Natural language is full of patterns that appear to fit with general linguistic rules but are ungrammatical. There has been much debate over how children acquire these “linguistic restrictions,” and whether innate language knowledge is needed. Recently, it has been shown that restrictions in language can be learned asymptotically via probabilistic inference using the minimum description length (MDL) principle. Here, we extend the MDL approach to give a simple and practical methodology for estimating how much linguistic data are required to (...) learn a particular linguistic restriction. Our method provides a new research tool, allowing arguments about natural language learnability to be made explicit and quantified for the first time. We apply this method to a range of classic puzzles in language acquisition. We find some linguistic rules appear easily statistically learnable from language experience only, whereas others appear to require additional learning mechanisms (e.g., additional cues or innate constraints). (shrink)

One anaphora (e.g., this is a good one) has been used as a key diagnostic in syntactic analyses of the English noun phrase, and “one‐replacement” has also figured prominently in debates about the learnability of language. However, much of this work has been based on faulty premises, as a few perceptive researchers, including Ray Jackendoff, have made clear. Abandoning the view of anaphoric one (a‐one) as a form of syntactic replacement allows us to take a fresh look at various uses (...) of the word one. In the present work, we investigate its use as a cardinal number (1‐one) in order to better understand its anaphoric use. Like all cardinal numbers, 1‐one can only quantify an individuated entity and provides an indefinite reading by default. Owing to unique combinatoric properties, cardinal numbers defy consistent classification as determiners, quantifiers, adjectives, or nouns. Once the semantics and distribution of cardinal numbers, including 1‐one, are appreciated, many properties of a‐one follow with minimal stipulation. We claim that 1‐one and a‐one are distinct but very closely related lexemes. When 1‐one appears without a noun (e.g., Take one), it is nearly indistinguishable from a‐one (e.g., take one)—the only differences being interpretive (1‐one foregrounds its cardinality while a‐one does not) and prosodic (presence versus absence of primary accent). While we ultimately argue that a family of constructions is required to describe the full range of syntactic contexts in which one appears, the proposed network accounts for properties of a‐one by allowing it to inherit most of its syntactic and interpretive constraints from its historical predecessor, 1‐one. (shrink)

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.