I find myself constantly fascinated by the human capacity to invent and innovate…

The Laboratory for Traceology and Controlled Experiments (TraCEr) focuses on understanding how early humans used their tools and what that tells us about the evolution of human behaviour. Archaeologists often argue about why stone tools look so different from one site to another, was it because people used different raw materials, did different tasks, or had different traditions? TraCEr tries to answer these questions by studying what tools were actually used for, not just how they were made. Since humans in the past used all kinds of materials (e.g., stone, bone, wood, metal) figuring out tool function is key to understanding how people lived, adapted, and solved problems. TraCEr’s main mission is to push functional studies forward by combining experimental work, new methods, and cutting-edge imaging technology. (enter to learn home)

In this study, we used convolutional neural networks (CNNs) to try and automate the classification of polish traces on stone tools — basically, the shiny or smooth spots that develop on tools from use-wear (e.g., from cutting wood, hide, or bone). We set up experiments with known contact materials (wood, hide, bone) and different use-intensity durations. We also experimented with different imaging parameters (like microscope objective magnification, patch size of image segments) and compared different model architectures (custom CNN vs pre-trained models like ResNet50). We looked into the following questions, * Whether a CNN can reliably tell the difference between polish made by contact with bone, hide or wood. * Whether the duration of use (short term vs long term) affects how easily the model classifies polish * How the imaging setup and model design affect performance (does using a smaller patch of the tool surface versus a larger one matter? Which objective magnification works best? Does a model trained from scratch outperform one that is pre-trained?). We found that CNN approach can classify polish from bone and hide quite well, but struggles more with wood. And also, the models were also able to distinguish polish from short-duration use versus longer use. We also emphasise that to really make this kind of approach robust, the datasets need to be much larger and more varied (different raw materials, contact materials, use contexts) and that imaging/analysis workflows should be optimised. It shows that deep learning has real promise in helping archaeologists analyse use-wear on tools in a more automated, standardised way, which could reduce human bias and increase repeatability. But it also cautions that there are still big challenges, small datasets, limited raw-material/usage variation, and model/generalisation issues mean we’re not yet at the point of “plug-and-play” ML for use-wear. For other researchers (working on archaeological objects, use-wear, materials) this paper suggests that combining controlled experiments + imaging + ML could open new lines of investigation, but we all need to pay attention to dataset design, imaging setup, and model choice.

This study was good fun! We analysed five stone artefacts from the Levantine Middle Palaeolithic, two are engraved Levallois cores from Manot Cave and Qafzeh Cave, one engraved plaquette from Quneitra, and two cortical artefacts (a flake and a retouched blade) from Amud Cave. We used 3D surface‐analysis techniques (morphometry of incision geometry) to characterise the incisions on each artefact (depth, width, angle, pattern). The study shows that by analysing incision geometry and spatial distribution, one can distinguish between engraved artefacts (intentional) and artefacts modified by functional processes. I think, this paper provides empirical evidence that some Middle Palaeolithic hominins in the Levant were engaging in behaviours that go beyond simple tool use — hinting at abstract, non‐utilitarian or symbolic behaviour, which pushes our understanding of cognitive complexity earlier than often assumed. Also, it helps bridge the gap between “tool use” and “symbolic behaviour” in early human evolution, by showing that even some stone tools may have been subject to engraving as part of non-subsistence practices.

In this paper we looked at how early hominins (tool‐making humans) at the site complex Melka Wakena in the Ethiopian highlands chose different stone materials for making percussive tools (like hammerstones). We gathered stone samples of different rock types found in that area. We measured their physical and engineering properties to see how the raw materials might differ in performance. We found measurable differences in the rock types, harder and denser rocks tended to show less volume loss (for example less damage) under the same experimental stress. For example, one rock (glassy ignimbrite) showed very low visible damage at macroscopic scale compared with others. It gives us concrete evidence that early humans weren’t just randomly picking stones; they may have been making informed decisions about which rocks to use for tools based on their likely durability and suitability for the task. This adds to our understanding of cognitive and technological behaviours in deep prehistory. Also, by establishing a baseline of raw material properties, we can later compare actual archaeological tools and see how performance and raw material choice relate.

In this experiment we tested how different stone raw materials perform as tools—specifically looking at four types, flint, dacite, quartzite and obsidian. We cut into pinewood under identical conditions and measured two key things, how deep the tool edge cuts (effectiveness) and how much the edge wore down (durability). We also measured material‐properties like hardness and grain size to see how those link to performance. What we looked at, Does tool edge efficiency vary with raw material type? (Our null hypothesis was that it doesn’t.) Which materials cut the deepest and also resist wear the best? How do traditional categories of high quality vs low quality raw materials hold up when tested quantitatively under use? We found that the different raw materials behaved very differently, flint showed the highest overall performance (deep cuts and moderate wear). Quartzite, though often considered low quality performed surprisingly well once the initial edge fragmentation settled. Obsidian was brittle and wore fast; dacite had good early performance but then plateaued. Additionally, the classic fine‐grain vs coarse‐grain raw material classification (flint is fine, quartzite is coarse) is too simplistic, some coarse‐grained materials (like quartzite here) can be efficient tool materials under certain conditions. For archaeologists this study gives us experimental data that supports the idea that raw material choice was not random. People in the past likely made technological decisions based on performance of the tool‐materials, not just availability. It also suggests that when you see certain raw materials in the archaeological record, you might infer something about the tasks they were used for, the mobility or procurement strategies of tool‐makers, or even maintenance and resharpening practices.

In this study we carried out use-wear analysis on a stone tool collection made of coarse-grained lithic raw materials from the Aterian sequence at Rhafas Cave in northeast Morocco. The idea was to look at how those heavier, chunkier raw materials were actually used, not just what they look like, but how they were worn in practice. We found that coarse-grained lithic tools show distinct use-wear patterns, which suggest active and probably diverse uses rather than passive or incidental wear. The wear on those tools indicates that the tool-makers were not just using whatever stone was at hand, they were making use of coarse-grained raw materials in ways that reflect deliberate choices and functional uses, not just tools for now. This supports the idea that the Aterian assemblages from Rhafas Cave carry deeper behavioural significance, such as choice of raw material, maintenance or curation of tools, and possibly task specialisation are all in the picture.

In this exciting work, we looked at how machine learning (ML) has been used in the field of use-wear analysis (that is, the study of the microscopic and macroscopic traces left on tools and artifacts from being used). We collected and reviewed nearly 50 case-studies spanning material types (lithics, bone, wood), ML algorithms, dataset sizes, and open science practices. We see that use-wear analysis is growing, but still quite limited in scope and diversity. Many studies use small datasets, often from the same institutions, focusing on just a few materials. Thus we argue that to make ML truly useful in this field, researchers need, greater dataset size and diversity, cross-institution collaboration, better documentation of methods, and open sharing of data and code. This review is a call-to-arms for archaeological scientists using ML, it shows what’s working, what’s not yet working, and what needs to change so that ML can genuinely help use-wear analysis rather than just being a flashy add-on. It helps researchers understand how to build better models, share better data, and ask better research questions.

We ran a bunch of controlled cutting experiments using stone tools, some with simple straight edges and others knapped and retouched. We used a mechanical tester to make each tool perform 2000 identical cutting strokes—either on softwood or on synthetic bone, so we could see how use-wear develops under tightly controlled conditions. While doing that, we 3D-scanned the tools and used a method borrowed from dental research (Occlusal Fingerprint Analysis, OFA) to simulate exactly where the tool should touch the worked material during cutting. We wanted to know whether the areas predicted by the simulation actually match where polish and other use-wear traces show up on the tools after the experiments. We also tested how much the shape of the tool and the hardness of the worked material influence where and how wear forms. The simulated contact zones and the real use-wear areas overlapped surprisingly well—especially for wood. The simulations tended to predict slightly bigger contact zones than the polish we actually saw, but the overall patterns matched. This approach could be used to build wear prediction libraries that help archaeologists understand how different stone-tool shapes and materials would wear during actual use. It could even help distinguish true use-wear from natural damage. Basically, the study shows that combining controlled experiments, 3D modelling, and simulations can give us much clearer insight into how stone tools were used in the past.

In this study, our team built a controlled mechanical experiment using standardised stone-tool blanks (based on the bifacial backed knives known as Keilmesser from the Late Middle Palaeolithic). We varied two key design features, raw material (flint vs siliceous schist), and edge angle (35° vs 45°). We then put each tool through the same cutting or carving movement under machine-controlled conditions and measured how well it performed—how deep it cut, how much damage it took, how durable it remained. The main questions focused on, 1) Does the type of rock (its hardness, structure, flaws) affect how well the tool performs?, 2) Does the angle of the edge (sharper vs slightly flatter) affect efficiency (how much work it does) and durability (how much it wears down or breaks)?, and finally, 3) What do these results tell us about the decisions that human tool-makers in the Neanderthal era might have been making when selecting raw material and designing edges? Results showed that both raw material and edge angle matter. Tools made of flint (harder material) showed less obvious damage at the sharper angle compared to those made of siliceous schist, which were more brittle and exhibited more micro-fracturing. Additionally, the edge angle had a real effect, sharper edges (35°) usually cut deeper but also tended to wear faster. In some cases, the 45° angle fared better in durability for carving movements. These results suggest that tool-makers weren’t just picking stones randomly or designing edges arbitrarily, they likely were making decisions based on how different combinations of raw material and geometry would perform for the required tasks.

Our investigation reported in this article focus on the study of over 2,000 lithic (stone) artefacts excavated at the site of Bacho Kiro Cave in Bulgaria from layers dated to about 45,040–43,280 cal BP. We analysed raw materials using petrography and identified many artefacts made from flint sourced some 130–190 kilometres from the cave; we also mapped and classified how the tools were produced, modified and re-worked. We applied chaîne opératoire and reduction sequence approaches (i.e., when and how blanks were produced, how tools were curated and re-used) to understand not just “what” tools were made, but how and where they were used and modified. We found that a large proportion of tools were made from non-local flint, Lower Cretaceous flints from the Ludogorie area (≈160-190 km away) accounted for ~62% of identified types; Upper Cretaceous flints from the Danube region (~110-130 km away) also appear in the assemblage. The technology is dominated by flakes (35.87%), blades (15.18%), and retouched blanks (15.04%). Minimal core production suggests that many tools were brought in already worked (or blanks transported) and then maintained rather than produced fully on site. In summary, the assemblage suggests deliberate selection and transport of finished or semi-finished products and a high level of tool curation — not simply production and discard based on local materials.

This study combines experiments and ethnographic studies to investigate how stone had-oc tools were selected and used in specific techniques applied to the production of plastic mineral matter. We use confocal microscopy to identify and quantify the traces of use left during these processes and later compare with those found on archaeological artefacts from the Late Natufian in the Levant.

Get in touch!