Traditional studies of language assume an atomistic model in which linguistic signals comprise discrete, minimal form elements associated with discrete, minimal elements of meaning. Since production has been seen to involve the composition of messages from an inventory of form elements, and comprehension the subsequent decomposition of these messages, researchers have focused on attempting to identify and classify these elements, and the lossless processes of composition and decomposition they support, a program that has raised more questions than answers, especially when it comes to the nature of form-meaning associations.
By contrast, behavioral and neuroscience research based on human and animal models has revealed that “associative learning” is a lossy, discriminative process. Learners acquire predictive understandings of their environments through competitive mechanisms that tune systems of internal representations to eliminate or reduce any uncertainty they promote.
In this talk, I will describe some empirical results that indicate that human communication is subject to the constraints that the basic principles of learning impose, and describe how, from this perspective, languages should be seen as discriminative communication systems that exhibit continuous variation within a multidimensional space of form-meaning contrasts. In illustrating how this process works, I will show how a discriminative approach to communication makes sense of many aspects of language that have long seemed puzzling, such as noun class systems (aka grammatical gender) and the semantics of personal names. I will show how noun class and personal name systems are neither random nor arbitrary, and that they actually represent highly structured and highly evolved linguistic subsystems that optimize the discriminative processes of communication.
