Toward a “Standard Model” of Early Language Learning
A "standard model" is a theoretical framework that synthesizes observables into a quantitative consensus. Have we made progress towards this kind of synthesis for children’s early language learning? Many computational models of early vocabulary learning assume that individual words are learned through an accumulation of environmental input. This assumption is also implicit in empirical work that emphasizes links between language input and learning outcomes. However, models have typically focused on average performance, while empirical work has focused on variability. To model individual variability, we relate the tradition of research on accumulator models to Item-Response Theory models from psychometrics. This formal connection reveals that currently available datasets cannot allow us to fully test these models, illustrating a critical need for theory in shaping new data collection and in creating and testing an eventual "standard model."