This chapter discusses different theoretical perspectives on modeling language understanding and learning. The first section discusses the role of rules in learning and understanding a language. In the second section, we introduce Fodor's argument that natural language learning requires an innate representational system which he calls the language of thought. The third section looks at a very different approach involving connectionist neural networks. Connectionists argue that the trajectory of language learning can be simulated in networks that lack explicitly encoded linguistic rules – e.g. in neural networks that can be trained to learn the tenses of English words. The last section looks at the Bayesian approach to language learning.

This chapter looks back to the pioneering studies that prefigured the emergence of cognitive science, subsequently converging into this new interdisciplinary field in the late 1970s. The first section addresses a key turning point in psychology. Whereas behaviorism holds that all explicit behaviors are the product of conditioning, it became clear that animals can manipulate representations of the environment to solve complex problems without reinforcement. The second section introduces the Turing machine developed by Turing in the 1930s, illustrating that purely mechanical procedures can process information algorithmically. The idea of a computable machine contributed to the birth of computer science and provided a model for thinking about how the mind processes information. Chomsky's transformational grammar offers a classic example of a computable model of how complex sentences convey information as a function of basic syntax rules. Finally, Miller and Broadbent's findings on attention support applying the information-processing model in psychology. These pioneering researchers were the first to lay out some of what were to be the basic concepts of cognitive science.