Automated identification and deep classification of cut marks on bones and its paleoanthropological implications

Highlights

• The identification of cut marks on bones created by stone tools is a crucial aspect of human evolution.

• Until now the identification of cut marks was exclusively based on the analyst expertise and experience.

• Here, we present a machine learning methodology that overcomes human subjective interpretations of marks using artificial intelligence tools.

• A computer vision and machine learning method provides a success rate of cut mark identification that is 50% higher tan the frequencies of correct identifications by human experts.

• Our method enables to correctly identify marks and assess when key behavioral features emerged in human evolution, such as meat-eating and hunting.

Abstract

The identification of cut marks and other bone surface modifications (BSM) provides evidence for the emergence of meat-eating in human evolution. This most crucial part of taphonomic analysis of the archaeological human record has been controversial due to highly subjective interpretations of BSM. Here, we use a sample of 79 trampling and cut marks to compare the accuracy in mark identification on bones by human experts and computer trained algorithms. We demonstrate that deep convolutional neural networks (DCNN) and support vector machines (SVM) can recognize marks with accuracy that far exceeds that of human experts. Automated recognition and analysis of BSM using DCNN can achieve an accuracy of 91% of correct identification of cut and trampling marks versus a much lower accuracy rate (63%) obtained by trained human experts. This success underscores the capability of machine learning algorithms to help resolve controversies in taphonomic research and, more specifically, in the study of bone surface modifications. We envision that the proposed methods can help resolve on-going controversies on the earliest human meat-eating behaviors in Africa and other issues such as the earliest occupation of America.

Introduction

The emergence of meat-eating is one of the most highly debated issues in human evolution. It has been linked to encephalization, stone tool use and other major changes in the structural behavior of early hominins (see review in [1]). Recent discoveries argue that both stone tools and meat-eating could have potentially occurred more than one million years before the earliest evidence of encephalization [2], [3]. If true, this will constitute a paradigm shift on our current interpretations on how these behaviors emerged during human evolution. This emphasizes the importance of correctly identifying the bone surface modifications that support such an early meat-eating behavior. The correct interpretation of these modifications, and more specifically of cut marks, is also of great relevance for understanding other important early hominin behavioral features, including early archaeological site function, meat used as a regular or a fallback food, and hunting or scavenging as methods of carcass acquisition. Inference of other social components could be achieved through the analysis of the butchering process (e.g., carcass disarticulation patterns). In fact, the use of cut marks in the fossil record has enabled the identification of specific butchering behaviors by early Pleistocene hominins and their carcass acquisition strategies in the earliest stages of evolution of Homo (see review in [4]).

The uncertainty surrounding the spatial association of stone tools and faunal remains during palimpsest formation, dictates that only anthropogenic bone surface modifications can be used effectively to link functionally lithics and bones. Hence, there is great need to accurately identify cut marks and to distinguish them from modifications created by other non-hominin agents, such as trampling or sediment abrasion. Claims have been made about the Pliocene antiquity of carcass processing using purported cut-marked bones in Dikika (Ethiopia) [2] or in the Plio-Pleistocene boundary in Quranwala (India) [5]. The claims made based on these discoveries remain controversial [6], as the purported cut-marked fossils from both sites also bear clear evidence of trampling or sedimentary abrasion [7].

The importance of a correct identification of bone surface modifications is not restricted to the earliest periods in human evolution. Recent claims have also been made for a 30,000-year-old presence of humans in the American continent based on the presence of cut-marked bones in old fossiliferous deposits (e.g., [8], [9], [10], [11]). This contradicts current evidence of the earliest presence of humans in the continent at a much later age. Similar to the findings in Dikika and Quranwala, the Arroyo del Vizcaino site (Uruguay) also contains abundant conspicuous evidence of trampling and/or sedimentary abrasion [9], [10], [11].

Taphonomists broadly disagree on how cut marks could be properly identified. A series of microscopic criteria were experimentally defined [12], [13], [14], but there is substantial disagreement on how these criteria should be interpreted by individual researchers [13], [15]. Recently, it has been documented that the interpretation of some of these variables remains subjective, hampering an objective scientific approach to cut mark identification and replication [44]. The lack of objective methods underscores the fact that the present identification of BSM (namely, cut marks) depends strongly on the subjective assessment and knowledge of each researcher [44]. Cut mark identification is, thus, commonly carried out outside replicable hypothesis-testing frameworks.

In order to introduce objectivity in cut mark identification, we introduce automatic image classification by machine learning algorithms. Algorithms such as Convolutional Neural Networks (CNN) [16], [17], [18], [19], [20], [21] and Support Vector Machine (SVM) [22], [23], [24], have been shown to match and even exceed human performance in image and pattern recognition. Their use for image processing and classification is greatly facilitated by access to open source software packages such as Neuroph of TensorFlow [25], [26], [27].

In the present study the sample size (79 marks) was kept intentionally short in order to maximize human expert scores (the larger the sample the higher the identification failure rates by humans) and to minimize the computer's accuracy (larger sample sizes lead to better training and higher identification rates) [22], [23], [24], [25], [26], [27]. Despite this initial advantage for human experts, we find that computer algorithms are more successful in the task of mark identification. The proposed CNN and SVM methods enable taphonomists to perform BSM identification in a more objective way than has ever been possible.

We present results that demonstrate the superiority of machine learning algorithms in identifying BSM over “subjective” assessment by several human experts. The present study, using modern bones, suggests that cut marks, like other taphonomic entities, are subject to morphological evolution, through a palimpsestic multiple-agent processes and an interplay between stasis and change [28], [29], [30]. This approach may enable the objective resolution of many cut mark-related controversies, including whether hominin butchering behaviors are identifiable in Pliocene fossils and whether the Americas were occupied by humans more than 30,000 years ago, given the presence of bones bearing BSM which could be interpreted as purported cut marks.