Animal Learning And Cognition A Neural Network Approach Pdf File
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It is therefore surprising that honey bees apparantly have this capacity.
- Importance Of Educational Psychology In Teaching Learning Process Pdf
- Animal learning and cognition : a neural network approach
- Animal Cognition
I will outline a few ideas, all predicated on the belief that learning consists of individuals' constructed meanings and then indicate how they influence museum education. Educational psychology is devoted to the study of how people learn including differences in learning, gifted learners, and learning disabilities. Learning strategies are procedures used to facilitate learning Chamot, and to enable learners to become 3 Metacognitive strategies: managing the learning process and dealing with the task, for In the socio-educational model, two categories of variables were pos-ited to influence learner. The article deals with psychological aspects in the process of foreign language mastering.
Importance Of Educational Psychology In Teaching Learning Process Pdf
Artificial neural networks ANNs have undergone a revolution, catalyzed by better supervised learning algorithms. However, in stark contrast to young animals including humans , training such networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead mainly on unsupervised learning.
Here we argue that most animal behavior is not the result of clever learning algorithms—supervised or unsupervised—but is encoded in the genome. Specifically, animals are born with highly structured brain connectivity, which enables them to learn very rapidly.
The genomic bottleneck suggests a path toward ANNs capable of rapid learning. Not long after the invention of computers in the s, expectations were high. Many believed that computers would soon achieve or surpass human-level intelligence. Of course, these predictions turned out to be wildly off the mark. In the tech world today, optimism is high again. In this scenario, as computers increase in power, it will become possible to build a machine that is more intelligent than the builders.
This superintelligent machine will build an even more intelligent machine, and eventually this recursive process will accelerate until intelligence hits the limits imposed by physics or computer science. But in spite of this progress, ANNs remain far from approaching human intelligence. ANNs can crush human opponents in games such as chess and Go, but along most dimensions—language, reasoning, common sense—they cannot approach the cognitive capabilities of a four-year old. Perhaps more striking is that ANNs remain even further from approaching the abilities of simple animals.
Many of the most basic behaviors—behaviors that seem effortless to even simple animals—turn out to be deceptively challenging and out of reach for AI. In the words of one of the pioneers of AI, Hans Moravec 3 :. The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge.
We are all prodigious Olympians in perceptual and motor areas, so good that we make the difficult look easy. Abstract thought, though, is a new trick, perhaps less than thousand years old. We have not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it. We cannot build a machine capable of building a nest, or stalking prey, or loading a dishwasher.
In many ways, AI is far from achieving the intelligence of a dog or a mouse, or even of a spider, and it does not appear that merely scaling up current approaches will achieve these goals. The good news is that, if we do ever manage to achieve even mouse-level intelligence, human intelligence may be only a small step away. Our vertebrate ancestors, who emerged about million years ago, may have had roughly the intellectual capacity of a shark.
A major leap in the evolution of our intelligence was the emergence of the neocortex, the basic organization of which was already established when the first placental mammals arose about million years ago 4 ; much of human intelligence seems to derive from an elaboration of the neocortex. Modern humans Homo sapiens evolved only a few hundred thousand years ago—a blink in evolutionary time—suggesting that those qualities such as language and reason which we think of as uniquely human may actually be relatively easy to achieve, provided that the neural foundation is solid.
Although there are genes and perhaps cell types unique to humans—just as there are for any species—there is no evidence that the human brain makes use of any fundamentally new neurobiological principles not already present in a mouse or any other mammal , so the gap between mouse and human intelligence might be much smaller than that between than that between current AI and the mouse.
This suggests that even if our eventual goal is to match or even exceed human intelligence, a reasonable proximal goal for AI would be to match the intelligence of a mouse. As the name implies, ANNs were invented in an attempt to build artificial systems based on computational principles used by the nervous system 5. In what follows, we suggest that additional principles from neuroscience might accelerate the goal of achieving artificial mouse, and eventually human, intelligence.
We argue that in contrast to ANNs, animals rely heavily on a combination of both learned and innate mechanisms. These innate processes arise through evolution, are encoded in the genome, and take the form of rules for wiring up the brain 6. We discuss the implications of these observations for generating next-generation machine algorithms. More recently, the debate has played out in disciplines such as cognitive psychology and linguistics. Symbolic AI was the dominant approach from the s to s, but since then it has been eclipsed by ANN approaches inspired by neuroscience.
Modern ANNs are very similar to their ancestors three decades ago The availability of large data sets is a second factor: Collecting the massive labeled image sets used for training would have been very challenging before the era of Google. Finally, a third reason that modern ANNs are more useful than their predecessors is that they require even less human intervention.
In ANNs, learning refers to the process of extracting structure—statistical regularities—from input data, and encoding that structure into the parameters of the network. These network parameters contain all the information needed to specify the network. There are three classic paradigms for extracting structure from data, and encoding that structure into network parameters i. In supervised learning, the data consist of pairs—an input item e.
In unsupervised learning, the data have no labels; the goal is to discover statistical regularities in the data without explicit guidance about what kind of regularities to look for. For example, one could imagine that with enough examples of giraffes and elephants, one might eventually infer the existence of two classes of animals, without the need to have them explicitly labeled. Much of the progress in ANNs has been in developing better tools for supervised learning.
A network with enough free parameters can fit any function 12 , 13 , but the amount of data required to train a network without overfitting generally also scales with the number of parameters. A network with more flexibility is more powerful, but without sufficient training data the predictions that network makes on novel test examples might be wildly incorrect—far worse than the predictions of a simpler, less powerful network.
The bias-variance tradeoff explains why large networks require large amounts of labeled training data. Although the natural answer may seem to be 10, a fitting function consisting of polynomials of degree 4—a function with five free parameters—might very well predict that the answer is Since we only have data for four points, the next entry could be literally any number e. To get the expected answer, 10, we might restrict the fitting functions to something simpler, like lines, by discouraging the inclusion of higher order terms in the polynomial blue line.
Learning in this context encompasses animal paradigms such as classical and operant conditioning, as well as an array of other paradigms such as learning by observation or by instruction.
Although there is some overlap between the neuroscience and ANN usage of the term learning, in some cases the terms differ enough to lead to confusion. For example, supervised learning is the paradigm that allows ANNs to categorize images accurately.
Although the final result of this training is an ANN with a capability that, superficially at least, mimics the human ability to categorize images, the process by which the artificial system learns bears little resemblance to that by which a newborn learns. There is, thus, a mismatch between the available pool of labeled data and how quickly children learn. Clearly, children do not rely mainly on supervised algorithms to learn to categorize objects. Because unsupervised algorithms do not require labeled data, they could potentially exploit the tremendous amount of raw unlabeled sensory data we receive.
Indeed, there are several unsupervised algorithms which generate representations reminiscent of those found in the visual system 16 , 17 , Although at present these unsupervised algorithms are not able to generate visual representations as efficiently as supervised algorithms, there is no known theoretical principle or bound that precludes the existence of such an algorithm although the No-Free-Lunch theorem for learning algorithms 19 states that no completely general-purpose learning algorithm can exist, in the sense that for every learning model there is a data distribution on which it will fare poorly.
Every learning model must contain implicit or explicit restrictions on the class of functions that it can learn. Discovering such an unsupervised algorithm—if it exists—would lay the foundation for a next generation of ANNs. A central question, then, is how animals function so well so soon after birth, without the benefit of massive supervised training data sets. It is conceivable that unsupervised learning, exploiting algorithms more powerful than any yet discovered, may play a role establishing sensory representations and driving behavior.
But even such a hypothetical unsupervised learning algorithm is unlikely to be the whole story. Indeed, the challenge faced by this hypothetical algorithm is even greater than it appears. Humans are an outlier: We spend more time learning than perhaps any other animal, in the sense that we have an extended period of immaturity. Examples like these suggest that the challenge may exceed the capacities of even the cleverest unsupervised algorithms.
So if unsupervised mechanisms alone cannot explain how animals function so effectively at or soon after birth, what is the alternative? The answer is that much of our sensory representations and behavior are largely innate.
For example, many olfactory stimuli are innately attractive or appetitive blood for a shark 20 or aversive fox urine for a rat Responses to visual stimuli can also be innate. For example, mice respond defensively to looming stimuli, which may allow for the rapid detection and avoidance of aerial predators But the role of innate mechanisms goes beyond simply establishing responses to sensory representations.
Indeed, most of the behavioral repertoire of insects and other short-lived animals is innate. There are also many examples of complex innate behaviors in vertebrates, for example in courtship rituals A striking example of a complex innate behavior in mammals is burrowing: Closely related species of deer mice differ dramatically in the burrows they build with respect to the length and complexity of the tunnels 24 , These innate tendencies are independent of parenting: Mice of one species reared by foster mothers of the other species build burrows like those of their biological parents.
From an evolutionary point of view, it is clear why innate behaviors are advantageous. Each individual is born, and has a very limited time—from a few days to a few years—to figure out how to solve these four problems. If it succeeds, it passes along part of its solution i. Performance here is taken as some measure of fitness, i. All other things being equal e. Evolutionary tradeoff between innate and learning strategies.
All other things being equal, the species relying on a strongly innate strategy will outcompete the species employing a mixed strategy.
In general, however, all other things may not be equal. The mature performance achievable via purely innate mechanisms might not be the same as that achievable with additional learning Fig. If an environment is changing rapidly—e. For example, a fruit-eating animal might evolve an innate tendency to look for trees; but the locations of the fruit groves in its specific environment must be learned by each individual.
There is, thus, pressure to evolve an appropriate tradeoff between innate and learned behavioral strategies, reminiscent of the bias-variance tradeoff in supervised learning. The line between innate and learned behaviors is, of course, not sharp. Innate and learned behaviors and representations interact, often synergistically. The propensity to form place fields is innate: A map of space emerges when young rat pups explore an open environment outside the nest for the very first time However, the content of place fields is learned; indeed, it is highly labile, since new place fields form whenever the animal enters a new environment.
Animal learning and cognition : a neural network approach
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a princi-pled way. This project was a collaboration with Kaz Sato. Spiegelhalter, C. Some algorithms need data that is expertly prepared to exacting. In this book we fo-cus on learning in machines. Well, this machine learning tutorial will clear out all of your confusion! Machine learning is a field of artificial intelligence with the help of which you can perform magic!
In recent years, there has been increased attention to animal minds in philosophical discussions across many areas of metaphysics, epistemology, and value theory. Given that nonhuman animals share some biological and psychological features with humans, and that we share community, land, and other resources, consideration of nonhuman animals has much to contribute to our philosophical activities. Contemporary philosophy of animal minds often also engages with the sciences of animal cognition and behavior. The science of comparative cognition is a thriving area of research, complementing the philosophical study in two ways. For one, philosophers of animal cognition can use claims resulting from animal cognition studies as premises in philosophical discussions.
Scientists create Artificial Neural Networks (ANNs) to make models of the brain. These networks mimic the architecture of a nervous system by connecting.
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Cognitive processes use existing knowledge and discover new knowledge. Cognitive processes are analyzed from different perspectives within different contexts, notably in the fields of linguistics , anesthesia , neuroscience , psychiatry , psychology , education , philosophy , anthropology , biology , systemics , logic , and computer science. The word cognition dates back to the 15th century, where it meant "thinking and awareness". Despite the word cognitive itself dating back to the 15th century,  attention to cognitive processes came about more than eighteen centuries earlier, beginning with Aristotle — BC and his interest in the inner workings of the mind and how they affect the human experience. Aristotle focused on cognitive areas pertaining to memory, perception, and mental imagery.
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1. What is Animal Cognition?
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