Connectionism

Abstract

Connectionism, also known as parallel distributed processing (PDP) or artificial neural networks, and most recently reengineered as Deep Learning, has been an important theoretical framework as well as a computational tool for the study of mind and behavior. It adopts the perspective that human cognition is an emergent property that is due to the interaction of a large number of interconnected processing units (neurons) that operate simultaneously in a network (thus “parallel”). In addition, connectionism advocates that learning, representation, and processing of information are dynamic and distributed. Language as a hallmark of human behavior has received in-depth treatment since the beginning of connectionist research. The acquisition of morphosyntax, the recognition of speech, and the processing of sentences are among the studies of the earliest connectionist models. The application of connectionism to second language acquisition has also gathered momentum in the late 20th and early 21st centuries. Learning a language entails complex cognitive and linguistic constraints and interactions, and connectionist models provide insights into how these constraints and interactions may be realized in the natural learning context.