Category: Epistemology and Method

Thought and Language. It’s entirely possible to think without language. But when we use language in our thinking we can calculate with much greater commensurability, much greater greater precision, much greater density, than we can when just imagining – just as when we use writing and symbols we can calculate with greater commensurability, greater precision, and greater density. language produces symbols in the mind that allow greater computational efficiency, just as symbols we compose in the real world produce greater computational efficiency, just as formulae and databases produce greater computational efficiency. The question is why our brains can use ‘names’ to create a stack of concepts (although very limited) that we can compare relatively accurately, the way our use of written marks (symbols) lets us reference whole stories accurately. Chomsky isn’t quite right that we can’t say anything abut thought without language. It’s that some of us can preserve greater short term state (memory) they way some of us can compose music, memorize long sets of number, practice doing mathematical calculations, memorize lines of a script or poem, than can others. Just as some of us can compose only phrases, some sentences, some arguments, and others long explanatory narratives. Thought consists, as does language, (and all grammars) of continuous recursive disambiguation, and symbols (names) allow us to compare, and language (streams of words) allow us to continuously manufacture different lengths of memory, to produce different lengths of forecasts (imagination). In computers we think of buffers. In electronics, capacitors and ballasts. In hydraulics, reservoirs. But for thoughts we use short term memory: the current context, currently revised, as new information is added, new forecasts made, in an ongoing process of continuous recursive disambiguation. What we have seen since the 1990’s is the slow replacement of the idea of computational efficiency with the introduction (thankfully, and finally) of economics – which accounts for time and effort necessary to produce a continuous stream speech in real time. We have also seen the increasing use of of ‘neural economy’, which also brings demand, supply, and time into the discourse as the (correct) replacement for efficiency.

Thought and Language. It’s entirely possible to think without language. But when we use language in our thinking we can calculate with much greater commensurability, much greater greater precision, much greater density, than we can when just imagining – just as when we use writing and symbols we can calculate with greater commensurability, greater precision, and greater density. language produces symbols in the mind that allow greater computational efficiency, just as symbols we compose in the real world produce greater computational efficiency, just as formulae and databases produce greater computational efficiency. The question is why our brains can use ‘names’ to create a stack of concepts (although very limited) that we can compare relatively accurately, the way our use of written marks (symbols) lets us reference whole stories accurately. Chomsky isn’t quite right that we can’t say anything abut thought without language. It’s that some of us can preserve greater short term state (memory) they way some of us can compose music, memorize long sets of number, practice doing mathematical calculations, memorize lines of a script or poem, than can others. Just as some of us can compose only phrases, some sentences, some arguments, and others long explanatory narratives. Thought consists, as does language, (and all grammars) of continuous recursive disambiguation, and symbols (names) allow us to compare, and language (streams of words) allow us to continuously manufacture different lengths of memory, to produce different lengths of forecasts (imagination). In computers we think of buffers. In electronics, capacitors and ballasts. In hydraulics, reservoirs. But for thoughts we use short term memory: the current context, currently revised, as new information is added, new forecasts made, in an ongoing process of continuous recursive disambiguation. What we have seen since the 1990’s is the slow replacement of the idea of computational efficiency with the introduction (thankfully, and finally) of economics – which accounts for time and effort necessary to produce a continuous stream speech in real time. We have also seen the increasing use of of ‘neural economy’, which also brings demand, supply, and time into the discourse as the (correct) replacement for efficiency.