Reinterpreting first language phonetic acquisition through data mining a case study

The “constrained statistical learning framework1” was proposed by J. Saffran to reshape the long-standing debate on language acquisition between Nativism and Empiricism. By defining the concept of “statistically biased learning mechanisms2” she posits an alternative way to account to the so-called “linguistic genius” of babies: by conceiving ground-breaking experimental designs, she shed light on how babies are able to spontaneously identify meaningful sequences and statistical regularities comparing natural and artificial languages3. According to her “it is possible that a combination of inherent constraints on the type of patterns acquired by learners, and the use of output from one level of learning as input to the next, may help to explain why something so complex is mastered readily by the human mind4”. By following this route, goal of this research proposal is to test whether and how “any variation doesn’t randomly vary in any other, but it rather should follow an underlying pattern, as every variation has an order in itself 5”.