View in EDS

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

Saved in:
Bibliographic Details
Title: Do Humans Use Push-Down Stacks When Learning or Producing Center-Embedded Sequences?
Language: English
Authors: Stephen Ferrigno (ORCID 0000-0002-8021-1662), Samuel J. Cheyette, Susan Carey
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
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1484284
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Do Humans Use Push-Down Stacks When Learning or Producing Center-Embedded Sequences?
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Stephen+Ferrigno%22">Stephen Ferrigno</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8021-1662">0000-0002-8021-1662</externalLink>)<br /><searchLink fieldCode="AR" term="%22Samuel+J%2E+Cheyette%22">Samuel J. Cheyette</searchLink><br /><searchLink fieldCode="AR" term="%22Susan+Carey%22">Susan Carey</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Cognitive+Science%22"><i>Cognitive Science</i></searchLink>. 2025 49(9).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: 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
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 38
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Institutes of Health (NIH) (DHHS)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: F32HD101208
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Sequential+Learning%22">Sequential Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+Representation%22">Knowledge Representation</searchLink><br /><searchLink fieldCode="DE" term="%22Training%22">Training</searchLink><br /><searchLink fieldCode="DE" term="%22Generalization%22">Generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Error+Patterns%22">Error Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Reaction+Time%22">Reaction Time</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+Statistics%22">Bayesian Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Memory%22">Memory</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Grammar%22">Grammar</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/cogs.70112
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0364-0213<br />1551-6709
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1484284
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1484284
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/cogs.70112
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 38
    Subjects:
      – SubjectFull: Sequential Learning
        Type: general
      – SubjectFull: Cognitive Processes
        Type: general
      – SubjectFull: Knowledge Representation
        Type: general
      – SubjectFull: Training
        Type: general
      – SubjectFull: Generalization
        Type: general
      – SubjectFull: Error Patterns
        Type: general
      – SubjectFull: Reaction Time
        Type: general
      – SubjectFull: Bayesian Statistics
        Type: general
      – SubjectFull: Memory
        Type: general
      – SubjectFull: Learning Processes
        Type: general
      – SubjectFull: Grammar
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
    Titles:
      – TitleFull: Do Humans Use Push-Down Stacks When Learning or Producing Center-Embedded Sequences?
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Stephen Ferrigno
      – PersonEntity:
          Name:
            NameFull: Samuel J. Cheyette
      – PersonEntity:
          Name:
            NameFull: Susan Carey
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0364-0213
            – Type: issn-electronic
              Value: 1551-6709
          Numbering:
            – Type: volume
              Value: 49
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Cognitive Science
              Type: main
ResultId 1