Rethinking Gestalts and the Natural Language Acquisition Framework: Advancing Construction Grammar Approaches for Autistic AAC-Aided Language Learners

The Natural Language Acquisition (NLA) framework and discourse on "gestalt language processing" (GLP) have gained substantial visibility in clinical communities and are increasingly shaping intervention for autistic children, including recommendations for augmentative and alternative communication (AAC). Despite this uptake, NLA's definitions, theoretical assumptions, and developmental claims remain contested in the spoken domain and have not been empirically examined in AAC.

In this article, we examine three assumptions central to NLA: (1) that words are the fundamental building blocks of language, (2) that autistic learners are "gestalt" processors, and (3) that neurotypical "analytic" processors acquire language one word at a time. We contrast these assumptions with a Construction Grammar (CXG) account of language acquisition, challenge the NLA-informed AAC recommendation to program "gestalts" onto a single AAC button, and outline the potential unintended consequences that make this practice highly concerning. Finally, we evaluate the implications of exporting other NLA treatment recommendations to AAC (e.g., restrictions on verbs, grammar, recasts) and outline how a CXG-informed approach better guides the development of AAC supports.

Transferring NLA protocols to AAC introduces avoidable clinical risks. By contrast, a CxG-informed approach aligns with prevailing language acquisition theory and established AAC best practices by emphasizing robust systems, morphosyntactically complete modeling, recasts, sentence diversity, and structured interaction that makes constructions salient and extendable. We conclude that the relevant clinical task is not to classify learners as gestalt or analytic processors but to design interactional environments that accommodate learner and device constraints to make salient and accessible the patterns replete in language to support language development.