Do Humans Use Push-Down Stacks When Learning or Producing Center-Embedded Sequences?
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| Title: | Do Humans Use Push-Down Stacks When Learning or Producing Center-Embedded Sequences? |
|---|---|
| Language: | English |
| Authors: | Stephen Ferrigno (ORCID |
| Source: | Cognitive Science. 2025 49(9). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 38 |
| Publication Date: | 2025 |
| Sponsoring Agency: | National Institutes of Health (NIH) (DHHS) |
| Contract Number: | F32HD101208 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Sequential Learning, Cognitive Processes, Knowledge Representation, Training, Generalization, Error Patterns, Reaction Time, Bayesian Statistics, Memory, Learning Processes, Grammar, Artificial Intelligence |
| DOI: | 10.1111/cogs.70112 |
| ISSN: | 0364-0213 1551-6709 |
| Abstract: | 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[superscript n]B[superscript 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1484284 |
| Database: | ERIC |
| Abstract: | 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[superscript n]B[superscript 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. |
|---|---|
| ISSN: | 0364-0213 1551-6709 |
| DOI: | 10.1111/cogs.70112 |
