A new way to identify if variation in children's input could be developmentally meaningful: Using computational cognitive modeling to assess input across socio-economic status for syntactic islands.

While there are always differences in children's input, it is unclear how often these differences impact language development - that is, are developmentally meaningful - and why they do (or do not) do so. We describe a new approach using computational cognitive modeling that links children's input to predicted language development outcomes, and can identify if input differences are potentially developmentally meaningful. We use this approach to investigate if there is developmentally-meaningful input variation across socio-economic status (SES) with respect to the complex syntactic knowledge called syntactic islands. We focus on four island types with available data about the target linguistic behavior. Despite several measurable input differences for syntactic island input across SES, our model predicts this variation not to be developmentally meaningful: it predicts no differences in the syntactic island knowledge that can be learned from that input. We discuss implications for language development variability across SES.

Computational cognitive modeling for syntactic acquisition: Approaches that integrate information from multiple places.

Computational cognitive modeling is a tool we can use to evaluate theories of syntactic acquisition. Here, I review several models implementing theories that integrate information from both linguistic and non-linguistic sources to learn different types of syntactic knowledge. Some of these models additionally consider the impact of factors coming from children's developing non-linguistic cognition. I discuss some existing child behavioral work that can inspire future model-building, and conclude by considering more specifically how to build better models of syntactic acquisition.

Theory and predictions for the development of morphology and syntax: A Universal Grammar + statistics approach.

The key aim of this special issue is to make developmental theory proposals concrete enough to evaluate with empirical data. With this in mind, I discuss proposals from the "Universal Grammar + statistics" (UG+stats) perspective for learning several morphology and syntax phenomena. I briefly review why UG has traditionally been part of many developmental theories of language, as well as common statistical learning approaches that are part of UG+stats proposals. I then discuss each morphology or syntax phenomenon in turn, giving an overview of relevant UG+stats proposals for that phenomenon, specific predictions made by each proposal, and what we currently know about how those predictions hold up. I conclude by briefly discussing where we seem to be when it comes to how well UG+stats proposals help us understand the development of morphology and syntax knowledge.

Computational Modeling for Language Acquisition: A Tutorial With Syntactic Islands.

PURPOSE: Given the growing prominence of computational modeling in the acquisition research community, we present a tutorial on how to use computational modeling to investigate learning strategies that underlie the acquisition process. This is useful for understanding both typical and atypical linguistic development. METHOD: We provide a general overview of why modeling can be a particularly informative tool and some general considerations when creating a computational acquisition model. We then review a concrete example of a computational acquisition model for complex structural knowledge referred to as syntactic islands. This includes an overview of syntactic islands knowledge, a precise definition of the acquisition task being modeled, the modeling results, and how to meaningfully interpret those results in a way that is relevant for questions about knowledge representation and the learning process. CONCLUSIONS: Computational modeling is a powerful tool that can be used to understand linguistic development. The general approach presented here can be used to investigate any acquisition task and any learning strategy, provided both are precisely defined.

The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation.

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate cognitive plausibility by using an age-appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition.