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  Neural networks differentiate between Middle and Later Stone Age lithic assemblages in eastern Africa

Grove, M., & Blinkhorn, J. (2020). Neural networks differentiate between Middle and Later Stone Age lithic assemblages in eastern Africa. PLoS One, 15(8): e0237528, pp. 1-27. doi:10.1371/journal.pone.0237528.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-F760-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-F761-6
Genre: Journal Article

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 Creators:
Grove, Matt, Author
Blinkhorn, James1, Author              
Affiliations:
1Lise Meitner Pan-African Evolution Research Group, Max Planck Institute for the Science of Human History, Max Planck Society, ou_3033582              

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Free keywords: Paleoanthropology, Archaeology, Artificial neural networks, Neural networks, Stone age, Africa, Lithic technology, Raw materials
 Abstract: The Middle to Later Stone Age transition marks a major change in how Late Pleistocene African populations produced and used stone tool kits, but is manifest in various ways, places and times across the continent. Alongside changing patterns of raw material use and decreasing artefact sizes, changes in artefact types are commonly employed to differentiate Middle Stone Age (MSA) and Later Stone Age (LSA) assemblages. The current paper employs a quantitative analytical framework based upon the use of neural networks to examine changing constellations of technologies between MSA and LSA assemblages from eastern Africa. Network ensembles were trained to differentiate LSA assemblages from Marine Isotope Stage 3&4 MSA and Marine Isotope Stage 5 MSA assemblages based upon the presence or absence of 16 technologies. Simulations were used to extract significant indicator and contra-indicator technologies for each assemblage class. The trained network ensembles classified over 94% of assemblages correctly, and identified 7 key technologies that significantly distinguish between assemblage classes. These results clarify both temporal changes within the MSA and differences between MSA and LSA assemblages in eastern Africa.

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Language(s): eng - English
 Dates: 2020-08-26
 Publication Status: Published online
 Pages: 27
 Publishing info: -
 Table of Contents: Introduction

The MSA/LSA transition

Artificial neural networks

Methods
- Data
- Assemblage classification
- Typological indicators

Results
- Assemblage classification
- Typological indicators

Discussion
- Significant lithic components
- Incorrectly classified assemblages
- Typology, quantification, and the LSA / MSA transition
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pone.0237528
Other: shh2697
 Degree: -

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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 15 (8) Sequence Number: e0237528 Start / End Page: 1 - 27 Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850