Do Humans Use Push‐Down Stacks When Learning or Producing Center‐Embedded Sequences

Datos Bibliográficos

ID7150344
AutoresStephen Ferrigno (0000-0002-8021-1662, Department of Psychology, University of Wisconsin-Madison, autor de correspondencia), Samuel J Cheyette (0000-0002-7052-5369, Department of Brain and Cognitive Sciences Massachusetts Institute of Technology), Susan Carey (0000-0001-7913-2687, Department of Psychology Harvard University)
Año2025
Volumen49
Número9
Páginase70112-e70112
Fecha de publicación2025-09-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaCognitive Science (JOURNAL)
Identificadores de la revistaISSN: 0364-0213 • E-ISSN: 1551-6709
EditorialWiley (PUBLISHER • GB)
DOI10.1111/cogs.70112
PMID40952769
OpenAlexW4414166714
IdiomaEN
Referencias citadas47

Complex sequences are ubiquitous in human mental life, structuring representations within many different cognitive domains—natural language, music, mathematics, and logic, to name a few. However, the representational and computational machinery used to learn abstract grammars and process complex sequences is unknown. Here, we used an artificial grammar learning task to study how adults abstract center‐embedded and cross‐serial grammars that generalize beyond the level of embedding of the training sequences. We tested untrained generalizations to longer sequence lengths and used error patterns, item‐to‐item response times, and a Bayesian mixture model to test two possible memory architectures that might underlie the sequence representations of each grammar: stacks and queues. We find that adults learned both grammars, that the cross‐serial grammar was easier to learn and produce than the matched center‐embedded grammar, and that item‐to‐item touch times during sequence generation differed systematically between the two types of sequences. Contrary to widely held assumptions, we find no evidence that a stack architecture is used to generate center‐embedded sequences in an indexed A n B n artificial grammar. Instead, the data and modeling converged on the conclusion that both center‐embedded and cross‐serial sequences are generated using a queue memory architecture. In this study, participants stored items in a first‐in‐first‐out memory architecture and then accessed them via an iterative search over the stored list to generate the matched base pairs of center‐embedded or cross‐serial sequences

Cognition · Cognitive architecture · Embedding · Grammar · Implicit Learning · Rule-based machine translation · Sequence learning · Neurobiology of Language and Bilingualism · Reading and Literacy Development · Topic Modeling