Using machine learning on new feature sets extracted from three-dimensional models of broken animal bones to classify fragments according to break agent

Abstract

Distinguishing agents of bone modification at paleoanthropological sites is an important means of understanding early hominin evolution. Fracture pattern analysis is used to help determine site formation processes, including whether hominins were hunting or scavenging for animal food resources. Determination of how these behaviors manifested in ancient human sites has major implications for our biological and behavioral evolution, including social and cognitive abilities, dietary impacts of having access to in-bone nutrients like marrow, and cultural variation in butchering and food processing practices. Nevertheless, previous analyses remain inconclusive, often suffering from lack of replicability, misuse of mathematical methods, and/or failure to overcome equifinality. In this paper, we present a new approach aimed at distinguishing bone fragments resulting from hominin and carnivore breakage. Our analysis is founded on a large collection of scanned three-dimensional models of fragmentary bone broken by known agents, to which we apply state of the art machine learning algorithms. Our classification of fragments achieves an average mean accuracy of 77% across tests, thus demonstrating notable, but not overwhelming, success for distinguishing the agent of breakage. We note that, while previous research applying such algorithms has claimed higher success rates, fundamental errors in the application of machine learning protocols suggest that the reported accuracies are unjustified and unreliable. The systematic, fully documented, and proper application of machine learning algorithms leads to an inherent reproducibility of our study, and therefore our methods hold great potential for deciphering when and where hominins first began exploiting marrow and meat, and clarifying their importance and influence on human evolution.