Validation of an open source, remote web-based eye-tracking method (WebGazer) for research in early childhood.

Measuring eye movements remotely via the participant's webcam promises to be an attractive methodological addition to in-person eye-tracking in the lab. However, there is a lack of systematic research comparing remote web-based eye-tracking with in-lab eye-tracking in young children. We report a multi-lab study that compared these two measures in an anticipatory looking task with toddlers using WebGazer.js and jsPsych. Results of our remotely tested sample of 18-27-month-old toddlers (N = 125) revealed that web-based eye-tracking successfully captured goal-based action predictions, although the proportion of the goal-directed anticipatory looking was lower compared to the in-lab sample (N = 70). As expected, attrition rate was substantially higher in the web-based (42%) than the in-lab sample (10%). Excluding trials based on visual inspection of the match of time-locked gaze coordinates and the participant's webcam video overlayed on the stimuli was an important preprocessing step to reduce noise in the data. We discuss the use of this remote web-based method in comparison with other current methodological innovations. Our study demonstrates that remote web-based eye-tracking can be a useful tool for testing toddlers, facilitating recruitment of larger and more diverse samples; a caveat to consider is the larger drop-out rate.

Explainability of neural networks for child language: Agent-First strategy in comprehension of Korean active transitive construction.

This study investigates how neural networks address the properties of children's linguistic knowledge, with a focus on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop various neural-network models and measure their classification performance on the test stimuli used in a behavioural experiment involving scrambling and omission of sentential components at varying degrees. Results show that, despite some compatibility of these models' performance with the children's response patterns, their performance does not fully approximate the children's utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study's findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language. RESEARCH HIGHLIGHTS: This study investigates how neural networks address properties of child language. We focus on the Agent-First strategy in comprehension of Korean active transitive. Results show by-model/condition asymmetries against children's response patterns. This implies some limits of neural networks on revealing properties of child language.

Korean-speaking children's constructional knowledge about a transitive event: Corpus analysis and Bayesian modelling.

We investigate Korean-speaking children's knowledge about clause-level constructions involving a transitive event - active transitive and suffixal passive - through corpus analysis and Bayesian modelling. The analysis of Korean caregiver input and children's production in CHILDES revealed that the rates of constructional patterns produced by the children mirrored those uttered by the caregivers to a considerable degree and that the caregivers' use of case-marking was skewed towards single form-function pairings (despite the multiple form-function associations that the markers manifest). Based on these characteristics, we modelled a Bayesian learner by employing construction-based input (without considering lexical information). This simulation revealed the dominance of several constructional patterns, occupying most of the input, and their inhibitory effects on the development of the other patterns. Our findings illuminate how children shape clause-level constructional knowledge in Korean, an understudied language for this topic, as a function of input properties and domain-general learning capacities, appealing to the usage-based constructionist approach.