Abstract
Artificial intelligence algorithms have recently been applied to taphonomic questions with great success, outperforming previous methods of bone surface modification (BSM) identification. Following these new developments, here we try different deep learning model architectures, optimizers and activation functions to assess if it is possible to identify a stone tool’s raw material simply by looking at the cut marks that it created on bone. The deep learning models correctly discerned between flint, sandstone and quartzite with accuracy rates as high as 78%. Also, single models seem to work better than ensemble ones, and there is no optimal combination of hyperparameters that perform better in every possible scenario. Model fine-tuning is thus advised as a protocol. These results consolidate the potential of deep learning methods to make classifications out of BSM’s microscopic features with a higher degree of confidence and more objectively than alternative taphonomic procedures.
Highlights
Bone surface modification (BSM) studies have long been at the core of the taphonomic discipline, since such traces represent some of the most direct evidence for interaction between hominins and their environment
Here we present a study aimed at applying computer vision algorithms to an experimental reference collection of butchery cut marks made with retouched flakes with the goal of assessing if cut mark microscopic features vary by raw material
The results presented here show that deep learning (DL) algorithms are capable of identifying raw material types through their impact on cut mark micro-morphology, but they provide overall better results with a greater degree of confidence than geometric morphometrics (GMM), involving low- and high-magnification approaches
Summary
Bone surface modification (BSM) studies have long been at the core of the taphonomic discipline, since such traces represent some of the most direct evidence for interaction between hominins and their environment. Of all BSM, butchering cut marks found on fossilized bones have received the most attention because they provide evidence for meat processing using stone or metal tools. The discovery of early butchering marks on animal bones has been claimed to be the first step in the cognitive evolution of the human lineage (Bello and Soligo 2008). The archaeological record may not always provide all the inferential steps necessary to go from the traces themselves to the understanding of the contextual and behavioural processes that generated these traces. Several studies have tried to identify the type of stone tools that generated marks through both quantitative and qualitative analyses of mark cross-section Studies addressing the impact of stone tool’s raw material on mark creation have been more limited in number
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