To model behavioral and neural correlates of language comprehension in naturalistic environments, researchers have turned to broad-coverage tools from natural-language processing and machine learning. Where syntactic structure is explicitly modeled, prior work has relied predominantly on context-free grammars (CFGs), yet such formalisms are not…

The broader aim of this study is the corpus-based investigation of the written language production process. To this end, temporal markers have been keylog recorded alongside the writing processes to exploit pauses to segment the speech product into linear units of performance. However, identifying these pauses requires selecting the relevant…

Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and usability. With the release of large language models (LLMs), new possibilities have emerged to…

Recent investigations on how people derive meaning from language have focused on task-dependent shifts between two cognitive systems. The symbolic (amodal) system represents meaning as the statistical relationships between words. The embodied (modal) system represents meaning through neurocognitive simulation of perceptual or sensorimotor systems…

Meaning in language emerges from multiple words, and children are sensitive to multi-word frequency from infancy. While children successfully use cues from single words to generate linguistic predictions, it is less clear whether and how they use multi-word sequences to guide real-time language processing and whether they form predictions on the…

This perspective piece explores the transformative potential and associated challenges of large language models (LLMs) in education and how those challenges might be addressed utilizing playful and game-based learning. While providing many opportunities, the stochastic elements incorporated in how present LLMs process text, requires domain…

Speech is a complex, redundant and variable signal happening in a noisy and ever changing world. How do listeners navigate these complex auditory scenes and continuously and effortlessly understand most of the speakers around them? Studies show that listeners can quickly adapt to new situations, accents and even to distorted speech. Although prior…

Rastle et al. (2004) reported that true (e.g., walker) and pseudo (e.g., corner) multi-morphemic words prime their stem words more than form controls do (e.g., brothel priming BROTH) in a masked priming lexical decision task. This data pattern has led a number of models to propose that both of the former word types are "decomposed" into…

Although computational models can simulate aspects of human sentence processing, research on this topic has remained almost exclusively limited to the single language case. The current review presents an overview of the state of the art in computational cognitive models of sentence processing, and discusses how recent sentence-processing models…

Understanding language requires readers and listeners to cull meaning from fast-unfolding messages that often contain conflicting cues pointing to incompatible ways of interpreting the input (e.g., "The cat was chased by the mouse"). This article reviews mounting evidence from multiple methods demonstrating that cognitive control plays…

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,…

Semantic composition in language must be closely related to semantic composition in thought. But the way the two processes are explained differs considerably. Focusing primarily on propositional content, language theorists generally take semantic composition to be a truth-conditional process. Focusing more on extensional content, cognitive…

Subject-verb agreement errors are common in sentence production. Many studies have used experimental paradigms targeting the production of subject-verb agreement from a sentence preamble ("The key to the cabinets") and eliciting verb errors (… "*were shiny"). Through reanalysis of previous data (50 experiments; 102,369…

Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec -- which have been extremely successful in modern natural language processing (NLP) applications -- count? Although these models often have ample parametric complexity to store exemplars from their training data, they also…

Many datasets resulting from participant ratings for word norms and also concreteness ratios are available. However, the concreteness information of infrequent words and non-words is rare. This work aims to propose a model for estimating the concreteness of infrequent and new lexicons. Here, we used Lancaster sensory-motor word norms to predict…