General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory--the component of short-term memory used for temporary…

Despite OT's success, opaque alternations prove difficult to capture with constraints, and some violate the theory's formal restrictions. Here, I propose a novel account of opacity drawing upon developments in psychology. Rather than one grammar, I propose a dual-system model with implicit and explicit mechanisms, a domain-specific OT-like system…

Foreign accents can vary considerably in the degree to which they deviate from the listener's native accent, but little is known about how the relationship between a speaker's accent and a listener's native language phonology mediates adaptation. Using an artificial accent methodology, we addressed this issue by constructing a set of three…

In the age of big data and analytics, it is important that students learn about artificial intelligence (AI) and machine learning (ML). Machine learning is a discipline that focuses on building a computer system that can improve itself using experience. ML models can be used to detect patterns from data and recommend strategic marketing actions.…

From limited evidence, children track the regularities of their language impressively fast and they infer generalized rules that apply to novel instances. This study investigated what drives the inductive leap from memorizing specific items and statistical regularities to extracting abstract rules. We propose an innovative entropy model that…

Adjuncts and arguments exhibit different syntactic behaviours, but modelling this difference in minimalist syntax is challenging: on the one hand, adjuncts differ from arguments in that they are optional, transparent, and iterable, but on the other hand they are often strictly ordered, reflecting the kind of strict selection seen in argument…

In natural language, evidence suggests that, while some rules are productive (regular), applying broadly to new words, others are restricted to a specific set of lexical items (irregular). Further, the literature suggests that children make a categorical distinction between regular and irregular rules, applying only regular rules productively…

Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of…

Word frequencies in natural language follow a highly skewed Zipfian distribution, but the consequences of this distribution for language acquisition are only beginning to be understood. Typically, learning experiments that are meant to simulate language acquisition use uniform word frequency distributions. We examine the effects of Zipfian…

Endress and Mehler (2009) reported that when adult subjects are exposed to an unsegmented artificial language composed from trisyllabic words such as ABX, YBC, and AZC, they are unable to distinguish between these words and what they coined as the "phantom-word" ABC in a subsequent test. This suggests that statistical learning generates knowledge…

In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for…

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…

Humans are remarkably sensitive to the statistical structure of language. However, different mechanisms have been proposed to account for such statistical sensitivities. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks, which learn by…

Why do adult language learners typically fail to acquire second languages with native proficiency? Does prior linguistic experience influence the size of the "units" adults attend to in learning, and if so, how does this influence what gets learned? Here, we examine these questions in relation to grammatical gender, which adult learners almost…

We present data from an artificial language domain that suggest new contributions to the theory of syntactic triggers. Whether a learning algorithm is capable of matching the achievements of child learners depends in part on how much parametric ambiguity there is in the input. For practical reasons this cannot be established for the domain of all…