How Social Pressure Can Affect What We Remember: Scientists Track Brain Activity as False Memories Are Formed

How Social Pressure Can Affect What We Remember: Scientists Track Brain Activity as False Memories Are Formed

  10:02:00 AM    Science Lover

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How easy is it to falsify memory? New research at the Weizmann Institute shows that a bit of social pressure may be all that is needed. The study, which appears in the journal Science, reveals a unique pattern of brain activity when false memories are formed -- one that hints at a surprising connection between our social selves and memory.

New research reveals a unique pattern of brain 
activity when false memories are formed -- one 
that hints at a surprising connection between 
our social selves and memory. (Credit: Image 
courtesy of Weizmann Institute of Science)

The experiment, conducted by Prof. Yadin Dudai and research student Micah Edelson of the Institute's Neurobiology Department with Prof. Raymond Dolan and Dr. Tali Sharot of University College London, took place in four stages. In the first, volunteers watched a documentary film in small groups. Three days later, they returned to the lab individually to take a memory test, answering questions about the film. They were also asked how confident they were in their answers.

They were later invited back to the lab to retake the test while being scanned in a functional MRI (fMRI) that revealed their brain activity. This time, the subjects were also given a "lifeline": the supposed answers of the others in their film viewing group (along with social-media-style photos). Planted among these were false answers to questions the volunteers had previously answered correctly and confidently. The participants conformed to the group on these "planted" responses, giving incorrect answers nearly 70% of the time.

But were they simply conforming to perceived social demands, or had their memory of the film actually undergone a change? To find out, the researchers invited the subjects back to the lab to take the memory test once again, telling them that the answers they had previously been fed were not those of their fellow film watchers, but random computer generations. Some of the responses reverted back to the original, correct ones, but close to half remained erroneous, implying that the subjects were relying on false memories implanted in the earlier session.

An analysis of the fMRI data showed differences in brain activity between the persistent false memories and the temporary errors of social compliance. The most outstanding feature of the false memories was a strong co-activation and connectivity between two brain areas: the hippocampus and the amygdala. The hippocampus is known to play a role in long-term memory formation, while the amygdala, sometimes known as the emotion center of the brain, plays a role in social interaction. The scientists think that the amygdala may act as a gateway connecting the social and memory processing parts of our brain; its "stamp" may be needed for some types of memories, giving them approval to be uploaded to the memory banks. Thus social reinforcement could act on the amygdala to persuade our brains to replace a strong memory with a false one.

Prof. Yadin Dudai's research is supported by the Norman and Helen Asher Center for Human Brain Imaging, which he heads; the Nella and Leon Benoziyo Center for Neurological Diseases; the Carl and Micaela Einhorn-Dominic Institute of Brain Research, which he heads; the Marc Besen and the Pratt Foundation, Australia; Lisa Mierins Smith, Canada; Abe and Kathryn Selsky Memorial Research Project; and Miel de Botton, UK. Prof. Dudai is the incumbent of the Sara and Michael Sela Professorial Chair of Neurobiology.

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Genius of Einstein, Fourier key to new humanlike computer vision

Genius of Einstein, Fourier key to new humanlike computer vision

  1:46:00 AM    Science Lover

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Two new techniques for computer-vision technology mimic how humans perceive three-dimensional shapes by instantly recognizing objects no matter how they are twisted or bent, an advance that could help machines see more like people.

This graphic illustrates a new computer-vision technology
that builds on the basic physics and mathematical equations
related to how heat diffuses over surfaces. The technique
mimics how humans perceive three-dimensional shapes
by instantly recognizing objects no matter how they are
twisted or bent, an advance that could help machines see
more like people. Here, a "heat mean signature" of a human
hand model is used to perceive the six segments of the
overall shape and define the fingertips. (Purdue University
image/Karthik Ramani and Yi Fang)

The techniques, called heat mapping and heat distribution, apply mathematical methods to enable machines to perceive three-dimensional objects, said Karthik Ramani, Purdue University's Donald W. Feddersen Professor of Mechanical Engineering.

"Humans can easily perceive 3-D shapes, but it's not so easy for a computer," he said. "We can easily separate an object like a hand into its segments - the palm and five fingers - a difficult operation for computers."

Both of the techniques build on the basic physics and mathematical equations related to how heat diffuses over surfaces.

"Albert Einstein made contributions to diffusion, and 18th century physicist Jean Baptiste Joseph Fourier developed Fourier's law, used to derive the heat equation," Ramani said. "We are standing on the shoulders of giants in creating the algorithms for these new approaches using the heat equation."

As heat diffuses over a surface it follows and captures the precise contours of a shape. The system takes advantage of this "intelligence of heat," simulating heat flowing from one point to another and in the process characterizing the shape of an object, he said.

Findings will be detailed in two papers being presented during the IEEE Computer Vision and Pattern Recognition conference on June 21-23 in Colorado Springs. The paper was written by Ramani, Purdue doctoral students Yi Fang and Mengtian Sun, and Minhyong Kim, a professor of pure mathematics at the University College London.

A major limitation of existing methods is that they require "prior information" about a shape in order for it to be analyzed.

Researchers developing a new machine-vision technique tested
their method on certain complex shapes, including the human form
or a centaur – a mythical half-human, half-horse creature. The heat
mapping allows a computer to recognize the objects no matter how
the figures are bent or twisted and is able to ignore "noise" introduced
by imperfect laser scanning or other erroneous data. (Purdue University
image/Karthik Ramani and Yi Fang)

"For example, in order to do segmentation you have to tell the computer ahead of time how many segments the object has," Ramani said. "You have to tell it that you are expecting, say, 10 segments or 12 segments."

The new methods mimic the human ability to properly perceive objects because they don't require a preconceived idea of how many segments exist.

"We are trying to come as close as possible to human segmentation," Ramani said. "A hot area right now is unsupervised machine learning. This means a machine, such as a robot, can perceive and learn without having any previous training. We are able to estimate the segmentation instead of giving a predefined number of segments."



The work is funded partially by the National Science Foundation. A patent on the technology is pending.

The methods have many potential applications, including a 3-D search engine to find mechanical parts such as automotive components in a database; robot vision and navigation; 3-D medical imaging; military drones; multimedia gaming; creating and manipulating animated characters in film production; helping 3-D cameras to understand human gestures for interactive games; contributing to progress of areas in science and engineering related to pattern recognition; machine learning; and computer vision.

The heat-mapping method works by first breaking an object into a mesh of triangles, the simplest shape that can characterize surfaces, and then calculating the flow of heat over the meshed object. The method does not involve actually tracking heat; it simulates the flow of heat using well-established mathematical principles, Ramani said.

Heat mapping allows a computer to recognize an object, such as a hand or a nose, no matter how the fingers are bent or the nose is deformed and is able to ignore "noise" introduced by imperfect laser scanning or other erroneous data.

"No matter how you move the fingers or deform the palm, a person can still see that it's a hand," Ramani said. "But for a computer to say it's still a hand is going to be hard. You need a framework - a consistent, robust algorithm that will work no matter if you perturb the nose and put noise in it or if it's your nose or mine."

The method accurately simulates how heat flows on the object while revealing its structure and distinguishing unique points needed for segmentation by computing the "heat mean signature." Knowing the heat mean signature allows a computer to determine the center of each segment, assign a "weight" to specific segments and then define the overall shape of the object.

"Being able to assign a weight to segments is critical because certain points are more important than others in terms of understanding a shape," Ramani said. "The tip of the nose is more important than other points on the nose, for example, to properly perceive the shape of the nose or face, and the tips of the fingers are more important than many other points for perceiving a hand."

In temperature distribution, heat flow is used to determine a signature, or histogram, of the entire object.

"A histogram is a two-dimensional mapping of a three-dimensional shape," Ramani said. "So, no matter how a dog bends or twists, it gives you the same signature."

The temperature distribution technique also uses a triangle mesh to perceive 3-D shapes. Both techniques, which could be combined in the same system, require modest computer power and recognize shapes quickly, he said.

"It's very efficient and very compact because you're just using a two-dimensional histogram," Ramani said. "Heat propagation in a mesh happens very fast because the mathematics of matrix computations can be done very quickly and well."

The researchers tested their method on certain complex shapes, including hands, the human form or a centaur, a mythical half-human, half-horse creature.

Sources: Karthik Ramani, 765-494-5725, ramani@purdue.edu

Yi Fang, fang4@purdue.edu

Note to Journalists: The papers are available by contacting Emil Venere, Purdue News Service, at 765-494-4709, venere@purdue.edu

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Butterfly Effect Makes Brain Unreliable

Butterfly Effect Makes Brain Unreliable

  11:07:00 PM    Science Lover

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Next time your brain plays tricks on you, you have an excuse: according to new research by UCL scientists published June 30 in the journal Nature, the brain is intrinsically unreliable.

Researchers introduced a small perturbation into the brain, the neural equivalent of butterfly wings, and ask what would happen to the activity in the circuit. Would the perturbation grow and have a knock-on effect, thus affecting the rest of the brain, or immediately die out? (Credit: Image courtesy of University College London)

This may not seem surprising to most of us, but it has puzzled neuroscientists for decades. Given that the brain is the most powerful computing device known, how can it perform so well even though the behaviour of its circuits is variable?

A long-standing hypothesis is that the brain's circuitry actually is reliable -- and the apparently high variability is because your brain is engaged in many tasks simultaneously, which affect each other.

It is this hypothesis that the researchers at UCL tested directly. The team -- a collaboration between experimentalists at the Wolfson Institute for Biomedical Research and a theorist, Peter Latham, at the Gatsby Computational Neuroscience Unit -- took inspiration from the celebrated butterfly effect -- from the fact that the flap of a butterfly's wings in Brazil could set off a tornado in Texas. Their idea was to introduce a small perturbation into the brain, the neural equivalent of butterfly wings, and ask what would happen to the activity in the circuit. Would the perturbation grow and have a knock-on effect, thus affecting the rest of the brain, or immediately die out?

It turned out to have a huge knock-on effect. The perturbation was a single extra 'spike', or nerve impulse, introduced to a single neuron in the brain of a rat. That single extra spike caused about thirty new extra spikes in nearby neurons in the brain, most of which caused another thirty extra spikes, and so on. This may not seem like much, given that the brain produces millions of spikes every second. However, the researchers estimated that eventually, that one extra spike affected millions of neurons in the brain.

"This result indicates that the variability we see in the brain may actually be due to noise, and represents a fundamental feature of normal brain function," said lead author Dr. Mickey London, of the Wolfson Institute for Biomedical Research, UCL.

This rapid amplification of spikes means that the brain is extremely 'noisy' -- much, much noisier than computers. Nevertheless, the brain can perform very complicated tasks with enormous speed and accuracy, far faster and more accurately than the most powerful computer ever built (and likely to be built in the foreseeable future). The UCL researchers suggest that for the brain to perform so well in the face of high levels of noise, it must be using a strategy called a rate code. In a rate code, neurons consider the activity of an ensemble of many neurons, and ignore the individual variability, or noise, produced by each of them.

So now we know that the brain is truly noisy, but we still don't know why. The UCL researchers suggest that one possibility is that it's the price the brain pays for high connectivity among neurons (each neuron connects to about 10,000 others, resulting in over 8 million kilometres of wiring in the human brain). Presumably, that high connectivity is at least in part responsible for the brain's computational power. However, as the research shows, the higher the connectivity, the noisier the brain. Therefore, while noise may not be a useful feature, it is at least a by-product of a useful feature.

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Hand Study Reveals Brain's Distorted Body Model

Hand Study Reveals Brain's Distorted Body Model

  9:32:00 AM    Science Lover

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Our brains contain a highly distorted model of our own bodies, according to scientists at UCL (University College London).

Locations of knuckles and tips of each finger for 
the actual hand (black dots) and localisation 
judgments (white dots) superposed to highlight 
differences in shape. Average hand shape is 
displayed as solid lines for the actual hand and 
as dotted lines for the localisation judgments. 
The hand representation revealed by the localisation 
judgments is distorted, being represented as too wide 
and the fingers too short. (Credit: Image courtesy 
of University College London)

A new study on the brain's representation of the hand found that our model of our bodies is out of sync with reality -- with a strong tendency to think that the hands are shorter and fatter than their true shape.

The results of the study, published in the journal Proceedings of the National Academy of Sciences, show that the brain maintains a model of the hand in which our fingers are perceived to be shorter and our hands fatter than they are. Neuroscientists suspect the reason for these distortions may lie in the way the brain receives information from different regions of the skin.

Dr Matthew Longo, lead author from the UCL Institute of Cognitive Neuroscience, said: "The phrase "I know the town like the back of my hand" suggests that we have near-perfect knowledge of the size and position of our own body parts but these results show that this is far from being the case."

"Our results show dramatic distortions of hand shape, which were highly consistent across participants. The hand appears to be represented as wider than it actually is and the fingers as shorter than they actually are -- a finding that might also apply to other parts of the body," added Dr Longo.

Participants in the study were asked to put their left hands palm down under a board and judge the location of the covered hand's knuckles and fingertips by pointing to where they perceived each of these landmarks to be. A camera situated above the experiment recorded where the participant pointed. By putting together the locations of all the landmarks, the researchers reconstructed the brain's model of the hand, and revealed its striking distortions.

The research, which was funded by the Biotechnology and Biological Science Research Council (BBSRC), aims to find out how the brain knows where all parts of the body are in space even when your eyes are closed -- an ability known as 'position sense'. Neuroscientists think that position sense requires two distinct kinds of information. Signals that the brain receives from muscles and joints play an important role in position sense, but the brain also needs a model of the shape and size of each body part. For example, to know where the fingertip is in space, the brain needs to know the angles of joints in the arm and hand, but also the length of the arm, hand, and finger.

It is this model of our body's size and shape that is investigated in the study. "Of course, we know what our hand really looks like" said Dr Longo, "and our participants were very accurate in picking out a photo of their own hand from a set of photos with various distortions of hand shape. So there is clearly a conscious visual image of the body as well. But that visual image seems not to be used for position sense."

The results showed that in this task people estimated that their hands were about two-thirds wider and about one-third shorter than actual measurements.

Neuroscientists suspect that the brain's distorted model of body shape result is due to the way the brain represents different parts of the skin. For example, the size of the brain representation of the five fingers gets progressively smaller for each finger between the thumb and the little finger, mirroring the relative size of fingers in the body model reported in this study.

"These findings may well be relevant to psychiatric conditions involving body image such as anorexia nervosa, as there may be a general bias towards perceiving the body to be wider than it is. Our healthy participants had a basically accurate visual image of their own body, but the brain's model of the hand underlying position sense was highly distorted. This distorted perception could come to dominate in some people, leading to distortions of body image as well, such as in eating disorders," said Dr Longo.

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How We Know A Dog Is A Dog: Concept Acquisition In The Human Brain

How We Know A Dog Is A Dog: Concept Acquisition In The Human Brain

  10:35:00 AM    Science Lover

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A new study explores how our brains synthesize concepts that allow us to organize and comprehend the world. The research, published by Cell Press in the September 24th issue of the journal Neuron, uses behavioral and neuroimaging techniques to track how conceptual knowledge emerges in the human brain and guides decision making.

Although two dogs can look very different, the human brain recognizes them as particular instances of the concept of a dog. (Credit: iStockphoto/Annette Wiechmann)







The ability to use prior knowledge when dealing with new situations is a defining characteristic of human intelligence. This is made possible through the use of concepts, which are formed by abstracting away the common essence from multiple distinct but related entities. "Although a Poodle and a Golden Retriever look very different from each other, we can easily appreciate their similar attributes because they can be recognized as instances of a particular concept, in this case a dog," explains lead study author, Dr. Dharshan Kumaran from the Wellcome Trust Centre for Neuroimaging at University College London.

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