In Manolo Martinez's recent talk at CUNY on the Rationalizing Role of Pains, he presented a model describing the decision-making process of a mind faced with multiple conflicting sensory messages of varying reliability. This model was by no means the focus of his talk, but it is what I'd like to focus on here.
One of the primary goals of contemporary neuroscience is the reverse-engineering of the brain's functional architecture. Our understanding has evolved from descriptive to functional, particularly through borrowing ideas from computer science and information theory. V. Balasubramanian and P. Sterling's paper explains several aspects of retinal design using information- and selection-theoretic arguments in conjunction with computer simulation.
Of the many machine learning models, the artificial neural network (ANN) is of particular interest because of the obvious analogy to the function of the brain. However, the standard supervised cost function and error back-propagation algorithm are entirely implausible from a biological perspective, and in practice the performance of back-prop decreases sharply with the number of hidden-layers, requiring more and more labeled training examples which are often in short supply.
As is the case in the physical sciences, I believe that linguistics proceeds in a pattern of punctuated equilibrium, those punctuations being paradigm shifts whereby the foundations of accepted theory are rewritten. In this way old mysteries become puzzles, explanations become more elegant, and new technological advances are enabled. Here I will briefly sketch the idea that generative linguistics should be reformulated in terms of quantitative neuroscience, and that such a reformulation will be immensely fruitful.
A method for aligning a transcript to recorded speech is described, motivated by the need for alignment of audiobooks for use by SLA software. A combination of text-to-speech technology, mel-spectrum feature extraction, and dynamic time warping are employed to obtain a word-alignment for the input speech sample.
This week I have been reading up on syntax. I started with Baker's The Atoms of Language, continued with Chomsky's Syntactic Structures, and have just finished the syntax chapter of the textbook Contemporary Linguistics (3rd edition). I have learned a great deal thus far, but the more I read the more questions I have. I write these notes into the margins of my books, but I now feel the need to post the more salient questions for later reference, lest they get buried when I continue my linguistic inquiry.