Unpacking language learning success: Cognitive, experiential, and neural trajectories in adult acquisition

Objective

Languages are fundamental to education, culture, and social inclusion, yet a central puzzle remains: why do some adults acquire new languages more successfully than others, even when they learn under similar conditions? Decades of research in second and additional language (Ln) acquisition have illuminated how input and instruction affect learning, but they have not resolved this foundational question. Current accounts struggle to explain the complex interplay between external factors (e.g., variability and ambiguity in the input, opportunities for use) and learner-internal characteristics such as cognitive and perceptual abilities or prior language experience. These challenges are especially pronounced in immersion and other naturalistic settings, where explicit guidance is limited and learners must detect regularities, extract meaning from ambiguity, and build new representations under conditions of uncertainty. VISTA (Variables in Immersive Statistical language learning: Trajectories and Aptitudes) addresses this gap by investigating how adults acquire linguistic patterns in a novel natural language (Portuguese) across three interdependent domains: phonology, lexis, and morphosyntax. This multidimensional design enhances ecological validity and responds to calls for more realistic approaches in statistical learning research. VISTA also develops a novel individual-differences framework, modelling how cognitive abilities, perceptual skills, and prior multilingual experience jointly shape learning trajectories. By integrating behavioural and neuroscientific measures, VISTA will capture how adults learn new linguistic patterns in real time and across multiple domains. This comprehensive approach will advance theories of implicit language learning and generate evidence-based recommendations to support inclusive education, lifelong learning, and digital innovation – contributing directly to European Union priorities on multilingualism and social cohesion.