Use and abuse of cut mark analyses: The Rorschach effect

Journal of Archaeological Science 86 (2017) 14e23

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Journal of Archaeological Science journal homepage: http://www.elsevier.com/locate/jas

Use and abuse of cut mark analyses: The Rorschach effect
d, e, f, g, Isabel Ca
ceres d, e, Manuel Domínguez-Rodrigo a, b, c, *, Palmira Saladie d, e, f a ,c a , c, d
Yravedra , Antonio Rodríguez-Hidalgo , Jose , Patricia Martín d, Rosa Huguet
g, Julia Aramendi a, c, Lucia Cobo-Sa
nchez a, c Antonio Pineda d, e, Juan Marín h, Clara Gene
de Henares, Covarrubias 36, 28010 Madrid, Spain Institute of Evolution in Africa (IDEA), University of Alcala Real Complutense College at Harvard, 26 Trowbridge Street, Cambridge, MA 02138, USA c Department of Prehistory, Complutense University, 28040 Madrid, Spain d
de Paleoecologia Humana i Evolucio
Social, Universitat Rovira I Virgili, 43007, Tarragona, Spain IPHES, Institut Catala e  ria, Universitat Rovira i Virgili (URV), Avinguda de Catalunya 35, 43002, Tarragona, Spain Area de Prehisto f Unit associated to CSIC. Departamento de Paleobiologia. Museo Nacional de Ciencias Naturales, C/ Jos
e Gutierrez Abascal, 2, 28006, Madrid, Spain g
rio e Pr

ria do Centro de Geoci^ GQP-CG, Grupo Quaterna e-Histo encias (uI&D 73 e FCT), Portugal h Museum National d'Histoire Naturelle, Institut de Paleontologie Humaine, 1 Rue Ren
e Panhard 75013 Paris, France a

b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 May 2017 Received in revised form 5 August 2017 Accepted 8 August 2017

A series of experimental cut marks have been analyzed by eleven taphonomists with the goal of assessing if they could identify similarly 14 selected microscopic variables which would identify those marks as cut marks. The main objective was to test if variable identification could be made scientifically; that is, different researchers using the same method and criteria making the same assessment of each variable. This experiment shows that even in researchers trained in the same laboratories and following the same protocols divergences in the perception of each variable are significant. This indicates that mark perception and interpretation is a highly subjective process. If this basic analytical stage is subjective, subjectivity permeates to a greater degree the higher inferential stages leading from mark identification to reconstruction of butchering behaviors based on mark frequencies, mark anatomical distribution, actor-effector-trace processes, and statistical interpretations of the stochastic mark-imparting butchering processes. Here, we emphasize that the use of bone surface modifications for behavioral interpretations remains a non-scientific endeavor because of lack of independent replicability of criteria and processes, divergences in how variables are selected and used and epistemologically flawed analogs. This constitutes a major call to taphonomy to engage in more scientific (i.e., objective) approaches to the study of bone surface modifications for taphonomic inference elaboration. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Taphonomy Cut marks Analogy Bone surface modifications Microscopy

1. Introduction Taphonomist and more specifically, zooarchaeologists, have been debating the behavioral meaning of cut marks on archaeofaunas for decades. From their meaning on carcass acquisition strategies by hominins (i.e., the hunting versus passive scavenging debate) (Binford et al., 1988; Blumenschine, 1995; Bunn, 1991; Bunn et al., 1986; Domínguez-Rodrigo, 2015; Domínguez-Rodrigo et al., 2007, 2014; Pante et al., 2015; Pickering, 2013; Selvaggio, 1998) to their meaning in specific butchering behaviors (i.e., defleshing,

* Corresponding author. Institute of Evolution in Africa (IDEA), University of
de Henares, Covarrubias 36, 28010 Madrid, Spain. Alcala E-mail address: [email protected] (M. Domínguez-Rodrigo). http://dx.doi.org/10.1016/j.jas.2017.08.001 0305-4403/© 2017 Elsevier Ltd. All rights reserved.


n and skinning, dismembering) (e.g., Binford, 1981; Gala Domínguez-Rodrigo, 2013; Nilssen, 2000). More importantly, cut mark correct identification, as the basis for any behavioral interpretation has long been debated and special emphasis has been made on its morphological attributes (Bunn, 1981; Shipman, 1983) and the link between groove morphology and the type of tool used, considering raw material (diverse types of stones, metal, bamboo, shell) (Fisher, 1995; McCardle, 2015, 2016; Walker, 1978; Walker and Long, 1977; Greenfield, 1999, 2002, 2006, 2005; Jones, 2011; Krasinski, 2016; Dewbury and Russell, 2007; West and Louys, 2007), and stone tool type -simple flakes, retouched flakes and handaxes (e.g., Bello et al., 2009; Bello and Soligo, 2008; Chioi and Driwantoro, 2007; de Juana et al., 2010; Domínguez-Rodrigo et al.,
n and Domínguez-Rodrigo, 2014; Val et al., 2017; 2009; Gala Yravedra et al., 2017a, b).

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The variable morphological and microscopic criteria presented by diverse authors using each different analytical methods has led to an inflated ambiguity in the identification of cut marks in prehistoric archaeofaunas. The most relevant outcome of this circumstance is the controversial interpretation of what some view as potential Pliocene cut marked bones from Dikika (Ethiopia) (Domínguez-Rodrigo et al., 2012, 2010; McPherron et al., 2010; Thompson et al., 2015). This led to a desperate call acknowledging that taphonomist were lost when it came to identifying cut marks in the archaeological record (Njau, 2012). To reduce the subjectivity in the interpretation of marks, alternative methods were approached using sophisticated microscopic and geometric morphometric approaches (e.g., Bello et al., 2009; Bello and Soligo,
-Gonza
lez et al., 2015, 2016; 2008; Boschin and Crezzini, 2012; Mate Moretti et al., 2015; Pante et al., 2017). However, these methods cannot escape controversial interpretations either (see below). How did taphonomists end up from being confident in identifying cut marks and other bone surface modifications in rates exceeding 90% of experimental marks (Blumenschine et al., 1996; de Juana et al., 2010; Domínguez-Rodrigo et al., 2009), including the directionality of the stroke (Bromage and Boyde, 1984) or the handedness of the hominin (Bromage et al., 1991; Pickering and Hensley-Marschand, 2008) to our current situation of doubt (Njau, 2012)? How did we end up needing the assistance of mechanical methods to sophisticate mark analysis (Bello et al., 2009;
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lez et al., 2015, 2016; Pante Bello and Soligo, 2008; Mate et al., 2017)? How did we end up denying our capability of differentiating cut marks from other bone surface modification and tool types inflicting them (Bello et al., 2009; Bello and Soligo, 2008; de
n and Juana et al., 2010; Domínguez-Rodrigo et al., 2009; Gala Domínguez-Rodrigo, 2014; Val et al., 2017; Yravedra et al., 2017a, b)? In an elegant summary of this situation, James and Thompson (2015) eloquently stressed that this inconsistency in the identification of bone surface modifications results from the lack of standardization in terminology and method. James and Thompson (2015) point out several examples of how definition of mark types vary according to researchers and also, how the same researchers are not consistent in sticking to the same definitions through time. If the object of study is not properly defined, then its scientific study is extremely difficult as epistemologists would argue (Bunge, 1999). These observations are not new. Thirty years ago, Lyman (1987) showed how there was disparity in mark definition, morphology, identification and interpretation among taphonomists and ended up with a pessimistic note on the solution to the equifinality involved in mark interpretation. When reading his description of the situation then, one could potentially sustain similar arguments today and realize that despite the technological sophistication of the past three decades, cut mark analyses still are under the spell of the Red Queen syndrome. James and Thompson (2015) seem confident that taphonomists could move on from this seemingly dead end beyond another “turn of the experimentationreapplication cycle” (p. 99), but as this work will show, we are still far from that. The main question that we intend to address here is whether cut marks and other bone surface modifications can be studied scientifically. For that to happen, not only do we need standardization in terminology and method, but also confirmation of the objective use of the identifying variables and associated methods. Otherwise said, is there any objective way of assessing mark section shape and its microscopic properties? Domínguez-Rodrigo et al. (2009) proposed a multivariate method combining 16 variables (including microscopic properties of marks) to effectively discriminate among mark types. However, it is not clear that all taphonomists would use these variables objectively in the same way. Before addressing variation in mark identification it is important to assess whether

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variable identification is consistently reproduced by analysts (as any objective method would require) or whether it lends itself to as much variation as mark interpretation, rendering the whole process of mark analysis subjective and, therefore, non-scientific. If analysts do not agree on intrinsic physical (microscopic) properties of marks, they will seldom agree on mark identification, let alone behavioral interpretations of mark frequencies and their anatomical distribution. At the heart of this issue lies the question of whether the analysis of bone surface modifications is a scientific endeavor or an inspirational approach more typical of a postmodern taphonomy. 2. Sample and method Eleven experienced taphonomists from two research laboratories (Institute of Evolution in Africa in Madrid and Institute of Human paleoecology and Social Evolution (IPHES) in Tarragona) were selected to conduct blind test identifications of 14 variables described by Domínguez-Rodrigo et al. (2009) on 30 cut marks made with simple flint flakes. Long bones (i.e., humerus, femur, radius and tibia fragments) were cut marked. For a definition and categorization of each variable see Table 1. The goal was not to identify the mark (the analysts knew that the sample was composed of cut marks from the beginning), but to score how each of the 14 variables involving microscopic mark features were expressed in each cut mark. These two laboratories are very similar in the protocols that they apply to mark analysis so, presumably, their mark identification patterns should a priori have been similar. Marks were analyzed through binocular microscopes using a magnification range between 15x-40x. Each researcher analyzed all the cut marks separately and introduced the scores of each variable as observed in each cut mark in a database. The resulting database was analyzed with a Fisher-FreemanHalton exact test, in which the p-values were simulated via a Monte Carlo method involving 10.000 simulations. The null hypothesis was that all analysts identified variables similarly. A threshold alpha value of <0.05 was used to reject this hypothesis. The analysis was carried out with R (version 3.3.0) (www.r-project. org). To compare the variance of determination of variables, a global multidimensional scaling (MDS) analysis was carried out using a Bray-Curtis distance matrix. For this purpose, the “metaMDS” function of the “vegan” R library was used. This function performs multiple MDS runs and retains the optimal solution. It uses scaling procedures based on dissimilarity matrix calculation and PCA rotation. Stress plots were used to assess if dissimilarities were well preserved in the number of dimensions selected. A canonical covariate analysis (CVA) was used as a confirmation of MDS. This was carried out using dummy transformations of the categorical variables. The resulting matrix was analyzed using the “BiplotGUI” R library. Alpha bags representing 95% confidence intervals and classification regions (based on variance) according to each analyst were carried out. 3. Results Table 2 shows that with the exception of groove trajectory and mark orientation, the remaining variables were interpreted very differently by each researcher. Most variables show significant pvalues of the Fisher-Freeman-Halton exact test, suggesting rejection of the null hypothesis. Identification of crucial and theoretically basic variables such as groove shape and presence of additional features such as shoulder effect, flaking on shoulder and its extent, presence of microstriations, their characteristics and location differed significantly among researchers.

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Table 1 Definition of the variables used for the present analysis. For a more detailed definition see Domínguez-Rodrigo et al. (2009). 1.Trajectory of the groove. 2.Barb. 3.Shape of the groove. 4.Symmetry of the groove (in cross section). 5.Shoulder effect. 6.Presence of flaking on the shoulders of the groove. 7.Extent (width) of flaking. 8.Internal microstriations. 9.Microstriations trajectory. 10.Shape of the microstriation trajectory. 11.Location of microstriations. 12.Mark orientation. 13.Microabrasion 14.Overlapping striations

Straight (a), curvy (b) or sinuous (c). This variable is applied to the outline of the mark, without taking into account the presence of barb ewhen it is present- at the end of the mark. Presence (a), or absence (b) Narrow V- shape (a) and wide V- shape (\_/) (b). In V its shape is deeper than wide. In the case of \_/the base of the groove is horizontal, and it is wider than deep. Symmetrical (a); asymmetrical (b) Presence (a), or absence (b) Presence (a), or absence (b). In some cases flaking inside the groove of the mark can be identified. Over more (a) or less (b) than one-third of the trajectory of the shoulder or inside the groove of the mark. It can be absent (c). Presence (a) or absence (b) Continuous (a), or discontinuous (b) Straight (a) or irregular (b) (curved, sinuous, or in combination). On the walls of the groove (a), on the bottom (b) or on both (c). Marks can show an oblique angle to the main axis of the bone specimen or being parallel or perpendicular to this axis. This is constituted by very fine striae that occurs on the bone surface, not necessarily linked to the mark and commonly found in trampled bone, caused by very small sedimentary particles. Additional striae running across the main groove with an oblique angle.

A MDS analysis provided a convergent solution after 20 tries. The stress level was 0.026. When comparing the cut mark sample studied by each analyst to the variables analyzed, a stress plot showed an optimal ordination distance according to the dissimilarity matrix for the resulting MDS solution (Fig. 1). Most of the divergences detected among analysts were related to the location of microstriations and their properties (presence, discontinuity, trajectory). This was followed by the identification of symmetry, groove shape, barb and shoulder effects (Fig. 1). When comparing the variables analyzed to each analyst, the MDS solution was also achieved via a perfect fit between the ordination distance and the dissimilarity matrix (Fig. 2). Here nonbinary variables were introduced individually by each factor with the intention of further identifying differences among analysts. The resulting MDS shows a more detailed list of variables which explain most of the variance in the divergence of the resulting interpretations. Microstriation associated features resulted the most divergent, together with presence of microabrasion, barbs, overlapping striations, symmetry and shoulder effect. The interesting part of this MDS analysis is that it shows distances among analysts and their relations with the variables. F1 is the most divergent analyst. This can be explained because out of the 11 researchers, F1 is the least experienced analyst. This stresses that experience is a major factor in conditioning mark interpretation. However, even when obviating F1, an even division of analysts around the first dimension is documented pulling in opposite directions (Fig. 2). To

determine if F1 had a strong leverage on the final solution, we conducted a Fisher-Freeman-Halton exact test on the sample excluding F1 and the results indicate that divergences in variable interpretation still exist, with researchers differing significantly in the interpretation of 10 out of the 14 variables (Table 2). This difference in the interpretation of most variables can also be documented in the CVA (Fig. 3). The CVA shows widely ranging confidence intervals for each analyst and very divergent classification areas corresponding to the spaces comprising most variance. The bigger the classification area of any given analyst, the wider his/ her divergences with the rest of analysts. The spreading of the centroids clearly shows the different interpretations made by each analyst. F1 stands in stark contrast again with the rest of researchers. However, there are wide differences in the classification areas of the other analysts. Some show some concordances and are centrally located with small areas, whereas others, displaying higher variance show larger areas that extend centripetally. The combination of all these analyses show how widely different the interpretation of most (12 out of 14) of the variables among researchers is. This indicates that objective replication of feature identification does not exist, rendering the whole interpretation of marks a subjective process. If this is documented in researchers from two laboratories that follow similar protocols, one may expect significantly wider divergences when comparing researchers from laboratories with different protocols. 4. Discussion

Table 2 P-values of the Fisher-Freeman-Halton exact test, including all analysts and excluding the outlier F1. Numbers in bold indicate significant differences.

groove trajectory presence of barb mark orientation groove shape symmetry shoulder effect flaking on shoulder extent flaking on shoulder overlapping striae presence internal microstriations microstriation continuous microstriation trajectory location of microstriations presence of microabrasion

all analysts

all except F1

0.2224 <0.000 0.8896 0.0222 <0.000 <0.000 <0.000 0.0009 <0.000 0.0489 0.0396 0.0134 <0.000 <0.000

0.0193 <0.000 0.8877 0.0104 <0.000 0.0105 0.0027 0.0012 0.0404 0.7834 0.5249 0.0171 <0.000 <0.000

Over the past few years, we have emphasized that no epistemologically valid interpretation can be made of the past when using analogs that are not adequate (Gidna et al., 2013, 2014). One of the most widespread concepts in the use of analogical reasoning for general and dynamic systems stems from Bunge's (1981) definition of analogy. Bunge criticized most analogical reasoning as either undefined or too narrowly defined under isomorphic (and sometimes homomorphic) applications of the concept. He developed a qualitative concept of analogy embedded within the concept that most analogical reasoning in science occurs in dynamic systemic structures. These systems depend on the tight interaction of three components: composition, structure and environment. Composition refers to the collection of components in any of two given systems. Structure refers to the relationship of those components within each system. Environment impacts the structure by determining how the system components interact. This third

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Fig. 1. MDS analysis showing an excellent fit between the observed dissimilarity and the ordination distance produced by the MDS solution (upper left graph) and distribution of variables and marks in the resulting space. Five groups determine the distribution: One central group that does not have an important effect on either the first of the second dimension (presence of microabrasion, overlapping striations, flaking on shoulder, extent of the flaking, and mark orientation), two groups that separate variables in the first dimension (presence of internal microstriations, microstriation trajectory (continuous/discontinuous), presence of straight microstriae and location of microstriations), and three variables that separate marks on the second dimension (presence of symmetry in mark section, shape of the groove and presence of shoulder effect). The variables in both dimensional groups explain most of the sample variance and account for most of the divergence in mark interpretation by analysts.

Fig. 2. MDS analysis showing a perfect fit between the observed dissimilarity and the ordination distance produced by the MDS solution (upper left graph) and distribution of variables and analysts in the resulting space. The eleven analysts are labeled as follows: B1, D1, E1, F1, G1, J1, R1, U1, X1, Y1, Z1. Variable key: flaking, presence of flaking; striae, presence of overlapping striations; microabrasion, presence of microabrasion; shoulder, presence of shoulder effect; barb, presence of barb; microstriae, presence of microstriations; symmetry, presence of symmetric groove shape; micro_w, presence of microstriations on walls; micro_f, presence of microstriations on floor; micro_b, presence of microstriations on both wall and floor; t_sin, sinusoidal trajectory of mark; t_st; straight trajectory of mark; t_c, curved trajectory of mark; long_flake, long extent of flaking on shoulder; short_flake, short extent of flaking on shoulder; o_obl, oblique orientation of mark; o_per, perpendicular orientation of mark; micro_traj, trajectory of microstriations; V-shape, closed V shape of mark; \_/-shape, open shape of mark. Notice the outlying position of F1.

element is of utmost importance because it shows that when comparing two systems (as analogical reasoning does), even if both systems have similar composition their structure may be different on account of the environmental differences of each of them. From this point of view two systems are “substantially analogous” when they share the same components, “structurally (or formally) analogous” when they share similar structures and

“environmentally analogous” when their contexts are similar. To emphasize that not all types of analogical reasoning are equally valid, Bunge (1981) stressed that there were different (heuristic and epistemic) degrees of analogy. The degree of similarity between system A and system B could be proportional to their similarity in composition (degree of substantial analogy), structure (degree of structural analogy) and environment (degree of environmental

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Fig. 3. Canonical variate analysis of the analysts, variables and marks including 95% confidence alpha bags (AB). The eleven analysts are labeled as follows: B1, D1, E1, F1, G1, J1, R1, U1, X1, Y1, Z1. Colors and polygons also show classification regions according to variance. Notice the variability in each analyst and the variable (in size) classification region and AB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

analogy). The most important criterion in using degrees of analogy lies in the combination of the three intertwined parts of analogical reasoning, which is what Bunge (1981) identified as the degree of total analogy, defined as the average of the degree of substantial, structural and environmental analogies shared between two systems. The lack of observance of these principles is not trivial. It has major consequences by creating widely divergent frames of reference leading to gross misinterpretation of hominin behavior in fossil archaeofaunas. This epistemological negligence has been argued, for instance, to be responsible for inadequate referents when interpreting carnivore behavior in prehistoric contexts by using experimental frameworks with captive carnivores or carnivores in highly human-impacted contexts (Dominguez-Rodrigo, 2012; Dominguez-Rodrigo et al., 2015; Sala et al., 2014; Gidna et al., 2013, 2014; Pobiner, 2015; Domínguez-Rodrigo et al., 2012). A crucial aspect that can be added to the balanced use of substance-structure-environment in the elaboration of analogies is that unambiguous links can be made between causes and effects throughout. This is referred to as “control”. A proper analogy should

leave no room for speculation or uncontrolled arguments. This has also been a cause of concern in much of the neotaphonomic experimentation available. When these four elements are applied differently by experimental researchers, it should not come as a surprise that their results and interpretations are widely different. The contrary would be hard to explain. Below, several critical remarks will be made trying to answer some crucial questions about interpretation of cut marks, because they involve a series of experiments that have neglected the balance among substance and structure or overlooked the importance of control while conducting the experimentation, or a combination of all the three. 4.1. Can we objectively identify variables on cut marks? The present study shows that taphonomists are unable to replicate similar identifications of variables. Three different statistical tests show that, although there is some overlap among some analysts, all of them differ significantly in the way most variables are categorized. The MDS analysis shows that there are drastic differences in identification between D1-E1-G1-J1 and U1-R1-Z1.

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Variables such as presence and location of microstriations, shoulder effect, shape and symmetry of the groove, which are crucial for determining in any given mark is a cut mark are the most widely divergently interpreted by the analysts. This is confirmed by the CVA, which indicates that although variance is minimized to identify grouping (resulting in overlap among analysts) the size and non-overlapping areas of the alpha bags differ significantly among taphonomists. As a result, the CVA shows that analysts displays their own classification areas, which differ in size and location in the Euclidean space (Fig. 3). An examination of the centroids of each alpha bag shows how different the interpretation of variables is by each analyst. Different grouping of analysts can be found on the first and second dimensions. Once could argue whether the overlap of some analysts is enough to warrant a similar interpretation of bone surface modifications. To do so, a different study was carried out to show how different mark identification (of a set of cut and trampling marks) was among three of the closest analysts of the present study (Domínguez-Rodrigo et al., 2017; submitted). The results showed significant differences in the percentages of correct identification of both types of marks by these similar analysts (Domínguez-Rodrigo et al., 2017; submitted). This, therefore, further stresses the impact that subjective appreciation of mark properties has for the correct identification of marks and that even analysts that closely categorize mark variables in a similar way may show widely different interpretations of the mark type and agency. 4.2. Can we identify cut marks confidently? Blumenschine et al. (1996) argued that >90% of experimental bone surface modifications could be reliably identified through blind tests. However, this estimate is probably the result of confronting cut marks made with metal tools with tooth marks, whose morphology and overall microscopic criteria are substantially different. A different matter is when cut marks made with stone tools are confronted with trampling marks and other abrasive bone surface modifications. In this case, “subjective” identification of the analyst is frequently highly inaccurate. An “objective” approach to mark identification has been recently advocated through the use of 3D microscopy and geometric morphometric analyses (Bello et al., 2009; Bello and Soligo,
-Gonza
lez et al., 2015, 2016; 2008; Boschin and Crezzini, 2012; Mate Moretti et al., 2015; Pante et al., 2017). Recent experimental work on cut marks using confocal microscopy is very promising (e.g., Pante et al., 2017), although the technique still needs developing properly before being reliably used for interpreting fossils bone surface modifications. Although the experimental sample used by Pante et al. (2017) is not big enough for solid statistical analysis, the main shortcoming in their study is that the final mark shapes that they analyzed are not the original shapes, but derived shapes through the use of polynomial algorithms that “modify” the original shape as captured by the profilometer due to the presence of discontinuities and gaps. Even if assuming that these derived mark sections are as close to the real mark sections as confocal microscopy can get, the resulting sections obtained are highly dependent on the protocol used for scanning each mark. As Pante et al. (2017) acknowledge “the measurements recorded by the three analysts were not identical” because “differences in the position and orientation of the mark relative to the optical pen resulted in small variations in the data captured”. This additional analytical variable, which introduces some distortion, is further biased when several marks are scanned at the same time, because of differences in leveling and orientation of the marks. Regardless of whether one or several marks are scanned at the same time, the average interanalyst error was still as high as 15%. We are currently unaware

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of how these biases may condition mark interpretation. The high accuracy in classification of marks using this method is virtually similar to traditional lower resolution methods (e.g., hand lenses) because the overall morphology of cut and tooth marks is widely divergent (see comment above) (Blumenschine et al., 1996). It remains to be seen how this method is useful in the classification of marks created by different agents or effectors whose modifications are morphologically more similar (i.e., cut marks made by different types of stone tools or trampling). Further work in this regard is highly encouraged. As it stands, this more expensive and timeconsuming method does not seem to show any improvement in mark identification compared to traditional low-resolution methods. Tridimensional and geometric morphometric approaches using photogrammetry has initially produced more promising results (Bello et al., 2009; Bello and Soligo, 2008; Boschin and Crezzini,
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lez et al., 2015, 2016; Moretti et al., 2015; Pante 2012; Mate
-Gonza
lez et al. (2016) observed that cut marks et al., 2017). Mate produced by chert, quartzite and metal could be better distinguished when applying morphometric criteria instead of metric measurements. Metric analyses could only clearly distinguish marks produced with metal from those made with quartzite; whereas differences between metal and chert, as well as between quartzite and chert were not so obvious. In contrast, a morphometric approach allowed for a clear differentiation of the three groups of marks when comparing chert, quartzite and basalt. However, the current use of this morphometric approach is rather limited. Since it uses a variable set derived exclusively from the mark section morphology (same as in Pante et al., 2017), it will incur equifinality when comparing marks made with different effectors that create the same morphology. In Domínguez-Rodrigo et al. (2009), groove section morphology was just one out of 14e16 variables (de Juana et al., 2010). In such equifinal cases, the resolution of using the multivariate “traditional” (i.e, categorical variables) approach or the geometric-morphometric one will need to be tested. In addition, the photogrammetric procedure entails some degree of distortion (despite camera calibration) (same as Pante et al.'s [2017] profilometer-derived sections). For this reason, several 3D microscopic/photogrammetric methods need to be compared to assess their respective resolution on real mark section
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lez et al. morphology. This has been recently done by Mate (2017) who compared the resolution of marks analyzed through a three-dimensional digital microscope, a laser scanning confocal microscope and through photogrammetry methods, concluding the scanning confocal microscopy provided the lowest resolution. 4.3. Can we identify cut marks made with different stone tools confidently? Several authors have differentiated marks types and effectors (i.e., tools) evaluating categorical variables (Greenfield, 2000, 2002, 2005; Bello and Soligo, 2008; Bello et al., 2009; DomínguezRodrigo et al., 2009; Walker and Long, 1977; Walker, 1978). Recently, Val et al. (2017) carried out a series of butchery experiments to determine if cut mark morphology could be related to stone tool type so that inferences could be drawn from tool use from the archaeofaunal assemblage of a Middle Paleolithic site. Their results showed that there was no statistical difference among experimental marks created by different tools. When lacking methodological standardization, as James and Thompson (2015) pointed out, it is relevant to be specific in every methodological step and display images of the marks as identified by authors. Both of these crucial elements are missing in Val et al. (2017) work. No description exists about all the tools used, beyond the assertion that butchery activities of carcasses were “conducted with replicas of

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Mousterian stone tools” (p.18). If the only figure showing some of the tools used is representative of the rest of the lithic assemblage, the equifinal results should not be surprising. The simple flakes show somewhat jagged edges, the point displays unretouched edge (no functional difference from a simple flake), the cleaver shows natural (yet with irregular outline) edge and the Mousterian point and the denticulate show retouched edges on their dorsal side alone. Probably no much difference exists in the morphology of the edges of the tools used: natural edges in simple flakes, points and cleavers and dorsal retouch maintaining a similar straight outline of the sagittal plane of the retouched edges of denticulates and Mousterian points. This contrast with previous experiments where retouched tools bore retouch both on dorsal and ventral sides, thus creating a zig-zagged outline of the sagittal plane of the tool edges
n and (de Juana et al., 2010; Domínguez-Rodrigo et al., 2009; Gala Domínguez-Rodrigo, 2014). This outline is responsible for the wider grooves and more intensive flaking and associated shoulder effects of cut marks made with retouched flakes and handaxes. In previous experiments, simple flakes and retouched flakes were structurally similar in terms of raw material used (de Juana et al.,
n and Domínguez2010; Domínguez-Rodrigo et al., 2009; Gala Rodrigo, 2014). In Val et al.'s work, raw material is very diverse (schist, flint, quartzite), which results in very variable cut mark morphology, as recently demonstrated by geometric morphometric
-Gonza
lez et al., 2016). The studies (Dewbury and Russell, 2007; Mate heterogeneity of raw materials in combination with the edge types described introduce a background noise that precludes any resolution from the interpretation of cut marks, which is caused by the tools used. Yravedra et al. (2017b) have recently shown that only similar tools made with the same raw materials represented at any given site can be analogically valid to discriminate raw materials used during butchery in archaeological contexts. However, the lack of concordance of Val et al. (2017) work with previous experimental studies probably is also strongly determined by the different interpretation of the variables used, as emphasized in the present study. For example, their description of “multiple marks” is not the same as originally described by de Juana et al. (2010). There, it was specified that those were marks accompanying the main (broader) groove and caused by a single stroke, regardless of their orientation (see de Juana et al., 2010, Figures 3 and 4). Given the lack of control in Val et al.'s method, their definition of “multiple mark” was “a cluster of marks that were not separated by > 1 mm and followed the same general orientation“ (p. 20), because “it was not always possible to know with how many strokes each mark or cluster of marks was associated“. However, the application of this method probably embodies also marks from separate strokes following the same direction and disregards non parallel marks which could have been caused by the same slicing stroke. Another example is documented in one of the two photographs that Val et al. published. In their Fig. 6, what they call “ancillary grooves” is what de Juana et al. (2010) and DomínguezRodrigo et al. (2010) refer to as “shoulder effect” (if the scale in the image is right). This is a good example of how different microscopic variables are interpreted diversely by different analysts, as the present work shows. In their Fig. 7, what they refer to as “type 2” marks is what de Juana et al. (2010) describe as “multiple marks” if they were produced by a single stroke, which can neither be confirmed nor refuted. Another example is their identification of “fork-shaped” (FS) marks, which can be seen in their Fig. 8. They identify as FS marks the confluence of two similarly-sized independent striations, usually with overall straight trajectories. In contrast, FS marks described by Juana et al. (2010, see Figures 2 and 3) were usually hierarchical, with one broader groove and they involved multiple striations, with one of the two grooves of the fork usually following

a curved trajectory. The appearance of several of the FS marks in Val et al.'s Fig. 8 is palimpsestic; that is, caused by redundant stroking on the same area causing connection of independent grooves, which sometimes creates “intersecting” marks as described by those authors. Once again, this interpretation can neither be supported nor disproved, given the lack of control in mark generation. This is just a limited example showing that although there was an attempt to adopt standardized methodology by Val et al., their understanding of the variables was very different from the original publications, which results in non-standardized praxis (James and Thompson, 2015). It should then cause no surprise when no match between frequencies of specific mark types and features were reported by Val et al. (2017) and previous studies. A similar experimental study made by Monnier and Bischoff (2014) testing similarities and differences of marks made by intentional use of unmodified rocks during butchery and unintentional use through bone-rock contact in a tumbler, using the same set of variables, produced a much closer replication of frequencies of cut mark features as described in Domínguez-Rodrigo et al. (2009), despite the substantial experimental differences. This was probably due to a more similar understanding of each variable type than Val et al. (2017) study. Monnier and Bischoff (2014) obtained different frequencies in specific variables because they were not comparing cut marks made with simple flakes and trampling marks, but cut marks made with unmodified rocks and marks caused by rock edges inside the tumbler (this does not reproduce the effect of trampling). The rock has consistency when modifying the bone surface; that is, the edge does not morph as it creates the groove, whereas the sediment particles in trampling marks frequently move changing the abrasive edge and creating more sinuous trajectories and associated features. The reason why Val et al. (2017) are puzzled by their comparison with de Juana et al. (2010) results is that they did not understood a crucial part of Juana et al.'s methodology that enabled them to exert control on the morphology of marks resulting from every single stroke. The marks were not produced during butchery, as they interpreted (p. 24), but on defleshed bones (de Juana et al., 2010; see Methods) and nothing like defleshing, skinning or tendon removal (as they interpret) was carried out. This facilitated experimental control, but may have biased the resulting marks, because some of the microscopic features identified may not occur if the creation of the mark occurs during butchery. This needs to be tested. Therefore, what Val et al. (2017) refer to as “limit of the multivariate approach” is nothing more than the limit of the experimental path followed by these researchers. The conceptual flaws are responsible for the equifinality, not the statistical tools. 4.4. Can we identify butchering behaviors (i.e. defleshing vs. disarticulation) confidently? Soulier and Costamagno (2017) recently carried out an experiment casting doubt on the ability to differentiate defleshing cut marks from those resulting from disarticulation. They rejected the referential frameworks made by Binford (1981), Nilssen (2000) and Gal
an and Domínguez-Rodrigo (2013), which despite internal differences (and inconsistencies) showed different ways to document both butchering behaviors. Soulier and Costamagno's (2017) skepticism would be a welcome addition to this debate, if it was not because their interpretation impinges highly on a deficient selection of variables and disregard for control during the reconstruction of the cause-effect process. Their experiments were carried out using a methodology that does not reproduce standard carcass butchery by any modern or

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prehistoric human group. They avoided superimposition of marks by different activities (i.e., dismembering and defleshing) on the same bone elements and portions by carrying them out separately. We understand their intention was to produce an unambiguous record of cut mark distribution according to each butchering behavior, but their approach introduces two different types of biases: 1 It interferes with decisions as to how defleshing and dismembering are carried out. In several human groups dismembering precedes complete defleshing; in others, defleshing is prior to disarticulation. The interaction of both behaviors leaves different cut mark patterns. This, therefore, shows that an "aseptic" "just dismembering" model of cut mark patterning is naive (and probably inadequate) if its interaction with defleshing is disregarded. 2 No matter how separately each butchering behavior is experimentally reproduced. The way these authors performed dismembering required the removal of flesh to access tendons and ligaments. This process certainly leaves abundant marks in a regular butchery process, which the authors here cannot simply identify by the butcher mentioning when he/she felt that the tool contacted the bone surface. This process generates marks on bones that are probably uncontrolled for. In our experience during regular butchery, frequently marks are created without a direct physical perception that the tool is contacting the bone surface. Even if accurately taking note of every single tool-bone contact, the authors did not implement an efficient method for differentiating subsequent marks caused by disarticulation. For instance, the accidental cutmarking of a distal femur resulting from defleshing and the subsequent cutmarking on the same spot caused by disarticulation remains undifferentiated in their analysis. How could they have been able to relate any observation of the butcher during the process while the limb was covered in flesh, ligaments and tendons to specific cut marks if the actual location was not clearly visible when the contact was reported? Otherwise said, there is no confident way of differentiating two marks side by side caused by dismemberingdefleshing in their disarticulation experimental set. There is a third and very important bias. Butchery in this work was carried out (with one exception) by archaeologists (novice butchers). As Padilla (2008) showed, archaeologists without any prior knowledge of butchery create unrealistic butchering patterns when compared to people habituated to carcass butchery. Nonexpert butchers leave a significantly higher number of cut marks, with a wider array of orientations and locations than compared to professional butchers. This is especially visible on near-joint bone portions. In this case, this could artificially inflate the number of marks in these portions masking a more patterned distribution of cut marks left by expert butchers when defleshing and disarticulating carcasses. This experimental decision limits substantially the application of the patterns documented in Soulier and Costamagno's (2017) work if the intention is to make interpretation of “professional” prehistoric butchers. This is why other researchers have emphasized the use of professional butchers in this type of experiments. Archaeologists can also “conscious or subconsciously” influence where marks occur. A clear example of this in Soulier and Costamagno's (2017) work is that these authors argue that they produced short cut marks with stone tools and these are not present in experiments with metal tools. This is not accurate. In our butchering experience, short cut marks can easily be generated by the use of knives (especially if the butcher is not novice). A simple look at Soulier and Costamagno's (2017) templates

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shows the overabundance of cutmarks, which contrast with the number of marks reported for expert butchers by Padilla (2008). Having a control problem with the identification of defleshing marks versus disarticulation marks (plus the overabundance of marks caused by novice butchers) invalidates several of the interpretations made of these authors for both behaviors (especially the latter). For instance, any experienced butcher would contemplate with surprise claims that cut marks on the medial side of the humeral trochlea are caused by defleshing, since no flesh overlies the bone surface, as it is protected by medial and collateral ligament and a thick periosteal sheath. Whereas Soulier and Costamagno's (2017) critique of Gal
an and Domínguez-Rodrigo (2013) is correct in mentioning that the experimental protocol of those authors prevent any interpretation of the orientation of marks, it is also true that Gal
an and Domínguez-Rodrigo (2013) did not intend to study mark orientation but have as much control on cut mark location as possible to attribute specific mark occurrences to disarticulation or defleshing. Soulier and Costamagno's (2017) critique that some marks reported by Galan and Domínguez-Rodrigo during disarticulation are too far from the joint to be attributed to disarticulation is unsupported. In the case of the tibia, disarticulation from the metatarsal can indeed leave marks substantially distant from the joint during the sectioning of the long peroneus tertius, the tibialis cranialis and the long digital extensor tendons. The three sections in this Discussion (can we objectively categorize variables, identify tool or raw material type and butchering behavior type) are interwoven. They occupy different positions within a hierarchical inferential framework. The same as MNIMAU-MNE ultimately depend on how NISP are interpreted, the identification of mark type and agency and butchering behavior depends on how the intrinsic morphological and microscopic properties of marks are perceived by the analyst. If perception (and, thus, interpretation) is subjective (i.e., it depends on the analyst and not of objective methods), then the resulting interpretation is not scientific. Objective data derivation is one of the tenants of the scientific method regardless of the epistemological approach (e.g., Bunge, 1999). Replicability must be objective to qualify as scientific. The moment that data are obtained via subjective interpretation of the properties of the object studied and the resulting interpretation and the data themselves are as varied as the analysts, then no objective demarcation criteria exist to differentiate a truthful from a false statement. In taphonomy this translates into two possible outcomes: an objective method that identifies marks similarly regardless of analyst or a subjective method in which replications of the same experimental processes are highly variable and depend on the analyst. The latter does not qualify as scientific. Different frequencies of replication of categories of variables do not apply in this case. We have shown that scientists who identify similarly some mark variables may end up making very different interpretations of mark type and agency (Domínguez-Rodrigo et al., 2017; submitted). These results depict a very pessimistic situation for the scientific taphonomy on bone surface modifications. We would like to conclude with a more optimistic note. Recent use of deep learning methods and algorithms have shown that computers with a small amount of training can handle exponential amounts of variables and information compared to humans, resulting in much more accurate identification of marks than experts that have been working on bone surface modifications for more than 20 years. This pioneer use of deep learning algorithms in taphonomy has shown that computers may be 50% more accurate than human experts in classifying marks, probably because they use objective methods and subjectivity is reduced to the information provided by the human analyst to the computer during training (Domínguez-

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Rodrigo et al., 2017; submitted). However, the use that computers make of this information is subjected to objective mathematical procedures. This constitutes the dawn of artificial intelligence in taphonomy. 5. Conclusion The present study has shown that in the most basic initial methodological steps to the analysis of cut marks (identification of variables on marks), analysts show widely divergent interpretations when exposed to the same marks. This bottom-to-top approach shows that this subjectivity accompanies research as it proceeds to higher levels of inferential quest. Subjectivity is introduced by selecting variables in experiments that do not contemplate with rigour the requirements of a correct analogy (Bunge, 1981). It follows by making decisions that impact how marks are made. It ends with interpretations at the assemblage level, where marks are quantified and analyzed according to their anatomical distribution. In summary, the cases discussed above indicate that we are far from the methodological standardization advocated by James and Thompson (2015). Researchers are frequently using different variables to address the same questions and when using the same variables, they seem to interpret them differently. The Rorschach test is a psychological projective test in which individuals are exposed to perfectly symmetrical inkblots representing figures that are interpreted subjectively according to specific categories, resulting in different appreciations by each individual (Gacono and Meloy, 1994). It could be argued that every time that taphonomists are exposed to cut marks, they experience a similar psychological process in which each researcher sees different things according to their experiences. Epistemologically, one of the defining features of a scientific approach is the reproducibility of results; different researchers using the same methods should arrive at the same results. Failure to do so, as in the case of cut mark identification and interpretation, shows that our procedures do not qualify as scientific yet, despite our aspirations. Too much is still left to the subjective interpretation of variables and their experimental framing and this is reflected in the praxiology of the discipline. This, indirectly, expresses a widespread disregard for the theoretical (epistemological) considerations of how we construct knowledge. Even when looking at the same variables, we perceive them differently to the joy of postmodern partisans. Even for those of us who feel confident in identifying cut marks, we do not manage to convey any justification beyond arguments that can be subjectively interpreted by others. In this regard, our work with cut marks is very similar to cognitive approaches to stone tools, where the inferential steps of prehistoric knapping and the reconstruction of the mental templates of hominins is done through the experience of the experimental knapper (at best) or the lucubrations of the theoretical lithicist (at worst) (working in an epistemological vacuum) instead of using wellframed testable hypotheses that contain premises on their rejectability. Until we do the latter more efficiently, we conclude that cut mark identification and interpretation is an art (with quite a variable endowment), instead of science. Acknowledgements This collaborative work was carried out with support from a Research Salvador Madariaga grant to MDR (Ministry of Education, Culture and Sport, Spain. Ref PRX16/00010). MDR thanks D. Lieberman and the Human Evolutionary Biology Department at Harvard and the Royal Complutense College at Harvard, where part of the analysis of data was conducted. PS, IC, RH, ARH, JM, PM, AP, JM

research is supported by AGAUR (project no. SGR 2014-899) and by the URV (projects 2014, 2015 and 2016 PFR-URV-B2-17), and is framed in CERCA Programme/Generalitat de Catalunya. ARH is the beneficiary of a postdoctoral scholarship from the MINECO Subprograma Juan de la Cierva (FJCI-2015-24144). A.P. is beneficiary of a predoctoral research fellowship (FI) from AGAUR (2015 FI_B1 01104, Agaur/FSE). J.M. is beneficiary of an Erasmus Mundus Doctorate scholarship.

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