As a result of a post What makes Humans Tick on Babel's Dawn my attention has been drawn to a paper Language Has Evolved to Depend on Multiple-Cue Integration by Morten H. Christiansen to appear in R. Botha & M. Everaert (Eds). The Evolutionary Emergence of Human Language, Oxford, Oxford University Press.
In discussing Language as a Culturally Evolved Linguistic System Morten writes:
A key question for language evolution research is to explain why language is the way it is, and how it got to be that way. The cultural evolution perspective suggests that the structure of language derives primarily from processes of cultural transmission involving repeated cycles of learning and use, constrained by the properties of the human brain. Thus, instead of asking, “Why is the brain so well-suited for learning language?”, we need to turn the question upsidedown and ask, “Why is language so well-suited to being learned by the brain?”
He goes on to say
Consequently, what has evolved is not a set of neural structures specific to language; rather, cultural evolution produces a system of linguistic constructions specific to a given speech community (i.e., a language).
This is really saying what I am trying to say on this blog – but coming from a very different direction.
The basic idea which underlies CODIL started as an attempt to design a sales accounting system in 1967 which would handle perhaps a quarter of a million very different customers, and perhaps 5,000 different products in a dynamic market. Details of the CODIL research and the sad reasons why the project eventually folded are given elsewhere on this blog.
To summaries the key factors: CODIL uses a conseptually very simple recursive set structure to represent information (there is no formal distinction between program and data) and a very simple “decision making unit” which scans through the stored information. Despite this simplicity it has been demonstrated to work, albeit sometimes on a small scale, on a wide range of non-numerical applications including some which would classify as artificial intelligence, or involve fuzzy logic or dynamic learning.
Its relevance to the evolution of language (I will be posting further information about this in September) is that it appears that both the information structure and the processing routines will translate comparatively easily from the sequential central process of the original model onto a neural net where the amount of information done by any one node is acceptably small. It could well be that the processing power already demonstrated in the original sequential model can be reproduced in a neural net and is sufficient to support many aspect of human natural language.
If a CODIL type model can be sustained for the inner workings of the brain and can be extended to support the plethora of human languages, this would fully demonstrate the feasibility of the ideas represented in the above quotations.
However don't expect me to come up with all the answers. I am an old age pension who retired from university research over 20 years ago, with deteriorating eyesight, and with no access to academic library or computer facilities. As far as I am concerned this blog site will have been successful if I can persuade someone younger and fitter than I am, and who has adequate resources, to follow up the ideas.