We present a simulation study based on a cognitive architecture that unifies various early language acquisition phenomena in laboratory and naturalistic settings. The model adaptively learns procedures through trial-and-error using general-purpose operators, guided by learned contextual associations to optimise future performance. For laboratory-based studies, simulated preferential focusing explains the delayed behavioural onset of statistical learning and the possible age-related decrease in algebraic processing. These findings suggest a link to continuous, implicit learning rather than explicit strategy acquisition. Moreover, procedures are not static but can evolve over time, and multiple plausible procedures may emerge for a given task. Besides, the same model provides a proof-of-concept for word-level phonological learning from naturalistic infant-directed speech, demonstrating how age-related processing efficiency may influence learning trajectories implicated in typical and atypical early language development. Furthermore, the artile discusses the broader implications for modelling other aspects of real-world language acquisition.

In this paper we discuss a computational cognitive model of children's poor performance on pronoun interpretation (the so-called Delay of Principle B Effect, or DPBE). This cognitive model is based on a theoretical account that attributes the DPBE to children's inability as hearers to also take into account the speaker's perspective. The cognitive model predicts that child hearers are unable to do so because their speed of linguistic processing is too limited to perform this second step in interpretation. We tested this hypothesis empirically in a psycholinguistic study, in which we slowed down the speech rate to give children more time for interpretation, and in a computational simulation study. The results of the two studies confirm the predictions of our model. Moreover, these studies show that embedding a theory of linguistic competence in a cognitive architecture allows for the generation of detailed and testable predictions with respect to linguistic performance.