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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.