A new approach to identify heat treated silcrete near Pinnacle Point, South Africa using 3D microscopy and Bayesian modeling

Murray, John K.; Harris, Jacob; Oestmo, Simen; Martin, Miles J.; Marean, Curtis W.

doi:10.1016/j.jasrep.2020.102622

Cited by 10 publications

(20 citation statements)

References 48 publications

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“…In addition to the change in quality of the toolstone for knapping, heat treatment has other functional benefits such as an increase in edge durability and edge sharpness [35,36], an increase in the cutting edge to mass ratio [37], and heattreated material is more amenable to pressure flaking for retouch or blade/micro-blade production [38][39][40][41]. Heat treatment also causes silcrete to change in color, increase in glossiness, and decrease in surface roughness [12][13][14]. Ultimately, heat treatment technology allowed Middle Stone Age (MSA) humans to improve the quality of silcrete toolstone to make better, and possibly smaller, tools [36,37] while increasing the net-return rate of procuring silcrete raw material [35].…”

Section: Heat Treatment Technologymentioning

confidence: 99%

“…This research has primarily focused on developing methods to identify heat treatment that are both non-destructive and quantitative [10, 14-16, 27, 28, 30]. Two of these approaches exploit changes in surface roughness to detect heat treatment using replica-tape [13,27] and 3D microscopy [14][15][16]. Agam and colleagues [28] apply UV Raman spectroscopy to determine the temperature at which archaeological specimens from Qesem Cave, Israel were heated.…”

Section: Identifying Heat-treated Silcretementioning

confidence: 99%

“…Since the realization that heat treatment alters the flaking parameters of silicate rocks such as chert, jasper, and silcrete, researchers have conducted heating experiments to better understand the relationship between the physical and chemical changes of the stone and its flaking properties [1][2][3][4][5][6][7][8][9][10][11]. Generally, heat-treated siliceous stone becomes more lustrous and smoother [12][13][14][15][16] and sometimes darker and redder [13,[17][18][19][20][21][22][23][24]. Traditionally, analyst observation of these visual and tangible changes was the most common way to identify heat treatment in archaeological assemblages.…”

Section: Introductionmentioning

confidence: 99%

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Portable, non-destructive colorimetry and visible reflectance spectroscopy paired with machine learning can classify experimentally heat-treated silcrete from three South African sources

2022

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The objective of this study was to determine if visible reflectance spectroscopy and quantitative colorimetry represent viable approaches to classifying the heat treatment state of silcrete. Silcrete is a soil duricrust that has been used as toolstone since at least the Middle Stone Age. The ancient practice of heat treating silcrete prior to knapping is of considerable interest to paleolithic archaeologists because of its implications for early modern human complex cognition generally and the ability to manipulate the material properties of stone specifically. Here, we demonstrate that our quantitative, non-invasive, and portable approach to measuring color, used in conjunction with k-Nearest Neighbors “lazy” machine learning, is a highly promising method for heat treatment detection. Traditional, expert human analyst approaches typically rely upon subjective assessments of color and luster and comparison to experimental reference collections. This strongly visual method can prove quite accurate, but difficult to reproduce between different analysts. In this work, we measured percent reflectance for the visible spectrum (1018 variables) and standardized color values (CIEL*a*b*) in unheated and experimentally heat-treated silcrete specimens from three sources in South Africa. k-NN classification proved highly effective with both the spectroscopy and colorimetry data sets. An important innovation was using the heat treatment state predicted by the k-NN model for the majority of replicate observations of a single specimen to predict the heat treatment state for the specimen overall. When this majority voting approach was applied to the 746 individual observations in this study, associated with 94 discrete silcrete flakes, both spectroscopy and colorimetry k-NN models yielded 0% test set misclassification rates at the specimen level.

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Section: Heat Treatment Technologymentioning

confidence: 99%

Section: Identifying Heat-treated Silcretementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Portable, non-destructive colorimetry and visible reflectance spectroscopy paired with machine learning can classify experimentally heat-treated silcrete from three South African sources

2022

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“…The deliberate and monitored heating of lithic materials is well-known from Neolithic (e.g., Roqué-Rosell et al 2011;Schmidt et al 2013a;Santaniello et al 2016;Yegorov et al 2020) and Mesolithic (e.g., Olausson and Larsson 1982;Schmidt et al 2017) sites, but was also demonstrated in Upper Paleolithic (e.g., Bachellerie et al 2019;Weiner et al 2015) and MSA (e.g., Murray et al 2020;Schmidt et al 2013bSchmidt et al , 2015 contexts. A recent study (Agam et al 2021) pointed to the application of intentional heat treatment of flint specifically for the production of blades at Lower Paleolithic Acheulo-Yabrudian Qesem Cave, Israel, more than 300 kya.…”

Section: Lithic Heat Treatmentmentioning

confidence: 99%

Flint Heat Treatment at Late Neanderthal Site Sesselfelsgrotte (Germany)

Agam

Hattermann

Pinkas

et al. 2023

Preprint

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We examine lithic artifacts from Late Neanderthal site Sesselfelsgrotte (Bavaria, Germany) to evaluate the possibility of fire-use and the intentional flint heat treatment performed by Late Neanderthals. We analyzed 1,113 flint pieces from the G-Layers-Complex (~ 60 to 45 kya; Micoquian), and 946 from the Lower-Layers-Complex (~ 115 to 70 kya; Mousterian), based on macroscopic traits associated with the exposure of flint to fire, assigning artifacts to one of three groups: burnt, unburnt, and possibly intentionally heated. Our results show that while both complexes demonstrate clear presence of fire, fire is more common in the younger G-Layers-Complex. Moreover, possibly intentionally heated pieces are significantly more frequent in the G-Layers-Complex, especially among the tools, and specifically among side scrapers, proposing a link between heat treatment and the production of these tools, most probably due to their functional and cultural significance. We therefore suggest the intentional heat treatment of flint in the G-Layers-Complex of Sesselfelsgrotte. The proportions of burnt flint artifacts in both sequences suggest an intensification in fire-use at the site over time, while the appearance of possibly intentionally heated artifacts in the G-Layers-Complex suggests the development of this advanced pyro-technology by Neanderthals, sometime between these two timeframes. Our results are supported by sedimentological and faunal data. We view these results as further indication for the advanced cognitive and technological capabilities of Neanderthals, such that did not fall from these of Early Modern Humans.

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“…These techniques allow researchers to assign probabilities to the phenomenon being studied. For example, Murray et al (2020) developed a novel method combining 3D microscopic analyses of surface roughness and a Bayesian probability model to evaluate if Middle Stone Age silcrete tools from Pinnacle Point 13B (South Africa) had been heat treated. The model measured the probability that a tool has been heat treated,allowed for the continued updating from future heat treatment experiments, and performed with high accuracy.…”

Section: Artifact Analysismentioning

confidence: 99%

The Bayesian Inferential Paradigm in Archaeology

Otárola‐Castillo¹,

Torquato²,

Buck³

2021

Preprint

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Archaeologists often use data and quantitative statistical methods to evaluate their ideas. Although there are various statistical frameworks for decision-making in archaeology and science in general, in this chapter, we provide a simple explanation of Bayesian statistics. To contextualize the Bayesian statistical framework, we briefly compare it to the more widespread null hypothesis significance testing (NHST) approach. We also provide a simple example to illustrate how archaeologists use data and the Bayesian framework to compare hypotheses and evaluate their uncertainty. We then review how archaeologists have applied Bayesian statistics to solve research problems related to radiocarbon dating and chronology, lithic, ceramic, zooarchaeological, bioarchaeological, and spatial analyses. Because recent work has reviewed Bayesian applications in archaeology from the 1990s up to 2017, this work considers the relevant literature published since 2017.

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A new approach to identify heat treated silcrete near Pinnacle Point, South Africa using 3D microscopy and Bayesian modeling

Cited by 10 publications

References 48 publications

Portable, non-destructive colorimetry and visible reflectance spectroscopy paired with machine learning can classify experimentally heat-treated silcrete from three South African sources

Portable, non-destructive colorimetry and visible reflectance spectroscopy paired with machine learning can classify experimentally heat-treated silcrete from three South African sources

Flint Heat Treatment at Late Neanderthal Site Sesselfelsgrotte (Germany)

The Bayesian Inferential Paradigm in Archaeology

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