Source codes in human communication

Abstract

Although information theoretic characterizations of human communication have become increasingly popular in linguistics, to date they have largely involved grafting probabilistic constructs onto older ideas about grammar. Similarities between human and digital communication have been strongly emphasized, and differences largely ignored. However, some of these differences matter: communication systems are based on predefined codes shared by every sender/receiver, whereas the distributions of words in natural languages guarantee that no speaker/hearer ever has access to an entire linguistic code, which seemingly undermines the idea that natural languages are probabilistic systems in any meaningful sense. This paper describes how the distributional properties of languages meet the various challenges arising from the differences between information systems and natural languages, along with the very different view of human communication these properties suggest. To illustrate, it presents an account of the semantic function of personal names (a traditional linguistic stumbling block), describing the non-compositional, discriminative communicative process supported by the statistical and syntactic properties of names in the world's languages. It also describes how this account extends to the rest of human communication, and describes how its predictions are supported by analyses the distribution of English nouns and verbs.

How the Probabilistic Structure of Grammatical Context Shapes Speech

“…This property of speech codes is often taken to suggest that human speech is shaped by the competing requirements of maximizing the success of message transmission while minimizing production effort in ways similar to those described by information coding solutions for electronic message transmission. There are, however, some critical differences between speech and the communication model described by information theory [ 3 ]: whereas information theory is concerned with defining the properties of variable length codes optimized for efficient communication in discrete, memoryless systems, human communication codes, at first blush at least, appear neither systematic [ 3 ] nor systematically discrete [ 2 , 4 ] or memoryless [ 5 ].…”

“…Recent work by Ramscar [ 3 ] addresses these apparent communication problems from the perspective of discriminative learning and suggests that, unlike the predefined source codes in artificial communication, human communicative codes are subcategorized by systematic patterns of variation in the way words and arguments are employed. The empirical distributions discriminated by these patterns of variation both serve to minimize communicative uncertainty and to make unattested word forms predictable in context, thereby overcoming some of the problems that arise from the way that linguistic codes are sampled.…”

“…A notable finding in this regard comes from an analysis of names (a universal feature of communicative codes that is almost universally ignored by grammatical theories) and in particular the distributions of English and Sinosphere first names [ 3 , 11 ]. This analysis shows that, historically, prior to the imposition of name laws that distorted their initial distributions, first names across a range of cultures had near identical geometric distributions.…”

“…The 20th century is characterized by large global increases in population sizes; that is, the number of individuals that name distributions must serve to discriminate between has increased. In western cultures, this has had two consequences: first, the fixing of last names has caused the increase in information in the distribution of first names in direct proportion to increases in population [ 3 , 11 ]. Second, it has led to an increase in the diversity of regional first name distributions across very large states such as the United States.…”

“…Second, it has led to an increase in the diversity of regional first name distributions across very large states such as the United States. An interesting consequence of this is that, although the first name distribution in the US as a whole follows a power law, the distribution of names in the individual states still show close fits to the geometric, indicating that the shape of the former distribution may reflect the result of the aggregation of the latter [ 3 , 8 ].…”