Section 1
Walking With Dinosaurs
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Peter L. Falkingham and his colleagues at Manchester University are developing techniques which look set to revolutionize our understanding of how dinosaurs and other extinct animals behaved.
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The media image of palaeontologists who study prehistoric life is often of field workers camped in the desert in the hot sun, carefully picking away at the rock surrounding a large dinosaur bone. But Peter Falkingham has done little of that for a while now. Instead, he devotes himself to his computer. Not because he has become inundated with paperwork, but because he is a new kind of palaeontologist: a computational palaeontologist.
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What few people may consider is that uncovering a skeleton, or discovering a new species, is where the research begins, not where it ends. What we really want to understand is how the extinct animals and plants behaved in their natural habitats. Drs Bill Sellers and Phil Manning from the University of Manchester use a 'genetic algorithm' – a kind of computer code that can change itself and 'evolve' – to explore how extinct animals like dinosaurs, and our own early ancestors, walked and stalked.
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The fossilized bones of a complete dinosaur skeleton can tell scientists a lot about the animal, but they do not make up the complete picture and the computer can try to fill the gap. The computer model is given a digitized skeleton, and the locations of known muscles. The model then randomly activates the muscles. This, perhaps unsurprisingly, results almost without fail in the animal falling on its face. So the computer alters the activation pattern and tries again … usually to similar effect. The modeled dinosaurs quickly 'evolve'. If there is any improvement, the computer discards the old pattern and adopts the new one as the base for alteration. Eventually, the muscle activation pattern evolves a stable way of moving, the best possible solution is reached, and the dinosaur can walk, run, chase or graze. Assuming natural selection evolves the best possible solution too, the modeled animal should be moving in a manner similar to its now-extinct counterpart. And indeed, using the same method for living animals (humans, emu and ostriches) similar top speeds were achieved on the computer as in reality. By comparing their cyberspace results with real measurements of living species, the Manchester team of palaeontologists can be confident in the results computed showing how extinct prehistoric animals such as dinosaurs moved.
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The Manchester University team have used the computer simulations to produce a model of a giant meat-eating dinosaur. It is called an acrocanthosaurus which literally means 'high spined lizard' because of the spines which run along its backbone. It is not really known why they are there but scientists have speculated they could have supported a hump that stored fat and water reserves. There are also those who believe that the spines acted as a support for a sail. Of these, one half think it was used as a display and could be flushed with blood and the other half think it was used as a temperature-regulating device. It may have been a mixture of the two. The skull seems out of proportion with its thick, heavy body because it is so narrow and the jaws are delicate and fine. The feet are also worthy of note as they look surprisingly small in contrast to the animal as a whole. It has a deep broad tail and powerful leg muscles to aid locomotion. It walked on its back legs and its front legs were much shorter with powerful claws.
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Falkingham himself is investigating fossilized tracks, or footprints, using computer simulations to help analyze how extinct animals moved. Modern-day trackers who study the habitats of wild animals can tell you what animal made a track, whether that animal was walking or running, sometimes even the sex of the animal. But a fossil track poses a more considerable challenge to interpret in the same way. A crucial consideration is knowing what the environment including the mud, or sediment, upon which the animal walked was like millions of years ago when the track was made. Experiments can answer these questions but the number of variables is staggering. To physically recreate each scenario with a box of mud is extremely time-consuming and difficult to repeat accurately. This is where computer simulation comes in.
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Falkingham uses computational techniques to model a volume of mud and control the moisture content, consistency, and other conditions to simulate the mud of prehistoric times. A footprint is then made in the digital mud by a virtual foot. This footprint can be chopped up and viewed from any angle and stress values can be extracted and calculated from inside it. By running hundreds of these simulations simultaneously on supercomputers, Falkingham can start to understand what types of footprint would be expected if an animal moved in a certain way over a given kind of ground. Looking at the variation in the virtual tracks, researchers can make sense of fossil tracks with greater confidence.
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The application of computational techniques in palaeontology is becoming more prevalent every year. As computer power continues to increase, the range of problems that can be tackled and questions that can be answered will only expand.
Section 2
A mechanical friend for children
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The development of robots that interact socially with people and assist them in everyday life has been an elusive goal of modern science. Despite impressive advances in the mechanical aspects of this problem, producing robots that bond and socialize with people for sustained periods of time has proven difficult. The most successful robots so far have been storytellers, but they have only been able to maintain human interest for a limited time and typically rely on the robot telling stories that change over time. In practice, commercially available robots seldom cross the 10-hour barrier (i.e. individual users tend to spend less than a combined total of 10 hours with the robots before losing interest). This observation is in sharp contrast to the long-term interactions and bonding that commonly develop between humans and their pets.
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In a recent study, researchers from the Institute for Neural Computation in California introduced a state-of-the-art social robot into a classroom of 18- to 24-month-olds for five months as a way of studying human/robot interactions. The researchers, including Fumihide Tanaka and Javier R Movellan, introduced a toddler-sized humanoid robot into a classroom at a childhood education center. One of the QRIO series of robots, the 58cm machine, was originally developed by Sony. Children of toddler age were chosen because they have no preconceived notions of robots, according to Tanaka. One of the goals of the study was to establish whether it was possible for social robots to maintain the interest of children beyond the 10-hour barrier.
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The researchers sent instructions to the robot about every two minutes to do things like giggle, dance, sit down, fall down or walk in a certain direction. The 45 sessions were videotaped, and interactions between toddlers and the robot were later analyzed. The results showed that the quality of those interactions improved steadily over 27 sessions. The interactions deteriorated quickly over the next 15 sessions, when the robot was ordered to behave in a more limited, predictable manner. Finally, the human/robot relations improved in the last three sessions, after the robot had been instructed to display its full range of behaviors.
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'Initially the children treated the robot very differently from the way they treated each other,' Tanaka said. 'Early in the study some children cried when QRIO fell. But a month into the study, the toddlers helped QRIO stand up by pushing its back or pulling its hands.' The most important aspect of interaction was touch, Tanaka said. At first the toddlers would touch the robot on its face, but later on they would touch it only on its hands and arms, like they would with other humans. Another robot-like toy named Robby, which resembled QRIO but did not move, was used as a control in the study. While hugging of QRIO increased, hugging of Robby decreased throughout the study. Furthermore, when QRIO laid down on the floor as its batteries ran down, a toddler would put a blanket over his silver-colored 'friend' and say 'night-night'. Caretaking behaviors were frequently observed toward QRIO but seldom toward Robby.
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The study concluded that after 45 days of immersion in a childcare center over a period of five months, long-term bonding and socialization occurred between toddlers and a state-of-the-art social robot. Overall, the interaction between children and the robot improved over time and the children progressively treated it more as a peer than a plaything. 'To my knowledge, this is the first long-term study of this sort,' said Ronald Arkin, a roboticist at the Georgia Institute of Technology in the US.
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Tanaka and Movellan are now developing autonomous robots for the toddler classroom. 'It could have great potential in educational settings assisting teachers and enriching the classroom environment,' Tanaka said. The researchers hope that more advanced versions of robots like QRIO could become personalized tutors to assist teachers in classrooms. A robotic tutor could react on the spot to social cues and approximate social skills like facial expression and eye gaze, they said. 'It is becoming clear that, to achieve this goal, we are going to [need to] endow machines with something similar to emotion, not just traditional forms of intelligence,' said Movellan.
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Associate professor David Powers, an expert in artificial intelligence and cognitive science at Flinders University in South Australia, commented, 'In this study it is clearly demonstrated that limited range of robot behaviors, however impressive, is nowhere near as important to human/robot interaction as being able to make appropriate responses from a broad repertoire of behaviors'. Ronald Arkin was not surprised by the affection demonstrated by the toddlers toward the robot. 'Humans have a tremendous propensity to bond with artifacts, whether it be a car, a doll, or a robot,' he said. But he also cautioned that researchers do not yet understand the consequences of increased human/robot interaction. 'Studying how robots and humans work together can give us insight into whether this is a good thing or a bad thing for society,' Arkin said. 'We need to find out what the consequences are of introducing a robot into a cadre of children. How will that enhance, or potentially interfere with, their social development? Do we really understand the long-term impact of having a robot as a childhood friend?'
Section 3
Understanding symbols
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About 20 years ago I had one of those wonderful moments when research takes an unexpected but fruitful turn. I had been studying toddler memory and was beginning a new experiment with two-and-a-half- and three-year-olds. For the project, I had built a model of a room that was in my laboratory. The real space contained basic furniture such as a couch and table. The miniature version was as similar as possible to its larger counterpart: the furniture was the same shape and material and was arranged in the same position. For the study, a child watched as we hid a miniature toy – a plastic dog we called Little Snoopy* – in the model. We then encouraged the child to find 'Big Snoopy', a large version of the toy 'hiding in the same place in his big room.'
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The three-year-olds were very successful. After they observed the small toy being placed behind the miniature couch, they ran into the room and found the large toy behind the real couch. But the two-and-a-half-year-olds failed abysmally. They cheerfully ran into the room to retrieve the large toy, but most had no idea where to look, even though they remembered where the tiny toy was hidden in the miniature room and could readily find it there.
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Their failure to use what they knew about the model to draw an inference about the real room indicated that they did not appreciate the relation between the model and the room. I realised my memory study was instead a study of symbolic understanding and that the children's failure might be telling us something about how and when children acquire the ability to understand that one object stands for another.
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The first type of symbolic object that infants and young children master is the picture. No symbols seem simpler to adults, but infants initially find pictures perplexing. The problem stems from the duality inherent in all symbolic objects: they are real in themselves and also representations of something else. A few years ago I became intrigued by anecdotes suggesting that infants do not appreciate this duality–stories of a baby trying to pick up a depicted toy or fit a foot into a photograph of a shoe. We assumed such behaviour would be rare and therefore difficult to study.
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Fortunately we were wrong. We began testing infants' understanding of pictures by putting a book of highly realistic colour photographs of individual objects in front of nine-month-olds. To our surprise, every child in the study reached out to feel or scratch the pictures. The confusion seems to be conceptual not perceptual. Infants can perfectly well perceive the difference between objects and pictures and given the choice they will choose the real thing. But they do not yet fully understand what pictures are and how they differ from real objects. However, when depicted objects bear little resemblance to the real thing – as in a black and white drawing – infants rarely explore them. By 18 months, babies have come to appreciate that a picture merely represents an object; instead of manipulating the paper, they point to pictures and name objects. Nevertheless, it takes several years for the nature of pictures to be completely understood.
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Studies have shown that, until the age of four, many children think that turning a picture of a bowl of popcorn upside down will result in the depicted popcorn falling out of the bowl.
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Pictures are not the only source of confusion for very young children. In a third experiment, we brought a group of toddlers (18- to 30-month-old children) into a room containing a child's slide, a chair, and a toddler-sized car. The children played with these for a while. Then we secretly replaced each object with an identical miniature version. Most children attempted to perform the same actions with the miniature items that they had with the larger ones. Some tried to sit on the chair, others attempted to climb into the car. Interestingly, most of the children showed little or no reaction to their failed attempts. We think this probably reflects the fact that toddlers' daily lives are full of unsuccessful attempts to do one thing or another. This confusion has implications for educational practice. Teachers everywhere use blocks or other objects to represent numerical quantity. However, if young children do not understand the relation between an object and what it represents, this could be counterproductive. To demonstrate this, we taught six- and seven-year-olds a difficult subtraction problem using blocks.
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We taught an identical comparison group the same concept, but using pencil and paper. Both groups learned to solve the problems equally well, but the group using blocks took three times as long to do so.
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Dual representation also comes into play in many children's books. Modern children's books are often 3D, with features that encourage children to interact directly with the book itself, for example flaps that can be lifted to reveal pictures. Graduate student Cynthia Chong and I reasoned that these features might distract children from the information. Accordingly, we used different types of books to teach letters to 30-month-old children. One was a simple old-fashioned alphabet book with each letter accompanied by an appropriate picture. The other was a 3-D version. The children using the traditional book subsequently recognised more letters than those using the 3-D book.
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Presumably, the children could concentrate more easily with the plain 2-D book, whereas with the other one they were distracted by the 3-D activities. Less may be more when it comes to educational books for young children.