall. This deterioration is a physical parallel to the emotional trauma being surfaced. There may be tears, writhing, or many other forms of physical disturbance. The occurrence of physical deterioration in memory recall coming from a patient with relatively minor issues prior to therapy could be an indication of the recalled memory's potential falsehood.^[26]

In children

If a child experienced abuse, it is not typical for them to disclose the details of the event when confronted in an open-ended manner.^[27] Trying to indirectly prompt a memory recall can lead to the conflict of source attribution, as if repeatedly questioned the child might try to recall a memory to satisfy a question. The stress being put on the child can make recovering an accurate memory more difficult.^[5] Some people hypothesise that as the child continuously attempts to remember a memory, they are building a larger file of sources that the memory could be derived from, potentially including sources other than genuine memories. Children that have never been abused but undergo similar response-eliciting techniques can disclose events that never occurred.^[27] If one concludes that the child's recalled memory is false, it is a type I error. Assuming the child did not recall an existing memory, it is a type II error.

One of children's most notable setbacks in memory recall is source misattribution. Source misattribution is the flaw in deciphering between potential origins of a memory. The source could come from an actual occurring perception, or it can come from an induced and imagined event. Younger children, preschoolers in particular, find it more difficult to discriminate between the two.^[28] Lindsay & Johnson (1987) concluded that even children approaching adolescence struggle with this, as well as recalling an existent memory as a witness. Children are significantly more likely to confuse a source between being invented or existent.^[29]

Commonly held false memories

The Bologna station clock, subject of a collective false memory

Similar false memories are sometimes shared by multiple people.^[30][31] One such false memory is that the name of the Berenstain Bears was once spelled Berenstein.^[32][33] Another example consists of false memories of a 1990s movie titled Shazaam starring comedian Sinbad as a genie, which may be a conflation of memories of the comedian wearing a genie costume during a TV presentation of Sinbad the Sailor movies in 1994,^[30][34] and a similarly named 1996 film Kazaam featuring a genie played by Shaquille O'Neal.^[30]

A 2010 study examined people who were familiar with the clock at Bologna Centrale railway station, which had been damaged in the Bologna massacre bombing in August 1980. In the study, 92% falsely remembered that the clock had remained stopped since the bombing; in fact, the clock was repaired shortly after the attack but was again stopped 16 years later as a symbolic commemoration of it.^[31]

In 2010 the phenomenon of collective false memory was dubbed the "Mandela effect" by self-described "paranormal consultant" Fiona Broome, in reference to a false memory she reports, of the death of South African leader Nelson Mandela in the 1980s (when he was in fact still alive), which she claims is shared by "perhaps thousands" of other people.^[35] Broome has speculated about alternate realities as an explanation, but most commentators suggest that these are instead examples of false memories shaped by similar factors affecting multiple people, such as social reinforcement of incorrect memories,^[41][42] or false news reports and misleading photographs influencing the formation of memories based on them.

Researchers watch video images people are seeing, decoded from their fMRI brain scans in near-real-time

Advanced deep-learning "mind-reading" system even interprets image meaning, providing high-level categories (face, bird, etc.) 

 

October 27, 2017
Original link:  http://www.kurzweilai.net/researchers-watch-video-images-people-are-seeing-decoded-from-their-fmri-brain-scans-in-near-real-time

 

Purdue Engineering researchers have developed a system that can show what people are seeing in real-world videos, decoded from their fMRI brain scans — an advanced new form of  “mind-reading” technology that could lead to new insights in brain function and to advanced AI systems.

The research builds on previous pioneering research at UC Berkeley’s Gallant Lab, which created a computer program in 2011 that translated fMRI brain-wave patterns into images that loosely mirrored a series of images being viewed.

The new system also decodes moving images that subjects see in videos and does it in near-real-time. But the researchers were also able to determine the subjects’ interpretations of the images they saw — for example, interpreting an image as a person or thing — and could even reconstruct a version of the original images that the subjects saw.

Deep-learning AI system for watching what the brain sees

Watching in near-real-time what the brain sees. Visual information generated by a video (a) is processed in a cascade from the retina through the thalamus (LGN area) to several levels of the visual cortex (b), detected from fMRI activity patterns (c) and recorded. A powerful deep-learning technique (d) then models this detected cortical visual processing. Called a convolutional neural network (CNN), this model transforms every video frame into multiple layers of features, ranging from orientations and colors (the first visual layer) to high-level object categories (face, bird, etc.) in semantic (meaning) space (the eighth layer). The trained CNN model can then be used to reverse this process, reconstructing the original videos — even creating new videos that the CNN model had never watched. (credit: Haiguang Wen et al./Cerebral Cortex)

The researchers acquired 11.5 hours of fMRI data from each of three women subjects watching 972 video clips, including clips showing people or animals in action and nature scenes.

To decode the  fMRI images, the research pioneered the use of a deep-learning technique called a convolutional neural network (CNN). The trained CNN model was able to accurately decode the fMRI blood-flow data to identify specific image categories (such as the face, bird, ship, and scene examples in the above figure). The researchers could compare (in near-real-time) these viewed video images side-by-side with the computer’s visual interpretation of what the person’s brain saw.

Reconstruction of a dynamic visual experience in the experiment. The top row shows the example movie frames seen by one subject; the bottom row shows the reconstruction of those frames based on the subject’s cortical fMRI responses to the movie. (credit: Haiguang Wen et al./ Cerebral Cortex)

The researchers were also able to figure out how certain locations in the visual cortex were associated with specific information a person was seeing.

Decoding how the visual cortex works

CNNs have been used to recognize faces and objects, and to study how the brain processes static images and other visual stimuli. But the new findings represent the first time CNNs have been used to see how the brain processes videos of natural scenes. This is “a step toward decoding the brain while people are trying to make sense of complex and dynamic visual surroundings,” said doctoral student Haiguang Wen.

Wen was first author of a paper describing the research, appearing online Oct. 20 in the journal Cerebral Cortex.

“Neuroscience is trying to map which parts of the brain are responsible for specific functionality,” Wen explained. “This is a landmark goal of neuroscience. I think what we report in this paper moves us closer to achieving that goal. Using our technique, you may visualize the specific information represented by any brain location, and screen through all the locations in the brain’s visual cortex. By doing that, you can see how the brain divides a visual scene into pieces, and re-assembles the pieces into a full understanding of the visual scene.”

The researchers also were able to use models trained with data from one human subject to predict and decode the brain activity of a different human subject, a process called “cross-subject encoding and decoding.” This finding is important because it demonstrates the potential for broad applications of such models to study brain function, including people with visual deficits.

The research has been funded by the National Institute of Mental Health. The work is affiliated with the Purdue Institute for Integrative Neuroscience. Data reported in this paper are also publicly available at the Laboratory of Integrated Brain Imaging website.

UPDATE Oct. 28, 2017 — Additional figure added, comparing the original images and those reconstructed from the subject’s cortical fMRI responses to the movie; subhead revised to clarify the CNN function. Two references also added.

---

Abstract of Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision.

References:

• Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu. Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision. Cerebral Cortex, 2017; 1 DOI: 10.1093/cercor/bhx268
• Wen, H., Shi, J., Zhang, Y., Lu, K., Cao, J., Liu, Z. (2017). Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests. Purdue University Research Repository. doi:10.4231/R7SF2TCW (open access)
• The data and source codes related to this study (open access)

Confabulation

From Wikipedia, the free encyclopedia

 

Confabulation
Classification and external resources
Specialty	Psychiatry

In psychiatry, confabulation (verb: confabulate) is a disturbance of memory, defined as the production of fabricated, distorted, or misinterpreted memories about oneself or the world, without the conscious intention to deceive. People who confabulate present incorrect memories ranging from "subtle alterations to bizarre fabrications", and are generally very confident about their recollections, despite contradictory evidence.

Description

Confabulation is distinguished from lying as there is no intent to deceive and the person is unaware the information is false.^[4] Although individuals can present blatantly false information, confabulation can also seem to be coherent, internally consistent, and relatively normal.^[4]

Most known cases of confabulation are symptomatic of brain damage or dementias, such as aneurysm, Alzheimer's disease, or Wernicke–Korsakoff syndrome (a common manifestation of thiamine deficiency caused by alcoholism).^[5] Additionally confabulation often occurs in people who are suffering from anticholinergic toxidrome when interrogated about bizarre or irrational behaviour.

Confabulated memories of all types most often occur in autobiographical memory and are indicative of a complicated and intricate process that can be led astray at any point during encoding, storage, or recall of a memory.^[3] This type of confabulation is commonly seen in Korsakoff's syndrome.^[6]

Distinctions

Two types of confabulation are often distinguished:

• Provoked (momentary, or secondary) confabulations represent a normal response to a faulty memory, are common in both amnesia and dementia,^[7] and can become apparent during memory tests.^[8]
• Spontaneous (or primary) confabulations do not occur in response to a cue^[8] and seem to be involuntary.^[9] They are relatively rare, more common in cases of dementia, and may result from the interaction between frontal lobe pathology and organic amnesia.^[7]

Another distinction is that between:^[9]

• Verbal confabulations, spoken false memories are more common, and
• Behavioral confabulations, occur when an individual acts on their false memories.

Signs and symptoms

Confabulation is associated with several characteristics:

1. Typically verbal statements but can also be non-verbal gestures or actions.
2. Can include autobiographical and non-personal information, such as historical facts, fairy-tales, or other aspects of semantic memory.
3. The account can be fantastic or coherent.
4. Both the premise and the details of the account can be false.
5. The account is usually drawn from the patient's memory of actual experiences, including past and current thoughts.
6. The patient is unaware of the accounts' distortions or inappropriateness, and is not concerned when errors are pointed out.
7. There is no hidden motivation behind the account.
8. The patient's personality structure may play a role in his/her readiness to confabulate.^[4]

Theories

Theories of confabulation range in emphasis.^[10] Some theories propose that confabulations represent a way for memory-disabled people to maintain their self-identity.^[8] Other theories use neurocognitive links to explain the process of confabulation.^[11] Still other theories frame confabulation around the more familiar concept of delusion.^[12] Other researchers frame confabulation within the fuzzy-trace theory.^[13] Finally, some researchers call for theories that rely less on neurocognitive explanations and more on epistemic accounts.^[14] Theories of confabulation need to take into account the commonplace confabulations in dreaming, such as disjunctive cognition and interobject.^[15][16]

Neuropsychological theories

The most popular theories of confabulation come from the field of neuropsychology or cognitive neuroscience.^[11] Research suggests that confabulation is associated with dysfunction of cognitive processes that control the retrieval from long-term memory. Frontal lobe damage often disrupts this process, preventing the retrieval of information and the evaluation of its output.^[17][18] Furthermore, researchers argue that confabulation is a disorder resulting from failed "reality monitoring/source monitoring" (i.e. deciding whether a memory is based on an actual event or whether it is imagined).^[19] Some neuropsychologists suggest that errors in retrieval of information from long-term memory that are made by normal subjects involve different components of control processes than errors made by confabulators.^[20] Kraepelin distinguished two subtypes of confabulation, one of which he called simple confabulation, caused partly by errors in the temporal ordering of real events. The other variety he called fantastic confabulation, which was bizarre and patently impossible statements not rooted in true memory. Simple confabulation may result from damage to memory systems in the medial temporal lobe. Fantastic confabulations reveal a dysfunction of the Supervisory System,^[21] which is believed to be a function of the frontal cortex.

Self-identity theory

Some argue confabulations have a self-serving, emotional component in those with memory deficits that aids to maintain a coherent self-concept.^[8] In other words, people who confabulate are motivated to do so, because they have gaps in their memory that they want to fill in and cover up.

Temporality theory

Support for the temporality account suggests that confabulations occur when an individual is unable to place events properly in time.^[8] Thus, an individual might correctly state an action he/she performed, but say he/she did it yesterday, when he/she did it weeks ago. In the Memory, Consciousness, and Temporality Theory, confabulation occurs because of a deficit in temporal consciousness or awareness.^[22]

Monitoring theory

Along a similar notion are the theories of reality and source monitoring theories.^[9] In these theories, confabulation occurs when individuals incorrectly attribute memories as reality, or incorrectly attribute memories to a certain source. Thus, an individual might claim an imagined event happened in reality, or that a friend told him/her about an event he/she actually heard about on television.

Strategic retrieval account theory

Supporters of the strategic retrieval account suggest that confabulations occur when an individual cannot actively monitor a memory for truthfulness after its retrieval.^[9] An individual recalls a memory, but there is some deficit after recall that interferes with the person establishing its falseness.

Executive control theory

Still others propose that all types of false memories, including confabulation, fit into a general memory and executive function model.^[23] In 2007, a framework for confabulation was proposed that stated confabulation is the result of two things: Problems with executive control and problems with evaluation. In the executive control deficit, the incorrect memory is retrieved from the brain. In the evaluative deficit, the memory will be accepted as a truth due to an inability to distinguish a belief from an actual memory.^[8]

In the context of delusion theories

Recent models of confabulation have attempted to build upon the link between delusion and confabulation.^[12] More recently, a monitoring account for delusion, applied to confabulation, proposed both the inclusion of conscious and unconscious processing. The claim was that by encompassing the notion of both processes, spontaneous versus provoked confabulations could be better explained. In other words, there are two ways to confabulate. One is the unconscious, spontaneous way in which a memory goes through no logical, explanatory processing. The other is the conscious, provoked way in which a memory is recalled intentionally by the individual to explain something confusing or unusual.^[24]

Fuzzy-trace theory

Fuzzy-trace theory, or FTT, is a concept more commonly applied to the explanation of judgement decisions.^[13] According to this theory, memories are encoded generally (gist), as well as specifically (verbatim). Thus, a confabulation could result from recalling the incorrect verbatim memory or from being able to recall the gist portion, but not the verbatim portion, of a memory.

FTT uses a set of five principles to explain false-memory phenomena. Principle 1 suggests that subjects store verbatim information and gist information parallel to one another. Both forms of storage involve the surface content of an experience. Principle 2 shares factors of retrieval of gist and verbatim traces. Principle 3 is based on dual-opponent processes in false memory. Generally, gist retrieval supports false memory, while verbatim retrieval suppresses it. Developmental variability is the topic of Principle 4. As a child develops into an adult, there is obvious improvement in the acquisition, retention, and retrieval of both verbatim and gist memory. However, during late adulthood, there will be a decline in these abilities. Finally, Principle 5 explains that verbatim and gist processing cause vivid remembering. Fuzzy-trace Theory, governed by these 5 principles, has proved useful in explaining false memory and generating new predictions about it.^[25]

Epistemic theory

However, not all accounts are so embedded in the neurocognitive aspects of confabulation. Some attribute confabulation to epistemic accounts.^[14] In 2009, theories underlying the causation and mechanisms for confabulation were criticized for their focus on neural processes, which are somewhat unclear, as well as their emphasis on the negativity of false remembering. Researchers proposed that an epistemic account of confabulation would be more encompassing of both the advantages and disadvantages of the process.

Presentation

Associated neurological and psychological conditions

Confabulations are often symptoms of various syndromes and psychopathologies in the adult population including: Korsakoff's syndrome, Alzheimer's disease, schizophrenia, and traumatic brain injury.

Wernicke–Korsakoff syndrome is a neurological disorder typically characterized by years of chronic alcohol abuse and a nutritional thiamine deficiency.^[26] Confabulation is one salient symptom of this syndrome.^[27][28] A study on confabulation in Korsakoff’s patients found that they are subject to provoked confabulation when prompted with questions pertaining to episodic memory, not semantic memory, and when prompted with questions where the appropriate response would be "I don’t know."^[29] This suggests that confabulation in these patients is "domain-specific." Korsakoff’s patients who confabulate are more likely than healthy adults to falsely recognize distractor words, suggesting that false recognition is a "confabulatory behavior."

Alzheimer's disease is a condition with both neurological and psychological components. It is a form of dementia associated with severe frontal lobe dysfunction. Confabulation in individuals with Alzheimer's is often more spontaneous than it is in other conditions, especially in the advanced stages of the disease. Alzheimer's patients demonstrate comparable abilities to encode information as healthy elderly adults, suggesting that impairments in encoding are not associated with confabulation.^[30] However, as seen in Korsakoff's patients, confabulation in Alzheimer's patients is higher when prompted with questions investigating episodic memory. Researchers suggest this is due to damage in the posterior cortical regions of the brain, which is a symptom characteristic of Alzheimer's Disease.

Schizophrenia is a psychological disorder in which confabulation is sometimes observed. Although confabulation is usually coherent in its presentation, confabulations of schizophrenic patients are often delusional^[31] Researchers have noted that these patients tend to make up delusions on the spot which are often fantastic and become increasingly elaborate with questioning.^[32] Unlike patients with Korsakoff's and Alzheimer's, patients with schizophrenia are more likely to confabulate when prompted with questions regarding their semantic memories, as opposed to episodic memory prompting.^[33] In addition, confabulation does not appear to be related to any memory deficit in schizophrenic patients. This is contrary to most forms of confabulation. Also, confabulations made by schizophrenic patients often do not involve the creation of new information, but instead involve an attempt by the patient to reconstruct actual details of a past event.

Traumatic brain injury (TBI) can also result in confabulation. Research has shown that patients with damage to the inferior medial frontal lobe confabulate significantly more than patients with damage to the posterior area and healthy controls.^[34] This suggests that this region is key in producing confabulatory responses, and that memory deficit is important but not necessary in confabulation. Additionally, research suggests that confabulation can be seen in patients with frontal lobe syndrome, which involves an insult to the frontal lobe as a result of disease or traumatic brain injury (TBI). Finally, rupture of the anterior or posterior communicating artery, subarachnoid hemorrhage, and encephalitis are also possible causes of confabulation.^[17][38]

Location of brain lesions

Confabulation is believed to be a result of damage to the right frontal lobe of the brain.^[4] In particular, damage can be localized to the ventromedial frontal lobes and other structures fed by the anterior communicating artery (ACoA), including the basal forebrain, septum, fornix, cingulate gyrus, cingulum, anterior hypothalamus, and head of the caudate nucleus.^[39][40]

Developmental differences

While some recent literature has suggested that older adults may be more susceptible than their younger counterparts to have false memories, the majority of research on forced confabulation centers around children.^[41] Children are particularly susceptible to forced confabulations based on their high suggestibility.^[42][43] When forced to recall confabulated events, children are less likely to remember that they had previously confabulated these situations, and they are more likely than their adult counterparts to come to remember these confabulations as real events that transpired.^[44] Research suggests that this inability to distinguish between past confabulatory and real events is centered on developmental differences in source monitoring. Due to underdeveloped encoding and critical reasoning skills, children's ability to distinguish real memories from false memories may be impaired. It may also be that younger children lack the meta-memory processes required to remember confabulated versus non-confabulated events.^[45] Children's meta-memory processes may also be influenced by expectancies or biases, in that they believe that highly plausible false scenarios are not confabulated.^[46] However, when knowingly being tested for accuracy, children are more likely to respond, "I don’t know" at a rate comparable to adults for unanswerable questions than they are to confabulate.^[47][48] Ultimately, misinformation effects can be minimized by tailoring individual interviews to the specific developmental stage, often based on age, of the participant.^[49]

Provoked versus spontaneous confabulations

There is evidence to support different cognitive mechanisms for provoked and spontaneous confabulation.^[50] One study suggested that spontaneous confabulation may be a result of an amnesic patient’s inability to distinguish the chronological order of events in their memory. In contrast, provoked confabulation may be a compensatory mechanism, in which the patient tries to make up for their memory deficiency by attempting to demonstrate competency in recollection.

Confidence in false memories

Confabulation of events or situations may lead to an eventual acceptance of the confabulated information as true.^[51] For instance, people who knowingly lie about a situation may eventually come to believe that their lies are truthful with time.^[52] In an interview setting, people are more likely to confabulate in situations in which they are presented false information by another person, as opposed to when they self-generate these falsehoods.^[53] Further, people are more likely to accept false information as true when they are interviewed at a later time (after the event in question) than those who are interviewed immediately or soon after the event.^[54] Affirmative feedback for confabulated responses is also shown to increase the confabulator’s confidence in their response.^[55] For instance, in culprit identification, if a witness falsely identifies a member of a line-up, he will be more confident in his identification if the interviewer provides affirmative feedback. This effect of confirmatory feedback appears to last over time, as witnesses will even remember the confabulated information months later.^[56]

Among normal subjects

On rare occasions, confabulation can also be seen in normal subjects.^[20] It is currently unclear how completely healthy individuals produce confabulations. It is possible that these individuals are in the process of developing some type of organic condition that is causing their confabulation symptoms. It is not uncommon, however, for the general population to display some very mild symptoms of provoked confabulations. Subtle distortions and intrusions in memory are commonly produced by normal subjects when they remember something poorly.

Diagnosis and treatment

Spontaneous confabulations, due to their involuntary nature, cannot be manipulated in a laboratory setting.^[9] However, provoked confabulations can be researched in various theoretical contexts. The mechanisms found to underlie provoked confabulations can be applied to spontaneous confabulation mechanisms. The basic premise of researching confabulation comprises finding errors and distortions in memory tests of an individual.

Deese–Roediger–McDermott lists

Confabulations can be detected in the context of the Deese–Roediger–McDermott paradigm by using the Deese–Roediger–McDermott lists.^[57] Participants listen to audio recordings of several lists of words centered around a theme, known as the critical word. The participants are later asked to recall the words on their list. If the participant recalls the critical word, which was never explicitly stated in the list, it is considered a confabulation. Participants often have a false memory for the critical word.

Recognition tasks

Confabulations can also be researched by using continuous recognition tasks.^[9] These tasks are often used in conjunction with confidence ratings. Generally, in a recognition task, participants are rapidly presented with pictures. Some of these pictures are shown once; others are shown multiple times. Participants press a key if they have seen the picture previously. Following a period of time, participants repeat the task. More errors on the second task, versus the first, are indicative of confusion, representing false memories.

Free recall tasks

Confabulations can also be detected using a free recall task, such as a self-narrative task.^[9] Participants are asked to recall stories (semantic or autobiographical) that are highly familiar to them. The stories recalled are encoded for errors that could be classified as distortions in memory. Distortions could include falsifying true story elements or including details from a completely different story. Errors such as these would be indicative of confabulations.

Treatment

Treatment for confabulation is somewhat dependent on the cause or source, if identifiable. For example, treatment of Wernicke–Korsakoff syndrome involves large doses of vitamin B in order to reverse the thiamine deficiency.^[58] If there is no known physiological cause, more general cognitive techniques may be used to treat confabulation. A case study published in 2000 showed that Self-Monitoring Training (SMT)^[59] reduced delusional confabulations. Furthermore, improvements were maintained at a three-month follow-up and were found to generalize to everyday settings. Although this treatment seems promising, more rigorous research is necessary to determine the efficacy of SMT in the general confabulation population.

Research

Although significant gains have been made in the understanding of confabulation in recent years, there is still much to be learned. One group of researchers in particular has laid-out several important questions for future-study. They suggest more information is needed regarding the neural-systems that support the different cognitive processes necessary for normal source=monitoring. They also proposed the idea of developing a standard neuro-psychological test battery able to discriminate between the different types of confabulations. And there is a considerable amount of debate regarding the best approach to organizing and combining neuro-imaging, pharmacological, and cognitive/behavioral approaches to understand confabulation.^[60]

In a recent review article, another group of researchers contemplate issues concerning the distinctions between delusions and confabulation. They question whether delusions and confabulation should be considered distinct or overlapping disorders and, if overlapping, to what degree? They also discuss the role of unconscious processes in confabulation. Some researchers suggest that unconscious emotional and motivational processes are potentially just as important as cognitive and memory problems. Finally, they raise the question of where to draw the line between the pathological and the nonpathological. Delusion-like beliefs and confabulation-like fabrications are commonly seen in healthy individuals. What are the important differences between patients with similar etiology who do and do not confabulate? Since the line between pathological and nonpathological is likely blurry, should we take a more dimensional approach to confabulation? Research suggests that confabulation occurs along a continuum of implausibility, bizarreness, content, conviction, preoccupation, and distress, and impact on daily life.

Machine-Learning Models Capture Subtle Variations in Facial Expressions

TOPICS:  Artificial IntelligenceComputer ScienceMachine LearningMITRobotics

By Rob Matheson, MIT News Office July 31, 2018

Original link:  https://scitechdaily.com/machine-learning-models-capture-subtle-variations-in-facial-expressions/ 

 

MIT Media Lab researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do. The model better captures subtle facial expression variations to better gauge moods. By using extra training data, the model can also be adapted to an entirely new group of people, with the same efficacy.

Personalized machine-learning models capture subtle variations in facial expressions to better gauge how we feel.

MIT Media Lab researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do.

In the growing field of “affective computing,” robots and computers are being developed to analyze facial expressions, interpret our emotions, and respond accordingly. Applications include, for instance, monitoring an individual’s health and well-being, gauging student interest in classrooms, helping diagnose signs of certain diseases, and developing helpful robot companions.

A challenge, however, is people express emotions quite differently, depending on many factors. General differences can be seen among cultures, genders, and age groups. But other differences are even more fine-grained: The time of day, how much you slept, or even your level of familiarity with a conversation partner leads to subtle variations in the way you express, say, happiness or sadness in a given moment.

Human brains instinctively catch these deviations, but machines struggle. Deep-learning techniques were developed in recent years to help catch the subtleties, but they’re still not as accurate or as adaptable across different populations as they could be.

The Media Lab researchers have developed a machine-learning model that outperforms traditional systems in capturing these small facial expression variations, to better gauge mood while training on thousands of images of faces. Moreover, by using a little extra training data, the model can be adapted to an entirely new group of people, with the same efficacy. The aim is to improve existing affective-computing technologies.

“This is an unobtrusive way to monitor our moods,” says Oggi Rudovic, a Media Lab researcher and co-author on a paper describing the model, which was presented last week at the Conference on Machine Learning and Data Mining. “If you want robots with social intelligence, you have to make them intelligently and naturally respond to our moods and emotions, more like humans.”

Co-authors on the paper are: first author Michael Feffer, an undergraduate student in electrical engineering and computer science; and Rosalind Picard, a professor of media arts and sciences and founding director of the Affective Computing research group.

Personalized experts

Traditional affective-computing models use a “one-size-fits-all” concept. They train on one set of images depicting various facial expressions, optimizing features — such as how a lip curls when smiling — and mapping those general feature optimizations across an entire set of new images.

The researchers, instead, combined a technique, called “mixture of experts” (MoE), with model personalization techniques, which helped mine more fine-grained facial-expression data from individuals. This is the first time these two techniques have been combined for affective computing, Rudovic says.

In MoEs, a number of neural network models, called “experts,” are each trained to specialize in a separate processing task and produce one output. The researchers also incorporated a “gating network,” which calculates probabilities of which expert will best detect moods of unseen subjects. “Basically the network can discern between individuals and say, ‘This is the right expert for the given image,’” Feffer says.

For their model, the researchers personalized the MoEs by matching each expert to one of 18 individual video recordings in the RECOLA database, a public database of people conversing on a video-chat platform designed for affective-computing applications. They trained the model using nine subjects and evaluated them on the other nine, with all videos broken down into individual frames.

Each expert, and the gating network, tracked facial expressions of each individual, with the help of a residual network (“ResNet”), a neural network used for object classification. In doing so, the model scored each frame based on level of valence (pleasant or unpleasant) and arousal (excitement) — commonly used metrics to encode different emotional states. Separately, six human experts labeled each frame for valence and arousal, based on a scale of -1 (low levels) to 1 (high levels), which the model also used to train.

The researchers then performed further model personalization, where they fed the trained model data from some frames of the remaining videos of subjects, and then tested the model on all unseen frames from those videos. Results showed that, with just 5 to 10 percent of data from the new population, the model outperformed traditional models by a large margin — meaning it scored valence and arousal on unseen images much closer to the interpretations of human experts.

This shows the potential of the models to adapt from population to population, or individual to individual, with very few data, Rudovic says. “That’s key,” he says. “When you have a new population, you have to have a way to account for shifting of data distribution [subtle facial variations]. Imagine a model set to analyze facial expressions in one culture that needs to be adapted for a different culture. Without accounting for this data shift, those models will underperform. But if you just sample a bit from a new culture to adapt our model, these models can do much better, especially on the individual level. This is where the importance of the model personalization can best be seen.”

Currently available data for such affective-computing research isn’t very diverse in skin colors, so the researchers’ training data were limited. But when such data become available, the model can be trained for use on more diverse populations. The next step, Feffer says, is to train the model on “a much bigger dataset with more diverse cultures.”

Better machine-human interactions

Another goal is to train the model to help computers and robots automatically learn from small amounts of changing data to more naturally detect how we feel and better serve human needs, the researchers say.

It could, for example, run in the background of a computer or mobile device to track a user’s video-based conversations and learn subtle facial expression changes under different contexts. “You can have things like smartphone apps or websites be able to tell how people are feeling and recommend ways to cope with stress or pain, and other things that are impacting their lives negatively,” Feffer says.

This could also be helpful in monitoring, say, depression or dementia, as people’s facial expressions tend to subtly change due to those conditions. “Being able to passively monitor our facial expressions,” Rudovic says, “we could over time be able to personalize these models to users and monitor how much deviations they have on daily basis — deviating from the average level of facial expressiveness — and use it for indicators of well-being and health.”

A promising application, Rudovic says, is human-robotic interactions, such as for personal robotics or robots used for educational purposes, where the robots need to adapt to assess the emotional states of many different people. One version, for instance, has been used in helping robots better interpret the moods of children with autism.

Roddy Cowie, professor emeritus of psychology at the Queen’s University Belfast and an affective computing scholar, says the MIT work “illustrates where we really are” in the field. “We are edging toward systems that can roughly place, from pictures of people’s faces, where they lie on scales from very positive to very negative, and very active to very passive,” he says. “It seems intuitive that the emotional signs one person gives are not the same as the signs another gives, and so it makes a lot of sense that emotion recognition works better when it is personalized. The method of personalizing reflects another intriguing point, that it is more effective to train multiple ‘experts,’ and aggregate their judgments, than to train a single super-expert. The two together make a satisfying package.”

New magnetism-control method could lead to ultrafast, energy-efficient computer memory

November 3, 2017
Original link:  http://www.kurzweilai.net/new-magnetism-control-method-could-lead-to-ultrafast-energy-efficient-computer-memory

A cobalt layer on top of a gadolinium-iron alloy allows for switching memory with a single laser pulse in just 7 picoseconds. The discovery may lead to a computing processor with high-speed, non-volatile memory right on the chip. (credit: Jon Gorchon et al./Applied Physics Letters)

Researchers at UC Berkeley and UC Riverside have developed an ultrafast new method for electrically controlling magnetism in certain metals — a breakthrough that could lead to more energy-efficient computer memory and processing technologies.

“The development of a non-volatile memory that is as fast as charge-based random-access memories could dramatically improve performance and energy efficiency of computing devices,” says Berkeley electrical engineering and computer sciences (EECS) professor Jeffrey Bokor, coauthor of a paper on the research in the open-access journal Science Advances. “That motivated us to look for new ways to control magnetism in materials at much higher speeds than in today’s MRAM.”

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Background: RAM vs. MRAM memory

Computers use different kinds of memory technologies to store data. Long-term memory, typically a hard disk or flash drive, needs to be dense in order to store as much data as possible but is slow. The central processing unit (CPU) — the hardware that enables computers to compute — requires fast memory to keep up with the CPU’s calculations, so the memory is only used for short-term storage of information (while operations are executed).

Random access memory (RAM) is one example of such short-term memory. Most current RAM technologies are based on charge (electron) retention, and can be written at rates of billions of bits per second (bits/nanosecond). The downside of these charge-based technologies is that they are volatile, requiring constant power or else they will lose the data.

In recent years, “spintronics” magnetic alternatives to RAM, known as Magnetic Random Access Memory (MRAM), have reached the market. The advantage of using magnets is that they retain information even when memory and CPU are powered off, allowing for energy savings. But that efficiency comes at the expense of speed, which is on the order of hundreds of picoseconds to write a single bit of information. (For comparison, silicon field-effect transistors have switching delays less than 5 picoseconds.)

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The researchers found a magnetic alloy made up of gadolinium and iron that could accomplish those higher speeds — switching the direction of the magnetism with a series of electrical pulses of about 10 picoseconds (one picosecond is 1,000 times shorter than one nanosecond) — more than 10 times faster than MRAM.*

A faster version, using an energy-efficient optical pulse

In a second study, published in Applied Physics Letters, the researchers were able to further improve the performance by stacking a single-element magnetic metal such as cobalt on top of the gadolinium-iron alloy, allowing for switching with a single laser pulse in just 7 picoseconds. As a single pulse, it was also more energy-efficient. The result was a computing processor with high-speed, non-volatile memory right on the chip, functionally similar to an IBM Research “in-memory” computing architecture profiled in a recent KurzweilAI article.

“Together, these two discoveries provide a route toward ultrafast magnetic memories that enable a new generation of high-performance, low-power computing processors with high-speed, non-volatile memories right on chip,” Bokor says.

The research was supported by grants from the National Science Foundation and the U.S. Department of Energy.

* The electrical pulse temporarily increases the energy of the iron atom’s electrons, causing the magnetism in the iron and gadolinium atoms to exert torque on one another, and eventually leads to a reorientation of the metal’s magnetic poles. It’s a completely new way of using electrical currents to control magnets, according to the researchers.

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Abstract of Ultrafast magnetization reversal by picosecond electrical pulses

The field of spintronics involves the study of both spin and charge transport in solid-state devices. Ultrafast magnetism involves the use of femtosecond laser pulses to manipulate magnetic order on subpicosecond time scales. We unite these phenomena by using picosecond charge current pulses to rapidly excite conduction electrons in magnetic metals. We observe deterministic, repeatable ultrafast reversal of the magnetization of a GdFeCo thin film with a single sub–10-ps electrical pulse. The magnetization reverses in ~10 ps, which is more than one order of magnitude faster than any other electrically controlled magnetic switching, and demonstrates a fundamentally new electrical switching mechanism that does not require spin-polarized currents or spin-transfer/orbit torques. The energy density required for switching is low, projecting to only 4 fJ needed to switch a (20 nm)3 cell. This discovery introduces a new field of research into ultrafast charge current–driven spintronic phenomena and devices.

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Abstract of Single shot ultrafast all optical magnetization switching of ferromagnetic Co/Pt multilayers

A single femtosecond optical pulse can fully reverse the magnetization of a film within picoseconds. Such fast operation hugely increases the range of application of magnetic devices. However, so far, this type of ultrafast switching has been restricted to ferrimagnetic GdFeCo
films. In contrast, all optical switching of ferromagnetic films require multiple pulses, thereby being slower and less energy efficient. Here, we demonstrate magnetization switching induced by a single laser pulse in various ferromagnetic Co/Pt multilayers grown on GdFeCo, by exploiting
the exchange coupling between the two magnetic films. Table-top depth-sensitive time-resolved magneto-optical experiments show that the Co/Pt magnetization switches within 7 ps. This coupling approach will allow ultrafast control of a variety of magnetic films, which is critical for
applications.

References:

• Yang Yang et al. Science Advances (Vol. 3, No. 49), Nov. 3, 2017 (open access)
• Jon Gorchon et al. Single shot ultrafast all optical magnetization switching of ferromagnetic Co/Pt multilayers. Applied Physics Letters, 111, 042401 (2017)