In this dissertation, I establish a research program that uses computational modeling as a testbed for theories of phonological learning. This dissertation focuses on a fundamental question: how do children acquire sound patterns from noisy, real-world data, especially in the presence of lexical exceptions that defy regular patterns? For instance,…

Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test the viability of the language exposure hypothesis by assessing whether models…

The last two decades have seen a significant amount of interest in bilingual language learning and processing. A number of computational models have also been developed to account for bilingualism, with varying degrees of success. In this article, we first briefly introduce the significance of computational approaches to bilingual language…

How do children learn the meaning of words like "pretty" and "tall," which are not only gradable and context dependent (Kennedy & McNally 2005), but encode speaker subjectivity? Despite their complex semantics (Stephenson 2007; Lasersohn 2009; Bylinina 2014), these and other adjectives like them, are some of the most…

The use of machine translation has become a topic of debate in language learning, which highlights the need to thoroughly examine the appropriateness and role of machine translation in educational settings. Under the theoretical framework of task-technology fit, this explanatory case study set out to investigate the predictive role of machine…

Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences…

Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up…

Ambridge argues that the existence of exemplar models for individual phenomena (words, inflection rules, etc.) suggests the feasibility of a unified, exemplars-everywhere model that eschews abstraction. The argument would be strengthened by a description of such a model. However, none is provided. I show that any attempt to do so would immediately…

In this commentary, I suggest that it may be helpful to think about the formidable problem space that Westergaard's (2021) Linguistic Proximity Model seeks to address at the three levels of analysis that Marr (1982) famously proposed are needed to understand any complex cognitive system. I argue that at the computational level of analysis, where…

Motivated by the urgent need to investigate the possibilities for re-positioning the human translator and his/her educator in the machine translation (MT) age, this article explores the dynamics of the human-machine dance in the translation classroom. The article discusses the results of a collaborative learning experiment which was conducted in…

Machine translation post-editing (MTPE) has become a common practice in translation industry, which calls much attention in academia. However, little research has been carried out to investigate students' cognitive and motivational individual differences in MTPE. The purpose of the present study was to examine the predictive effects of…

When learning a language, typically-developing infants face the daunting task of learning both the sounds and the meanings of words. In this dissertation, we focus on a source of variability that complicates the one-to-one relationship between words and their meanings: wordform variability. In Chapter 1 we make a distinction between the micro…

Language is one of the hallmarks of intelligence, demanding explanation in a theory of human cognition. However, language presents unique practical challenges for quantitative empirical research, making many linguistic theories difficult to test at naturalistic scales. Artificial neural network language models (LMs) provide a new tool for studying…

While previous investigations on online machine translation (MT) in language learning have analyzed how second language (L2) learners use and post-edit MT output, no study as of yet has investigated how the learners process MT errors and what factors affect this process using response and reading times. The present study thus investigates L2…

Material development is important for training beginner student interpreters, as it guides the direction of interpreting learning. One key principle is difficulty progression, which requires a good knowledge of the indicators of difficulty. Since text structure outweighs words and sentences in the information processing of consecutive…