Academic Journal
Why statistical learning researchers should study nonhuman animals.
| Title: | Why statistical learning researchers should study nonhuman animals. |
|---|---|
| Authors: | Tosatto L; School of Biological Sciences, Monash University, Australia; Normandie University, UNICAEN, CNRS, EthoS, UMR 6552, 14000 Caen, France., Avarguès-Weber A; CNRS, University of Toulouse, Centre de Recherches sur la Cognition Animale (CRCA-CBI), Toulouse, France. Electronic address: aurore.avargues-weber@utoulouse.fr. |
| Source: | Current opinion in neurobiology [Curr Opin Neurobiol] 2026 Oct; Vol. 100, pp. 103261. Date of Electronic Publication: 2026 Aug 19. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: Current Biology Country of Publication: England NLM ID: 9111376 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-6882 (Electronic) Linking ISSN: 09594388 NLM ISO Abbreviation: Curr Opin Neurobiol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London, UK : Current Biology, c1991- |
| MeSH Terms: | Learning*/physiology , Brain*/physiology , Cognition*/physiology , Probability Learning*, Animals ; Humans ; Biological Evolution |
| Abstract: | Statistical learning (SL) - the ability to detect patterns in sensory input without explicit instruction - is crucial for building internal models of the environment. In humans, it notably supports language acquisition, including word segmentation and grammar learning. Evidence across primates, songbirds, rodents, and insects indicate that SL is a widely shared evolutionarily conserved ability. The computational complexity of the mechanisms involved; however, varies between species, likely reflecting specific cognitive limitations. Alternatively, specific competences may have evolved to support the emergence of demanding, ecologically relevant, functions such as complex communication systems. These findings challenge the idea of a human-specific SL module while raising key questions about its evolutionary drivers and underlying mechanisms. Addressing these questions requires the expansion of cross-species, cross-modal studies with ecologically valid, unsupervised paradigms. Comparative approaches are indeed essential to uncover shared properties and species-specific SL adaptations. Framing SL as a foundational component of cognition, informed by animal research, offers new insights into brain function, and implicit learning in light of the evolution of sophisticated, emergent cognitive abilities such as complex communication systems. (Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.) |
| Competing Interests: | Declaration of competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Entry Date(s): | Date Created: 20260819 Date Completed: 20260904 Latest Revision: 20260904 |
| Update Code: | 20260905 |
| DOI: | 10.1016/j.conb.2026.103261 |
| PMID: | 42617431 |
| Database: | MEDLINE |
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| Items | – Name: Title Label: Title Group: Ti Data: Why statistical learning researchers should study nonhuman animals. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Tosatto+L%22">Tosatto L</searchLink>; School of Biological Sciences, Monash University, Australia; Normandie University, UNICAEN, CNRS, EthoS, UMR 6552, 14000 Caen, France.<br /><searchLink fieldCode="AU" term="%22Avarguès-Weber+A%22">Avarguès-Weber A</searchLink>; CNRS, University of Toulouse, Centre de Recherches sur la Cognition Animale (CRCA-CBI), Toulouse, France. Electronic address: aurore.avargues-weber@utoulouse.fr. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%229111376%22">Current opinion in neurobiology</searchLink> [Curr Opin Neurobiol] 2026 Oct; Vol. 100, pp. 103261. <i>Date of Electronic Publication: </i>2026 Aug 19. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Review – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Current+Biology%22">Current Biology </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>9111376 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1873-6882 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2209594388%22">09594388 </searchLink><i>NLM ISO Abbreviation: </i>Curr Opin Neurobiol <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: London, UK : Current Biology, c1991- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Learning%22">Learning*</searchLink>/<searchLink fieldCode="MM" term="%22Learning+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Brain%22">Brain*</searchLink>/<searchLink fieldCode="MM" term="%22Brain+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Cognition%22">Cognition*</searchLink>/<searchLink fieldCode="MM" term="%22Cognition+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Probability+Learning%22">Probability Learning*</searchLink><br /><searchLink fieldCode="MH" term="%22Animals%22">Animals</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Biological+Evolution%22">Biological Evolution</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Statistical learning (SL) - the ability to detect patterns in sensory input without explicit instruction - is crucial for building internal models of the environment. In humans, it notably supports language acquisition, including word segmentation and grammar learning. Evidence across primates, songbirds, rodents, and insects indicate that SL is a widely shared evolutionarily conserved ability. The computational complexity of the mechanisms involved; however, varies between species, likely reflecting specific cognitive limitations. Alternatively, specific competences may have evolved to support the emergence of demanding, ecologically relevant, functions such as complex communication systems. These findings challenge the idea of a human-specific SL module while raising key questions about its evolutionary drivers and underlying mechanisms. Addressing these questions requires the expansion of cross-species, cross-modal studies with ecologically valid, unsupervised paradigms. Comparative approaches are indeed essential to uncover shared properties and species-specific SL adaptations. Framing SL as a foundational component of cognition, informed by animal research, offers new insights into brain function, and implicit learning in light of the evolution of sophisticated, emergent cognitive abilities such as complex communication systems.<br /> (Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.) – Name: Abstract Label: Competing Interests Group: Ab Data: Declaration of competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260819 <i>Date Completed: </i>20260904 <i>Latest Revision: </i>20260904 – Name: DateUpdate Label: Update Code Group: Date Data: 20260905 – Name: DOI Label: DOI Group: ID Data: 10.1016/j.conb.2026.103261 – Name: AN Label: PMID Group: ID Data: 42617431 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.conb.2026.103261 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 103261 Subjects: – SubjectFull: Animals Type: general – SubjectFull: Humans Type: general – SubjectFull: Biological Evolution Type: general – SubjectFull: Learning physiology Type: general – SubjectFull: Brain physiology Type: general – SubjectFull: Cognition physiology Type: general – SubjectFull: Probability Learning Type: general Titles: – TitleFull: Why statistical learning researchers should study nonhuman animals. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tosatto L – PersonEntity: Name: NameFull: Avarguès-Weber A IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2026 Oct Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1873-6882 Numbering: – Type: volume Value: 100 Titles: – TitleFull: Current opinion in neurobiology Type: main |
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