Density and Distinctiveness in Early Word Learning: Evidence From Neural Network Simulations.
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| Title: | Density and Distinctiveness in Early Word Learning: Evidence From Neural Network Simulations. |
|---|---|
| Authors: | Jones, Samuel David1 (AUTHOR) sam.jones@lancs.ac.uk, Brandt, Silke1 (AUTHOR) |
| Source: | Cognitive Science. Jan2020, Vol. 44 Issue 1, pN.PAG-N.PAG. 1p. |
| Subject Terms: | Error rates, Network performance, Artificial neural networks, Information commons |
| Abstract: | High phonological neighborhood density has been associated with both advantages and disadvantages in early word learning. High density may support the formation and fine‐tuning of new word sound memories—a process termed lexical configuration (e.g., Storkel, 2004). However, new high‐density words are also more likely to be misunderstood as instances of known words, and may therefore fail to trigger the learning process (e.g., Swingley & Aslin, 2007). To examine these apparently contradictory effects, we trained an autoencoder neural network on 587,954 word tokens (5,497 types, including mono‐ and multisyllabic words of all grammatical classes) spoken by 279 caregivers to English‐speaking children aged 18–24 months. We then simulated a communicative development inventory administration and compared network performance to that of 2,292 children aged 18–24 months. We argue that autoencoder performance illustrates concurrent density advantages and disadvantages, in contrast to prior behavioral and computational literature treating such effects independently. Low network error rates signal a configuration advantage for high‐density words, while high network error rates signal a triggering advantage for low‐density words. This interpretation is consistent with the application of autoencoders in academic research and industry, for simultaneous feature extraction (i.e., configuration) and anomaly detection (i.e., triggering). Autoencoder simulation therefore illustrates how apparently contradictory density and distinctiveness effects can emerge from a common learning mechanism. Open Research Badges: This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://osf.io/2qk5j/. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki. [ABSTRACT FROM AUTHOR] |
| Copyright of Cognitive Science is the property of Wiley-Blackwell 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: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 141288980 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Density and Distinctiveness in Early Word Learning: Evidence From Neural Network Simulations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jones%2C+Samuel+David%22">Jones, Samuel David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sam.jones@lancs.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Brandt%2C+Silke%22">Brandt, Silke</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Cognitive+Science%22">Cognitive Science</searchLink>. Jan2020, Vol. 44 Issue 1, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Error+rates%22">Error rates</searchLink><br /><searchLink fieldCode="DE" term="%22Network+performance%22">Network performance</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Information+commons%22">Information commons</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: High phonological neighborhood density has been associated with both advantages and disadvantages in early word learning. High density may support the formation and fine‐tuning of new word sound memories—a process termed lexical configuration (e.g., Storkel, 2004). However, new high‐density words are also more likely to be misunderstood as instances of known words, and may therefore fail to trigger the learning process (e.g., Swingley & Aslin, 2007). To examine these apparently contradictory effects, we trained an autoencoder neural network on 587,954 word tokens (5,497 types, including mono‐ and multisyllabic words of all grammatical classes) spoken by 279 caregivers to English‐speaking children aged 18–24 months. We then simulated a communicative development inventory administration and compared network performance to that of 2,292 children aged 18–24 months. We argue that autoencoder performance illustrates concurrent density advantages and disadvantages, in contrast to prior behavioral and computational literature treating such effects independently. Low network error rates signal a configuration advantage for high‐density words, while high network error rates signal a triggering advantage for low‐density words. This interpretation is consistent with the application of autoencoders in academic research and industry, for simultaneous feature extraction (i.e., configuration) and anomaly detection (i.e., triggering). Autoencoder simulation therefore illustrates how apparently contradictory density and distinctiveness effects can emerge from a common learning mechanism. Open Research Badges: This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://osf.io/2qk5j/. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Cognitive Science is the property of Wiley-Blackwell 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/cogs.12812 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Error rates Type: general – SubjectFull: Network performance Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Information commons Type: general Titles: – TitleFull: Density and Distinctiveness in Early Word Learning: Evidence From Neural Network Simulations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jones, Samuel David – PersonEntity: Name: NameFull: Brandt, Silke IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 03640213 Numbering: – Type: volume Value: 44 – Type: issue Value: 1 Titles: – TitleFull: Cognitive Science Type: main |
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