Tag: cognitivescience

Why Do Some People Never Forget A Face?
Individuals who process faces more holisticallythat is, as an integrated wholeare better at face recognition, says Liu.The findings will appear in an upcoming issue of Psychological Science, a journal published by the Association for Psychological Science. To isolate holistic processing as the key to face recognition, the researchers first measured the ability of study participants337 male and female studentsto remember whole faces, using a task in which they had to select studied faces and flowers from among unfamiliar ones. They do better when the feature is presented within the whole face than when it stands on its own among other noses: again, we remember the nose integrated into the whole face. But there was no link between facial recognition and general intelligence, which is made up of various cognitive processesa suggestion that face processing is unique. (more)

Scientists Model Brain to Teach Computers to Recognize
Researchers at the beginning of the computer revolution assumed that teaching a computer to recognize something would be easy. Researchers from Los Alamos National Laboratory, Chatham University, and Emory University created a neural network with a slightly different structure than what is usually used in research. Most neural network research involves wiring artificial neurons from one layer to others in another layer. In this case, the researchers decided to wire some neurons to other neurons in the same layer, creating lateral connections. (more)

Virtual robot links body to numbers just like humans
Read more: "Squishybots: Soft, bendy and smarter than ever" ONE of the many curious habits of the human brain is that we tend to associate small numbers with the left side of our body and large numbers with our right. The so-called SNARC (spatial-numerical association of response codes) effect is well established: people respond faster to a number (by pressing a button, say) with their left hand when the number is small and with their right hand when the number is large. Similarly, people who have brain damage that causes them to ignore the left side of their body show a bias towards larger numbers when asked to report the middle of a numerical interval. This way of learning sets up links between small numbers and the part of the brain that controls our left side, and vice versa, that persists into adulthood. (more)

Brain Stimulation And Ethics
Transcranial direct current stimulation (or TDCS), is a type of non-invasive brain stimulation in which weak electrical currents are applied to the head via electrodes for a short time (about 20 minutes). Most research has focused on using this type of stimulation as a means to improve the cognitive capacities of people with certain psychological or cognitive disabilities. But recent research has shown that TDCS may also improve the cognitive capacities of those without such disabilities. Next, they focus on two issues in particular: premature use of TDCS and using it on the developing brain. (more)

Neural network learns to identify group sizes without knowledge of numbers
After feeding the network 51,800 images, where each was a unique layout of rectangles of various sizes, the researchers found that the new images generated by the system began to demonstrate an awareness of the relative size of different groups without having to perform any counting. Next, to demonstrate, ANS, the team fed the system another program that allowed the system to compare different groups that it had seen during the first run and found, based on new images generated, that the system was able to make educated guesses about which was bigger or smaller; Teaching computer systems to learn to use ANS is but one step towards creating machines that think rather than simply crunch numbers for us, and the hope is that one day, such systems can be put into robots to make them as useful as those weve seen in movies for decades. (more)

Have We Met? Tracing Face Blindness to Its Roots
Those with prosopagnosia, also known as face blindness, can see perfectly well, but their brains are unable to piece together the information needed to understand that a collection of features represents an individuals face. One of the keys to understanding face recognition, it seems, is understanding how the brain comes to recognize voices. But by testing for these two conditions simultaneously, researchers at the Max Planck Institute for Human Cognitive and Brain Sciences in Germany recently found evidence that face and voice recognition may be linked in a novel person-recognition system. The researchers found that regions of the brain already associated with facial recognition, like the fusiform face area in the occipital lobe, are directly linked to regions responsible for voice recognition, mostly in the temporal lobe. (more)

How does our brain see Jesus face on a tortilla?
Whether its New Hampshires erstwhile granite Old Man of the Mountain, or a face on Mars, our brains are adept at locating images that look like faces. nonface Sinha and his students set out to investigate how that brain region decides what is and is not a face, particularly in cases where an object greatly resembles a face. To help them do that, the researchers created a continuum of images ranging from those that look nothing like faces to genuine faces. They found images that very closely resemble faces by examining photographs that machine vision systems had falsely tagged as faces. (more)

Science fiction brings up religious conflicts
tool nameIn my previous column, I briefly described how I came to be interested in the film and literary genre of science fiction from the perspective of a religious practitioner, in my case, Roman Catholic Christianity, and how it could be a useful way of exploring issues such as the increasingly pervasive presence of technology in our world. Defining the human being is a way of trying to understand what human beings are as a foundation for understanding the purpose of human beings in the universe. More often in science fiction the nonhuman characters with human appearance are machines such as the cyborgs in the Terminator franchise, the cylons in the rebooted Battlestar Galactica and its spin-off, Caprica, or the replicants in the classic, Blade Runner, based on Philip Dicks novel, Do Androids Dream of Electric Sheep? Some science fiction works explore the human motivations behind the creation of such humanoid machines. (more)

A tour of the lab where IBM makes its brain chips (video)
A tour of the lab where IBM makes its brain chips(video) San Jose, Calif. At the IBM Almaden Research Center , a team of researchers is creating the artificial brains of the future. We took a tour of this lab from Dharmendra Modha (pictured), principle investigator on the brain chip project. IBM and its partners have already built a brain-like chip prototype , and Modha and his colleagues showed us how it works. IBM calls the larger project Synapse (Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE). (more)

How the Brain Becomes Conscious
The Avatar is not imaginary or ethereal but rather has a clear physical existence in the form of unique impulse patterns in distributed brain circuits. It all begins in the womb, says Klemm. Klemms idea is that When active in wakefulness or dream states, the combinatorial impulse patterns that represent conscious self act as an agent of the brain, metaphorically as an Avatar. Each cortical column has the same circuitry of neurons and is extensively connected to many other columns, both adjacent and far away. (more)

Local academics recognised for brain computer interface work
It may sound like something straight from a science-fiction novel but Dr Damien Coyle and Dr Kongfatt Wong-Linn have been hailed by peers for their pioneering work in brain computer interface technology which allows people interact with computers using their brainwaves. Dr Coyle, a lecturer in the School of Computing and Intelligent Systems, was awarded the International Neural Network Societys Young Investigator of the Year 2011 award for his outstanding contributions in the field of neural networks at the International Joint Conference on Neural Networks (IJCNN) in San Jose while his colleague, Research Fellow Dr Wong-Lin, picked up the award for Best Paper at the same conference. Both the International Neural Network Society and the IEEE Computational Intelligence Society are the leading societies in the field so it is a humbling experience to have now received prestigious awards from both societies. He explains: Neurons in a brain region called the dorsal raphe nucleus emit an important brain chemical, serotonin, throughout various parts of the brain, modulating their neuronal activities and network functions, and also regulating mood, cognition and behaviour. (more)

New Chip Borrows Brains Computing Tricks
Reactions to the computer giant s press release about SyNAPSE, short for Systems of Neuromorphic Adaptive Plastic Scalable Electronic, have ranged from conservative to zany. Each neuron in the brain is a processor and memory, and part of a social network, but that s where the brain analogy ends. Modern computers are good at some things they have been with us since ENIAC, and I think they will be with us for perpetuity but they aren t well-suited for learning. The brain can take information from sight, touch, sound, smell and other senses and integrate them into modalities. (more)

Language localized in the brain
Functional specificity refers to the idea that discrete parts of the brain handle distinct tasks. To determine this, the researchers analyzed each subject individually using fMRI, making sure thatpatterns of activity in one brain would only ever be compared to patterns of activity from that same brain.The researchers spent the first 10 to 15 minutes of each fMRI scan having their subject do a fairly sophisticated language task while tracking brain activity. This way, they established where the language areas lie in that individual subject, so that later, when the subject performed other cognitive tasks, they could compare those activation patterns to the ones elicited by language. The researchers said the results don t imply that every cognitive function has its own dedicated piece of cortex. (more)

Robot vision lags behind human sight
Related articles NEW YORK: By pitting human vision against that of machines for the first time, computer scientists have shown that machines still struggle with interpreting visual patterns, compared to their human counterparts. The study, published in Proceedings of the National Academy of Sciences today, implies that computer vision research still has a long way to go before machines have visual perception rivalling that of humans. Humans recollect, robots forget The fact that humans are better than machines at understanding visual patterns did not come as a surprise to scientists. For machines to achieve a higher level of visual understanding, artificial intelligence researchers are teaching them to recognise individual parts of an object and combine their relative positions into a recognisable whole object. (more)

Unlocking the key to human intelligence
For Computer Science and Artificial Intelligence Laboratory (CSAIL) Professor Patrick Winston, the Ford Professor of Artificial Intelligence and Computer Science and leader of the Genesis Group at CSAIL, uncovering the true nature of human intelligence is the next grand challenge.To solve the puzzle of how humans think, Winston is employing classic engineering methodology to build systems that think and comprehend as people do using computational methods.Motivated by a desire to advance artificial intelligence and create systems that operate in a manner consistent with high-level human thinking, Winston feels there is a substantial difference between machines that actually display human-like intelligence and those that possess superb computational powers such as IBMs Watson system. By outfitting machines with language-enabled characteristics the ability to direct our perceptual apparatus to solve problems, to describe events, and to teach through the sharing of stories Winston feels scientists will be able to develop systems that not only can sift through vast amounts of information, but also can deploy human-like precedent-based judgment to help solve complex problems and deal with complex situations.Winston and his students focus on creating systems that use previously acquired common sense knowledge and knowledge of plot patterns when tasked with story-understanding problems, just like humans do. (more)

Monkeys Control Virtual Limbs With Their Minds
Now, by implanting electrodes into both the motor and the sensory areas of the brain, researchers have created a virtual prosthetic hand that monkeys control using only their minds, and that enables them to feel virtual textures. Using the first set, the monkey could control a virtual monkey arm on a computer screen and sweep the hand over virtual disks with different textures. By giving the monkey rewards when it identified the right texture, the researchers discovered that it took as few as four training sessions for the animal to consistently distinguish the textures from one another, even when the researchers switched the order of the visually identical disks on the screen. Although the monkeys are all adults, the motor and sensory regions of their brains are amazingly plastic, Nicolelis says: the combination of seeing an appendage that they control and feeling a physical touch tricks them into thinking that the virtual appendage is their own within minutes. (more)

BYU PhD student creates computer that composes music
When BYU PhD candidate Kristine Monteith was sitting in natural language processing class, it wasn't letter sequences going through her head but music notes. The Utah State graduate in music therapy pursuing her PhD in computer at BYU decided to apply her right and left brain abilities to combine music and computer science. She invented a computer program that can compose original music that evokes emotions humans can relate with, even though it was generated from a machine. In the survey she performed for that, she found that 54 percent of listeners could identify the emotions in computer-generated music, while for the human-composed music, only 43 percent could identify the emotions in the melodies. (more)

The future of artificial intelligence
I believe as artificial intelligence advances, a new model "software as collaborator" - will become possible, with tremendous potential benefits. Software collaborators could be designed to be enough like people that this mutual adaptation is possible, and that we can understand and trust their contributions. Software collaborators that do not share these frailties could become valuable complements to individuals and to teams. We are still a long way from being able to build software collaborators, but there is important progress being made in many fronts in artificial intelligence. (more)

A Conversation with Herbert Simon: Previous Research Experience.
Herbert A. Simon explains how he came to apply computers to psychology. (more)

A panel discussion about Artificial Intelligence.
The Charlie Rose Show television broadcast: A panel discussion about the latest developments in Artificial Intelligence with Rodney Brooks of MIT, Eric Horvitz of Microsoft Research and Ron Brachman of the Defense Advanced Research Projects Agency. December 21, 2004. (more)

AGI-08 promotional video.
Promotional video for The First Conference on Artificial General Intelligence (AGI-08). FedEx Institute of Technology, University of Memphis. In cooperation with AAAI. March 1-3, 2008. The video answers the question: What is AGI?. December 2007. (more)

AGIRI 2006 Workshop Keynote Speaker: Dr. Stan Franklin (Dir. Institute for Intelligent Systems, University of Memphis) - A Cognitive Theory of Everything: The LIDA Technology as an Artificial General Intelligence.
"Implementing and fleshing out a number of psychological and neuroscience theories of cognition, the LIDA conceptual model aims at being a cognitive 'theory of everything.' With modules or processes for perception, working memory, episodic memories, 'consciousness,' procedural memory, action selection, perceptual learning, episodic learning, deliberation, volition, and non-routine problem solving, the LIDA model is ideally suited to provide a working ontology that would allow for the discussion, design, and comparison of AGI systems. The LIDA technology is based on the LIDA cognitive cycle, a sort of 'cognitive atom.' The more elementary cognitive modules play a role in each cognitive cycle. Higher-level processes are performed over multiple cycles. This talk will give a quick overview of the LIDA conceptual model, and its underlying computational technology." May 20, 2006. (more)

BACON.
Herbert A. Simon describes BACON and the nature of programs that do science and scientific discovery. March 21, 1990. (more)

BrainWorks.
"With the help of five kids, host Eric Chudler takes viewers on a journey inside of the brain. The show begins in the studio with an introduction to the nervous system. The kids then visit laboratories where they learn about automatic functions of the brain and how the electrical activity of the brain is recorded. Back in the studio, the kids see a real human brain and build their own model nerve cells and brains." April 13, 2006. (more)

CSE Colloquia - 2005: Intelligent Tutoring Systems - Improving Student Modeling.
Intelligent Tutoring Systems (ITS) are computer-based instructional tools that rely on artificial intelligence techniques to generate individualized interactions tailored to a student's learning needs. Cristina Conati [University of British Columbia] discusses how the scope and effectiveness of ITS can be increased by extending the range of features captured in a student model to include domain independent, meta-cognitive skills and affective states. October 19, 2004. (more)

CSE Colloquia - 2006: Turing’s Dream and the Knowledge Challenge.
In this Turing Center distinguished lecture, Lenhart Schubert [University of Rochester] explains that there is a set of clear-cut challenges for artificial intelligence, all centering around knowledge. The solution to those challenges could realize Alan M. Turing's dream - the dream of a machine capable of intelligent human-like response and interaction. Schubert presents preliminary results of recent efforts to extract 'shallow' general knowledge about the world from large text corpora. November 10, 2005. (more)

CSE Colloquia 2001 - Machines with Emotional Intelligence. Speaker: Rosalind Picard, Media Laboratory, Massachusetts Institute of Technology.
"Over 70 studies on human-machine interaction in the last decade have pointed to an intriguing phenomenon: People interact with machines in a way that is basically social, even when the interaction was not designed to be that way. This program will describe how we're giving computers some social skills, specifically the ability to recognize and respond appropriately to human emotion. Examples are shown on keyboard-mouse-monitor systems that try to assess user frustration for usability feedback, and wearable systems that classify affective state based on skin-surface measurements." Questions from the audience follow the talk. October 18, 2001. (more)

Chess Play.
Excerpt from "Cognitive Processes" lecture by Herbert A. Simon. October 24, 1989. (more)

Children using Computers to Learn.
Seymor Papert trying to show how kids can use computers to learn. Various shots of fourth grade kids giving mathematical orders to a computer in order to control a HP display, or to create songs. 1968-1969? (more)

Discussion of and Demonstrations of Learning Programs for Robots.
The first half of the film is a lecture by Marvin Minsky describing the basic ideas of Patrick Winston's learning program, using examples and "near misses" to refine the program's model of what an "arch" is. The second half of the film is a narration by Dave Waltz describing other robotics research at MIT. He discusses Tim Finin's program that uses Winston-like models to recognize objects that match the model even when parts of the object are obscured. It uses hypotheses about dimensions of the objects that it can not directly observe. 1975??. (more)

Eric Horvitz with Microsoft Research on “Surprise Modeling”.
Eric Horvitz, head of the Adaptive Systems and Interaction group at Microsoft Research, talks about surprise modeling. 2008?. (more)

Lighthill Controversy Debate at the Royal Institution with Professor Sir James Lighthill, Professor Donald Michie, Professor Richard Gregory and Professor John McCarthy.
Professors Donald Michie [Edinburgh], Richard Gregory [Bristol] and John McCarthy [Stanford] challenge the pessimistic findings & views of Professor Sir James Lighthill [Cambridge], author of "The Lighthill Report" [Artificial Intelligence: A General Survey, in Artificial Intelligence: a paper symposium, Science Research Council (1973)]. June 1973. (more)

Linking Brains, Computers.
Because it has been around for such a long time, and has either misled or annoyed so many people over the years, it ought to have a name. Let's call it the Synapse Equivalency Fallacy. Synapses are the interconnections between the neurons that make up the brain and nervous system. The fallacy occurs when a writer likens the transistors in a computer to the synapses in a brain, usually as part of an effort to make computers seem like brains. July 09, 2008. (more)

Mind Reading.
“As pollsters have so well demonstrated this presidential primary season, reading minds, whether of voters or the person next to you, is close to impossible. However as this ScienCentral News video explains, scientists are actually one step closer to reading our thoughts. … [T]he new research is aimed at the biology underlying thoughts-- or, as scientists call them, ‘cognitive processes.’ Carnegie Mellon cognitive psychologist Marcel Just teamed up with machine learning expert Tom Mitchell to conduct the research.” February 2, 2008. (more)

Reflections on Science (series): Creativity and Computers - a discussion with Margaret Boden, University of Sussex..
"The concept of creativity from the point of view of how original ideas develop is explored with the aid of recent advances in computer modelling programming strategies. Featuring some beautiful examples, Margaret [Boden] addresses the question, can computers ever be truly creative?" Hosted by Mike Bullivant, a scientist at the Open University. 1998. (more)

Scientific American Frontiers with Alan Alda: "Almost Human" segment from the "Robots Alive!" broadcast.
Rodney Brooks is beginning to build the first robot with human-like senses, allowing it to learn about the world for itself, like a human baby. April 9, 1997. (more)

Scientific American Frontiers with Alan Alda: "Alpha Wolf" segment from "The Intimate Machine" broadcast.
Researchers build artificial intelligence software modeled on the canine mind. October 22, 2002. (more)

Scientific American Frontiers with Alan Alda: "Robots Have Feelings, Too" segment from the "Natural Born Robots" broadcast.
SAGE, Bit and Kismet are robots with social skills. Researchers hope robotic emotions will make tomorrow's technology more user-friendly. November 2, 1999. (more)

Soar: excerpt from Allen Newell's William James Lectures.
Allen Newell explains the Soar architecture. March 11, 1987. (more)

Symbol System: excerpt from AI: What Can it Do? Where is it Going?.
Herbert A. Simon explains the hypothesis that intelligent behavior (be it humans or computers) requires the ability to deal with symbols/patterns. March 21, 1990. (more)

TED Conference - Hod Lipson: Robots that are "self-aware".
“Hod Lipson demonstrates a few of his cool little robots, which have the ability to learn, understand themselves and even self-replicate. At the root of this uncanny demo is a deep inquiry into the nature of how humans and living beings learn and evolve, and how we might harness these processes to make things that learn and evolve.”. March 10, 2008. (more)

The Next Big Thing (Series Two): Machines with Minds.
Real moving, interacting robots is one promising direction in artificial intelligence. But what about the original hope of matching human performance, and what has A.I. told us about the human brain? When science of artificial intelligence was launched in the 50s, its goal was to match the intellectual achievements of human beings. Why isn't machine intelligence already far superior to that of people? Chaired by Colin Blakemore [Oxford University], the panel consists of Professor Aaron Sloman (University of Birmingham), Dr Amanda Sharkey (University of Sheffield), and Professor Igor Aleksander (Imperial College). 2002. (more)

The Painting Fool.
Simon Colton lecture on The Painting Fool. Winner of the 2007 Machine Intelligence Competition. December 11, 2007. (more)

Tower of Hanoi: excerpt from A Conversation with Herbert Simon – A Video Tribute by Julia Love.
Herbert A. Simon uses the Tower of Hanoi puzzle to illustrate how human problem solving is studied. (more)

USC Presents...Closer To Truth: What is Consciousness?
"What is Consciousness – our inner thoughts, feelings, personalities -- the hidden 'Stuff' of our Private Selves? Is there something special about Consciousness, something of the mind not in the brain? This is self awareness, the interior mental experience we call Consciousness. What is the importance of studying Consciousness? The panel discusses the concept of human consciousness." August 8, 2004. (more)

Unified Theory of Cognition: excerpt from Allen Newell's William James Lectures.
Excerpt from lecture by Allen Newell. February 18, 1987. (more)

Visual Elements in Robotics: excerpt from "AI: What Can it Do? Where is it Going?".
Excerpt from lecture by Herbert A. Simon. March 21, 1990. (more)

Washburn Lecture Series at the Museum of Science, Boston: "2001: A Space Odyssey. Are we there yet?" Lecture one (of three) - Human/Computer Conversation: HAL and Beyond, with Justine Cassell, Ph.D..
Justine Cassell's lecture, "Human/Computer Conversation: HAL and Beyond," was the first in the three speaker lecture series: "2001: A Space Odyssey. Are we there yet?" November 6, 2001. (more)

Wired Science: Face Reader.
"Ziya Tong meets children with Asperger’s Syndrome testing a new MIT Media Lab device that reads facial expressions." In the course of the report she discusses the project with several individuals including Rana el Kaliouby Ph.D. (Mindreader Software Developer, MIT)); Alea Teeters (MIT Affective Computing Group), and Rosalind Picard, Ph.D. (Director, MIT Affective Computing Group). October 3, 2007. (more)