A proof of concept for machine learning-based virtual knapping using neural networks
Abstract Prehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution. Archaeologists use insights from the experimental replication of lithics to understand phenomena such as the behaviours and cognitive capacities required to manufacture th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-98755-6 |
| _version_ | 1818425695863308288 |
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| author | Jordy Didier Orellana Figueroa Jonathan Scott Reeves Shannon P. McPherron Claudio Tennie |
| author_facet | Jordy Didier Orellana Figueroa Jonathan Scott Reeves Shannon P. McPherron Claudio Tennie |
| author_sort | Jordy Didier Orellana Figueroa |
| collection | DOAJ |
| description | Abstract Prehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution. Archaeologists use insights from the experimental replication of lithics to understand phenomena such as the behaviours and cognitive capacities required to manufacture them. However, such experiments can require large amounts of time and raw materials, and achieving sufficient control of key variables can be difficult. A computer program able to accurately simulate stone tool production would make lithic experimentation faster, more accessible, reproducible, less biased, and may lead to reliable insights into the factors that structure the archaeological record. We present here a proof of concept for a machine learning-based virtual knapping framework capable of quickly and accurately predicting flake removals from 3D cores using a conditional adversarial neural network (CGAN). We programmatically generated a testing dataset of standardised 3D cores with flakes knapped from them. After training, the CGAN accurately predicted the length, volume, width, and shape of these flake removals using the intact core surface information alone. This demonstrates the feasibility of machine learning for investigating lithic production virtually. With a larger training sample and validation against archaeological data, virtual knapping could enable fast, cheap, and highly-reproducible virtual lithic experimentation. |
| first_indexed | 2024-12-14T14:18:02Z |
| format | Article |
| id | doaj.art-faa7963ae11740449c1faf5f3832bb7c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T14:18:02Z |
| publishDate | 2021-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-faa7963ae11740449c1faf5f3832bb7c2022-12-21T22:58:10ZengNature PortfolioScientific Reports2045-23222021-10-0111111210.1038/s41598-021-98755-6A proof of concept for machine learning-based virtual knapping using neural networksJordy Didier Orellana Figueroa0Jonathan Scott Reeves1Shannon P. McPherron2Claudio Tennie3Department of Early Prehistory and Quaternary Ecology, University of TübingenDepartment of Early Prehistory and Quaternary Ecology, University of TübingenDepartment of Human Evolution, Max Planck Institute for Evolutionary AnthropologyDepartment of Early Prehistory and Quaternary Ecology, University of TübingenAbstract Prehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution. Archaeologists use insights from the experimental replication of lithics to understand phenomena such as the behaviours and cognitive capacities required to manufacture them. However, such experiments can require large amounts of time and raw materials, and achieving sufficient control of key variables can be difficult. A computer program able to accurately simulate stone tool production would make lithic experimentation faster, more accessible, reproducible, less biased, and may lead to reliable insights into the factors that structure the archaeological record. We present here a proof of concept for a machine learning-based virtual knapping framework capable of quickly and accurately predicting flake removals from 3D cores using a conditional adversarial neural network (CGAN). We programmatically generated a testing dataset of standardised 3D cores with flakes knapped from them. After training, the CGAN accurately predicted the length, volume, width, and shape of these flake removals using the intact core surface information alone. This demonstrates the feasibility of machine learning for investigating lithic production virtually. With a larger training sample and validation against archaeological data, virtual knapping could enable fast, cheap, and highly-reproducible virtual lithic experimentation.https://doi.org/10.1038/s41598-021-98755-6 |
| spellingShingle | Jordy Didier Orellana Figueroa Jonathan Scott Reeves Shannon P. McPherron Claudio Tennie A proof of concept for machine learning-based virtual knapping using neural networks Scientific Reports |
| title | A proof of concept for machine learning-based virtual knapping using neural networks |
| title_full | A proof of concept for machine learning-based virtual knapping using neural networks |
| title_fullStr | A proof of concept for machine learning-based virtual knapping using neural networks |
| title_full_unstemmed | A proof of concept for machine learning-based virtual knapping using neural networks |
| title_short | A proof of concept for machine learning-based virtual knapping using neural networks |
| title_sort | proof of concept for machine learning based virtual knapping using neural networks |
| url | https://doi.org/10.1038/s41598-021-98755-6 |
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