Natural language syntax complies with the free-energy principle.

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect spee...

Descripción completa

Detalles Bibliográficos
Publicado en:Synthese Vol. 203; no. 5; pp. 1 - 36
Autores principales: Murphy, Elliot, Holmes, Emma, Friston, Karl
Formato: Artículo
Publicado: Springer Nature May2024
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=177053151&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 177053151
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00397857
        4LI
      jtl: Synthese
      issn: 00397857
      maglogo: N
    pubinfo:
      dt: May2024
      vid: 203
      iid: 5
      pid: 237
      pub: Springer Nature
    artinfo:
      ui:
        177053151
        10.1007/s11229-024-04566-3
      ppf: 1
      ppct: 35
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
              size: 675KB
      tig:
        atl: Natural language syntax complies with the free-energy principle.
      aug:
        au:
          Murphy, Elliot
          Holmes, Emma
          Friston, Karl
        affil:
          Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center, 77030, Houston, TX, USA
          Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center, 77030, Houston, TX, USA
          https://ror.org/02jx3x895 Department of Speech Hearing and Phonetic Sciences, University College London, WC1N 1PF, London, UK
          https://ror.org/02704qw51 The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, WC1N 3AR, London, UK
      sug:
      keyword:
        Active inference
        Compression
        Free-energy principle
        Kolmogorov complexity
        Language
        Lempel–Ziv
      ab: Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design—such as “minimal search” criteria from theoretical syntax—adhere to the FEP. This affords a greater degree of explanatory power to the FEP—with respect to higher language functions—and offers linguistics a grounding in first principles with respect to computability. While we mostly focus on building new principled conceptual relations between syntax and the FEP, we also show through a sample of preliminary examples how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel–Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing–Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      custom: Synthese is a copyright of Springer, 2024. All Rights Reserved.
      item: Synthese
      holder: Springer Nature
      dt:
        @attributes:
          year: 2024
    holdings:
      @attributes:
        islocal: N