In recent years, Bayesian models have become increasingly popular as a way of understanding human cognition. Ideal learner Bayesian models assume that cognition can be usefully understood as optimal behavior under uncertainty, a hypothesis that has been supported by a number of modeling studies across various domains (e.g., Griffiths and Tenenbaum, Cognitive Psychology, 51, 354-384, 2005; Xu and Tenenbaum, Psychological Review, 114, 245-272, 2007). The models in these studies aim to explain why humans behave as they do given the task and data they encounter, but typically avoid some questions addressed by more traditional psychological models, such as how the observed behavior is produced given constraints on memory and processing. Here, we use the task of word segmentation as a case study for investigating these questions within a Bayesian framework. We consider some limitations of the infant learner, and develop several online learning algorithms that take these limitations into account. Each algorithm can be viewed as a different method of approximating the same ideal learner. When tested on corpora of English child-directed speech, we find that the constrained learner's behavior depends non-trivially on how the learner's limitations are implemented. Interestingly, sometimes biases that are helpful to an ideal learner hinder a constrained learner, and in a few cases, constrained learners perform equivalently or better than the ideal learner. This suggests that the transition from a computational-level solution for acquisition to an algorithmic-level one is not straightforward.
We identify three components of any learning theory: the representations, the learner's data intake, and the learning algorithm. With these in mind, we model the acquisition of the English anaphoric pronoun one in order to identify necessary constraints for successful acquisition, and the nature of those constraints. Whereas previous modeling efforts have succeeded by using a domain-general learning algorithm that implicitly restricts the data intake to be a subset of the input, we show that the same kind of domain-general learning algorithm fails when it does not restrict the data intake. We argue that the necessary data intake restrictions are domain-specific in nature. Thus, while a domain-general algorithm can be quite powerful, a successful learner must also rely on domain-specific learning mechanisms when learning anaphoric one.
Parametric systems have been proposed as models of how humans represent knowledge about language, motivated in part as a way to explain children's rapid acquisition of linguistic knowledge. Given this, it seems reasonable to examine if children with knowledge of parameters could in fact acquire the adult system from the data available to them. That is, we explore an argument from acquisition for this knowledge representation. We use the English metrical phonology system as a nontrivial case study and test several computational models of unbiased probabilistic learners. Special attention is given to the modeled learners' input and the psychological plausibility of the model components in order to consider the learning problem from the perspective of children acquiring their native language. We find that such cognitively inspired unbiased probabilistic learners uniformly fail to acquire the English grammar proposed in recent metrical studies from English child-directed speech, suggesting that probabilistic learning alone is insufficient to acquire the correct grammar when using this parametric knowledge representation. Several potential sources of this failure are discussed, along with their implications for the parametric knowledge representation and the trajectory of acquisition for English metrical phonology.
Plural definite descriptions (e.g. the things on the plate) and free relative clauses (e.g. what is on the plate) have been argued to share the same semantic properties, despite their syntactic differences. Specifically, both have been argued to be non-quantificational expressions referring to the maximal element of a given set (e.g. the set of things on the contextually salient plate). We provide experimental support for this semantic analysis with the first reported simultaneous investigation of children's interpretation of both constructions, highlighting how experimental methods can inform semantic theory. A Truth-Value Judgment task and an Act-Out task show that children know that the two constructions differ from quantificational nominals (e.g. all the things on the plate) very early on (4 years old). Children also acquire the adult interpretation of both constructions at the same time, around 6-7 years old. This happens despite major differences in the frequency of these constructions, according to our corpus study of children's linguistic input. We discuss possible causes for this late emergence. We also argue that our experimental findings contribute to the recent theoretical debate on the correct semantic analysis of free relatives.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations鈥揷itations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.