The Implications of Connectionism and Neural Networks for Simultaneous Interpreting
US-China Foreign Language
The aim of this paper is to approach simultaneous interpreting adopting some connectionist models and conceptions and, thus, nurture interpreting modeling. Using neural networks stimulations, connectionism looks at the brain as a network of inter-connected nodes/units that substrate any intelligent ability or cognitive process through the propagation of activation. Neural networks, in connectionist models, acquire and learn diverse cognitive patterns through the strengthening and weakening of
... and weakening of nodes associations. Drawing on this seminal framework, this paper is an attempt to account for interpreting and novice interpreters as neural networks. Throughout the matrix of argumentation, we suggest that the accumulation of a number of input units in novice interpreters' neural nodes, as micro agents for processing informational units, and the building of the necessary knowledge associations among such nodes, through the strengthening and weakening principles, are prerequisites for being an interpreter. The establishment of a huge network of knowledge nodes enables the network to interpret in a given velocity. More to the point, such endeavor entails an extensive training on the propagation of a distributed neural activation in distributed circuits. Such neural circuits give emerge to the required neural associations echoing the declarative and procedural abilities crucial for interpreting as a quasi-automatic activity.