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…

Lexical inferencing is considered a listening strategy that is commonly employed by advanced EFL (English as a Foreign Language) listeners and a factor that contributes to successful listening comprehension. However, investigations of the factors that influence inferencing success in listening as well as how much each factor contributes to success…

People often use different types of available information as cues to make inferences about memory (Koriat, 1997). When these cues are unreliable predictors of memory (like font size), metacognitive illusions can ensue (Mueller, Dunlosky, Tauber, & Rhodes, 2014). Extending upon Rhodes and Castel's (2008) findings that perceptual fluency cues…

This research examines issues regarding model estimation and robustness in the use of Bayesian Inference Networks (BINs) for measuring Learning Progressions (LPs). It provides background information on LPs and how they might be used in practice. Two simulation studies are performed, along with real data examples. The first study examines the case…

Stochastic understanding of probability distribution undergirds development of conceptual connections between probability and statistics and supports development of a principled understanding of statistical inference. This study investigated the impact of an instructional course intervention designed to support development of stochastic…