Connectionist Approaches to Language Learning
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Chapter 6 : Connectionist Approaches
Chapter 6 : Connectionist Approaches 1. Competition Model Cont. There are many forms of connectionism, but the most common forms use neural network models. In Stock. Superintelligence Paths, Dangers and Stragegies. Bayesian Time Series Models. Deep Learning. Cost-Sensitive Machine Learning.
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These changes occur at three levels: among genes, among molecules, and within neuronal environments. Moreover, modifications also occur during ontogeny without external input from outside the organism p. Still, Pinker , a strong advocate of nativism, argues that it is precisely those genes which are involved in those processes and which act upon language acquisition what they have been referring as innate grammar genes p. Yet, Elman et al. In other words, morphosyntax was not what was impaired.
Hence, maintaining a pervasive obstination in the grammar gene issue could be misleading and detrimental p.
Still, there are other issues, such as the role of interaction over developmental processes, that are helping to define the new role of innateness. For instance, Elman et al. Henceforth, the problem is not one of nature over nurture, or nurture over nature, but one of nature and nurture.
In sum, lower levels of circuitry are not innate, whereas macrocircuitry may well be.
- Other Subject Areas;
- 2.0 PRINCIPLES OF CONNECTIONISM.
- Connectionist Approaches to Language Learning;
Still, neuroscientists would have not been able to confirm these assumptions without the help of another newcomer: computer simulated networks. For example, this approach has been crucial in the reevaluation and reinterpretation of important issues in both cognition and language acquisition such as the Critical Period Hypothesis, and overregularization of English past-tense verbs, among others By way of example, Marchman cited in Elman et al.
Finally, the network changed its original structure up to a point where it could no longer start again to relearn the task as usually computer networks do. As Seidenberg , suggests, it is a matter of using it or losing it. This experiment guided Elman et al. Incidentally, from these findings, scientists are concluding that noise might be beneficial because it can keep the brain from sculpting too early, which might prevent it from further learning.
Hence, connectionist models are bringing light into the Critical Period issue. Moreover, these scientists suggest that the term Critical Period should be replaced by a more subtle term: Sensitive Period. Accordingly, the term Critical Period refers to a non-specific point in the development process in which organisms are sensitive to experience, and furthermore, these periods do not exhibit drastic termination points p.
Likewise, connectionist models have helped to solve the puzzle of overregularization of English past-tense verbs. Later, with further acquisition of more vocabulary, children tend to 8 6 overrregularize already learned irregular verbs until, finally, they acquire adult commandship of the language. Mac Whinney and Leinbach , refer to this phenomena as the U-shaped learning problem p. Indeed, computer networks experiments have exhibited that same behavior. For example.
Hare and Elman cited in Elman et al. In their quest for answers on how language is acquired and used, connectionists have come with a possible explanation of how language is produced. Put in the simplest possible form, languages represent solutions to the problem of mapping inherently nonlinear patterns of thought onto a linear sequence of signals, under a severe set of processing constraints from human perception, motor coordination and production, and memory, p. However, the search for a theory of second language acquisition SLA , is still unanswered.
Klein , acknowledges that UG has nothing to offer to SLA research, and proposes that connectionism should be the ideal search framework p. Eventually, connectionist approaches to SLA should address those assumptions drawn from present findings while scientists humbly acknowledge to be in search for better ideas Elman et al. In conclusion, connectionist approaches to language acquisition are accumulating impressive amounts of data on how language is acquired and used, and how this knowledge is 10 8 represented in the brain.