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)

This paper attempts to articulate a dispositional account of innateness that applies to cognitive capacities. After criticizing an alternative account of innateness proposed by Cowie (1999) and Samuels (2002), the dispositional account of innateness is explicated and defended against a number of objections. The dispositional account states that an innate cognitive capacity (output) is one that has a tendency to be triggered as a result of impoverished environmental conditions (input). Hence, the challenge is to demonstrate how the input can be (...) compared to the output and shown to be relatively impoverished. I argue that there are robust methods of comparing input to output without measuring them quantitatively. (shrink)

Recent work in rational probabilistic modeling suggests that a kind of propositional reasoning is ubiquitous in cognition and especially in cognitive development. However, there is no reason to believe that this type of computation is necessarily conscious or resource-intensive.

This article examines and rejects the claim that 'innateness is canalization'. Waddington's concept of canalization is distinguished from the narrower concept of environmental canalization with which it is often confused. Evidence is presented that the concept of environmental canalization is not an accurate analysis of the existing concept of innateness. The strategy of 'biologicizing the mind' by treating psychological or behavioral traits as if they were environmentally canalized physiological traits is criticized using data from developmental psychobiology. It is concluded that (...) identifying innateness with environmental canalization can only result in adding unhelpful associations from 'folkbiology' to the relatively precise idea of canalization. (shrink)

A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012), error-driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, (...) Denny, & Thorpe, 2010). In error-driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue–outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes—plural and diminutive—co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account—as predicted by domain general error-driven learning. (shrink)

Oneanaphora (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 anaphoricone(a‐one) as a form of syntactic replacement allows us to take a fresh look at various uses of the wordone. 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‐onecan 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 ofa‐onefollow with minimal stipulation. We claim that 1‐oneanda‐oneare distinct but very closely related lexemes. When 1‐oneappears without a noun (e.g.,Takeone), it is nearly indistinguishable froma‐one(e.g.,takeone)—the only differences being interpretive (1‐oneforegrounds its cardinality whilea‐onedoes 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 whichoneappears, the proposed network accounts for properties ofa‐oneby allowing it to inherit most of its syntactic and interpretive constraints from its historical predecessor, 1‐one. (shrink)

It is widely held that children’s linguistic input underdetermines the correct grammar, and that language learning must therefore be guided by innate linguistic constraints. Here, we show that a Bayesian model can learn a standard poverty‐of‐stimulus example, anaphoric one, from realistic input by relying on indirect evidence, without a linguistic constraint assumed to be necessary. Our demonstration does, however, assume other linguistic knowledge; thus, we reduce the problem of learning anaphoric one to that of learning this other knowledge. We discuss (...) whether this other knowledge may itself be acquired without linguistic constraints. (shrink)