Complex cognitive algorithms preserved by selective social learning in experimental populations.
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| Title: | Complex cognitive algorithms preserved by selective social learning in experimental populations. |
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| Authors: | Thompson, B., Opheusden, B., Sumers, T., Griffiths, T. L. |
| Source: | Science (pre-March 2025). 4/1/2022, Vol. 376 Issue 6588, p95-98. 4p. 4 Diagrams. |
| Subjects: | Social learning, Social evolution, Cognitive ability, Algorithms, Decision making |
| Abstract: | Many human abilities rely on cognitive algorithms discovered by previous generations. Cultural accumulation of innovative algorithms is hard to explain because complex concepts are difficult to pass on. We found that selective social learning preserved rare discoveries of exceptional algorithms in a large experimental simulation of cultural evolution. Participants (N = 3450) faced a difficult sequential decision problem (sorting an unknown sequence of numbers) and transmitted solutions across 12 generations in 20 populations. Several known sorting algorithms were discovered. Complex algorithms persisted when participants could choose who to learn from but frequently became extinct in populations lacking this selection process, converging on highly transmissible lower-performance algorithms. These results provide experimental evidence for hypothesized links between sociality and cognitive function in humans. [ABSTRACT FROM AUTHOR] |
| Copyright of Science (pre-March 2025) is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 156069351 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Complex cognitive algorithms preserved by selective social learning in experimental populations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Thompson%2C+B%2E%22">Thompson, B.</searchLink><br /><searchLink fieldCode="AR" term="%22Opheusden%2C+B%2E%22">Opheusden, B.</searchLink><br /><searchLink fieldCode="AR" term="%22Sumers%2C+T%2E%22">Sumers, T.</searchLink><br /><searchLink fieldCode="AR" term="%22Griffiths%2C+T%2E+L%2E%22">Griffiths, T. L.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Science+%28pre-March+2025%29%22">Science (pre-March 2025)</searchLink>. 4/1/2022, Vol. 376 Issue 6588, p95-98. 4p. 4 Diagrams. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Social+learning%22">Social learning</searchLink><br /><searchLink fieldCode="DE" term="%22Social+evolution%22">Social evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Many human abilities rely on cognitive algorithms discovered by previous generations. Cultural accumulation of innovative algorithms is hard to explain because complex concepts are difficult to pass on. We found that selective social learning preserved rare discoveries of exceptional algorithms in a large experimental simulation of cultural evolution. Participants (N = 3450) faced a difficult sequential decision problem (sorting an unknown sequence of numbers) and transmitted solutions across 12 generations in 20 populations. Several known sorting algorithms were discovered. Complex algorithms persisted when participants could choose who to learn from but frequently became extinct in populations lacking this selection process, converging on highly transmissible lower-performance algorithms. These results provide experimental evidence for hypothesized links between sociality and cognitive function in humans. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Science (pre-March 2025) is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=156069351 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1126/science.abn0915 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 95 Subjects: – SubjectFull: Social learning Type: general – SubjectFull: Social evolution Type: general – SubjectFull: Cognitive ability Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Decision making Type: general Titles: – TitleFull: Complex cognitive algorithms preserved by selective social learning in experimental populations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Thompson, B. – PersonEntity: Name: NameFull: Opheusden, B. – PersonEntity: Name: NameFull: Sumers, T. – PersonEntity: Name: NameFull: Griffiths, T. L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 4/1/2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00368075 Numbering: – Type: volume Value: 376 – Type: issue Value: 6588 Titles: – TitleFull: Science (pre-March 2025) Type: main |
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