Bridging language with the rest of cognition: computational, algorithmic and neurobiological issues and methods

The computational program for theoretical neuroscience initiated by Marr and Poggio (1977) calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels — computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational — significant progress has been made in the understanding of cognition. In the past three decades, computational principles have been discovered that are common to a wide range of functions in perception (vision, hearing, olfaction) and action (motor control). More recently, these principles have been applied to the analysis of cognitive tasks that require dealing with structured information, such as visual scene understanding and analogical reasoning. Insofar as language relies on cognition-general principles and mechanisms, it should be possible to capitalize on the recent advances in the computational study of cognition by extending its methods to linguistics. The possibility of integrating linguistics into a unified science of cognition — a desideratum put forward in many of the relevant disciplines — depends on the degree to which common computational principles (Marr and Poggio, 1977) and brain mechanisms are shared by language and by the other cognitive functions. To explore this possibility, we need to bring together ideas from several fields, which as yet have seen little intellectual cross-fertilization. The first of these is cognitive linguistics (Langacker, 1987; Bernárdez, 1999) — a natural home discipline for the integration project, which consistently produces valuable insights into the psychology of language, yet is little concerned with algorithmic or implementational issues. The second is computational linguistics (Jurafsky and Martin, 2000), including statistical natural language processing