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

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.

Exploring variability in lithic armature discard in the archaeological record.

The invention of projectile technology had important ramifications for hominin evolution. However, the number of stone points that could have been used as projectiles fluctuates in archaeological assemblages, making it difficult to define when projectile technology was first widely adopted and how its usage changed over time. Here we use an agent-based model to simulate a hunter-gatherer foraging system where armatures are dropped according to their usage. We explore the impact of interactions between human behaviors and the environmental constraints of a data-informed landscape on the distribution and number of lithic armatures found in archaeological assemblages. We ran 2400 simulations modeling different population sizes, rates of hunting with projectiles, and tool curation levels. For each simulation, we recorded the location of dropped armatures and calculated the number and percentage of used armatures that were discarded at habitation camps vs. lost during hunting. We used linear regression to identify the demographic, behavioral, and environmental factor(s) that best explained changes in these numbers and percentages. The model results show that in a well-controlled environment, most armatures used as projectile weapons are lost or discarded at hunting sites; only ∼4.5% of used armatures (or ∼2 armatures per year of simulation) are discarded in habitation camps where they would likely be excavated. These findings suggest that even rare hafted armatures found in the Early and Middle Stone Age could indicate a well-established use of such tools. Our model shows that interactions between reoccupation of archaeological sites, population size, rate of hunting with projectile weapons, and tool curation levels strongly influence the count of lithic armatures found in archaeological assemblages. Therefore, we argue that fluctuations in the counts of armatures documented at archaeological sites should be evaluated within their demographic and environmental contexts to better understand if they reflect spatiotemporal changes in hunting behavior.

The extended evolutionary synthesis and human origins: Archaeological perspectives.

Recent developments in evolutionary biology have led to a call for an extension of standard evolutionary theory, with its emphasis on processes such as selection and drift, into a much larger theoretical framework that includes processes such as niche construction, developmental plasticity, inclusive inheritance, and developmental bias. Skeptics argue that these processes are already subsumed within the standard theory and thus an extension is not required. Here, we outline what this evolutionary "rethink" might mean for the study of human origins. Specifically, can paleoanthropologists benefit from an extended theoretical toolkit? The papers in this special issue suggest it can be useful but may not be necessary, depending on the kinds of questions that are being asked.