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.
Highlights
Prehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution
Predicted flakes from a more complete virtual knapper—e.g. using the approach outlined here—could form the basis for lithic assemblages to compare with archaeological data, which could allow archaeologists to examine how the different knapping variables affect the resulting assemblages, and to examine important inferences on the various biological, environmental, and sociocultural factors that could have played a role in the formation of the archaeological assemblages we find in the present; informing a large part of our understanding of human evolution
We have used machine learning and programmatically-generated core and flake inputs to produce a proof of concept for a virtual knapping program
Summary
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 Such experiments can require large amounts of time and raw materials, and achieving sufficient control of key variables can be difficult. 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. The virtual knapping program would be unaffected by any biases that individual human knappers may have in traditional experiments, and which are hard to control (given that these biases may in some cases still be unknown)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have