Experimental replication of stone tools is an important method for understanding the context and production of prehistoric technologies. In scientific undertakings, experimental control is a valuable and necessary means of ensuring that confounding variables are not influencing the outcomes of the study. One way that researchers can exert control in knapping experiments is to standardize the type, form, and size of raw materials that are provided to the knappers. Though measures to standardize materials are already part of archaeological practice, specific protocols, let alone comparisons between standardization techniques, are rarely openly reported. Consequently, independent laboratories often repeat the same trial-and-error process for selecting the ‘right’ material for testing. We investigate a variety of techniques and raw materials (e.g., hand-knapped flint, machine-cut basalt, manufactured glass, and porcelain). Each material was evaluated for their validity, reliability, and ease of standardization. Here, we have outlined the raw material tests we performed, providing information on the individual approaches, as well as comparisons between the techniques and materials according to both validity and reliability, along with their costs and our own recommendations. This text is intended as a serviceable guide on raw material standardization for knapping experiments, including previously pursued avenues and means that are as-of-yet undescribed in the experimental archaeology literature. We include additional considerations for techniques that were not included in this study or which are presently not widely available and/or affordable. Future potential in this field would benefit from advances in the relevant technologies and in methodological approaches.
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.
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 stone tools to understand things 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 knapping variables can be difficult. A computer program able to accurately simulate stone tool production would make lithic experimentation faster and more accessible, 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 core surface information alone. This demonstrates the feasibility of machine learning for investigating stone tool production virtually. With an increased training sample and validation against archaeological data, virtual knapping could enable fast, cheap, and highly-repeatable virtual lithic experimentation.
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