Bilingualism: Language and Cognition’s 2013 Special Issue features computational modeling studies of bilingualism and second language acquisition. Seven research papers illustrate seven different but highly related computational models designed to understand the workings of the bilingual mind from a cognitive science perspective. This Special Issue fills a large gap in the literature, in that the specific, algorithmically implemented, models of bilingualism provide a good variety of computational architectures, cover a range of theoretical issues, and analyze both spoken and written languages across different bilingual populations. Moreover, they integrate theories and mechanisms of learning, representation, and development in order to account for a variety of phenomena, in bilingual aphasia, lexical memory, word translation, grammatical acquisition, speech perception, and reading development.
Readers of this Special Issue will be convinced that computational models have much to offer to the understanding of the bilingual mind, over and beyond what general verbal, hypothesis-driven, models can do. Implementation of computational models forces the researcher to be very explicit about their hypotheses, predictions, materials, and testing procedures, and at the same time, gives the flexibility of parameter selection and reliability of testing that are often not found in empirical studies. Indeed, the potential of a bilingual computational model lies in its ability to identify gaps in experimental designs, and in systematic manipulation of variables such as age of acquisition (early vs. late), proficiency (high vs. low), and memory resources (large vs. small), variables that may be naturally confounded in experimental or realistic learning situations.
The seven models presented in this Special Issue demonstrate the advantages and the need for developing more computational models of bilingualism, as they deepen our understanding of the complex interactive mechanisms involved in the acquisition and processing of multiple competing linguistic systems. For example, the effects of dynamic interactions in the competing languages at different times of learning can be clearly simulated, providing alternative accounts of the critical period effects from the perspectives of competition, entrenchment, and plasticity. These models examine the extent to which early learning impacts later learning and the extent later learning can soften or even reverse early-learned structures. In addition to simulating known patterns in the empirical data, the computational models presented here will also inform theories of bilingualism by making distinct predictions under different hypotheses or conditions. In so doing, they will provide a new forum for generating novel ideas, inspiring new experiments, and helping formulate new theories.
Blog post written by Ping Li, Pennsylvania State University, USA