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Connectionism Synonyms Connectionist Modeling; (Artificial) Neural Network Modeling; Parallel Distributed Processing (PDP); Neural Nets Definition Connectionism is an interdisciplinary approach to the study of cognition that integrates elements from the fields of Artificial Intelligence, Neuroscience, Cognitive Psychology, and Philosophy of Mind. As a theoretical movement in Cognitive Science, Connectionism suggests that cognitive phenomena can be explained with respect to a set of general information-processing principles, known as Parallel Distributed Processing (Rumelhart, Hinton and McClelland, 1986). From a methodological point of view, Connectionism is a framework for studying cognitive phenomena using architectures of simple processing units interconnected via weighted connections. These architectures present analogies to biological neural systems and are referred to as (Artificial) Neural Networks. Connectionist studies typically propose and implement neural network models to explain various aspects of cognition. The term Connectionism stems from the proposal that cognition emerges in neural network models as a product of a learning process which shapes the values of the weighted connections. Connectionism supports the idea that knowledge is represented in the weights of the connections between the processing units in a distributed fashion. This means that knowledge is encoded in the structure of the processing system, in contrast to the Symbolic approach where knowledge is readily shifted between different memory registers. CONNECTIONIST MODELS OF LANGUAGE ACQUISITION AND PROCESSING Connectionist modeling of language acquisition has attracted strong research interests in the past decades since Rumelhart &McClelland ’s (1986)pioneering model of the acquisition of the English past tense. The claims include that connectionism supplants outmoded Chomskyan (nativist, modular, syntax-dominated) accounts of language; that, as in the physical sciences, connectionism exploits the power of computation to interpret a complex dynamic system; and that connectionist approaches to language have practical utility in areas as diverse as speech recognition and language education. o the connectionist model is like a network. Every word is set in your brain with connections to other words. So when you hear the word cow, you also think of horse and other animals. There is a strong connection between these words semantically. Learning a (new) language is basically improving your network's strength. If the connections between the words are stronger, you should be a better speaker, because you can more easily come up with antonyms, synonyms and other related words, like rhymes. Neural network From Wikipedia, the free encyclopedia Neural network (disambiguation). Simplified view of a feedforward artificial neural network The term neural network was traditionally used to refer to a network or circuit of biological neurons.[1] The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages: 1. Biological neural networks are made up of real biological neurons that are connected or functionally related in a nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis. 2. Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets. This article focuses on the relationship between the two concepts; for detailed coverage of the two different concepts refer to the separate articles: biological neural network and artificial neural network. Connectionism and second language acquisition Connectionism attempts to model the cognitive language processing of the human brain, using computer architectures that make associations between elements of language, based on frequency of co-occurrence in the language input. Frequency has been found to be a factor in various linguistic domains of language learning. Connectionism posits that learners form mental connections between items that co-occur, using exemplars found in language input. From this input, learners extract the rules of the language through cognitive processes common to other areas of cognitive skill acquisition. Since connectionism denies both innate rules and the existence of any innate language-learning module, L2 input is of greater importance than it is in processing models based on innate approaches, since, in connectionism, input is the source of both the units and the rules of language. Interaction hypothesis Long's interaction hypothesis proposes that language acquisition is strongly facilitated by the use of the target language in interaction. In particular, the negotiation of meaning has been shown to contribute greatly to the acquisition of vocabulary In a review of the substantial literature on this topic, Nation relates the value of negotiation to the generative use of words: the use of words in new contexts which stimulate a deeper understanding of their meaning.