Mar 19, 2025
The Dichotomy of Human Decision-Making: The Impact of Lithic Raw Material Properties on Stone Tool Efficiency.
- David Nora1,2,
- Joao Marreiros3,
- Walter Gneisinger3,
- Telmo Pereira4,
- Antonella Perdergnana5
- 1Institute of Archaeology of the Hebrewe University of Jerusalem, Humendy Lab;
- 2ICArHEB (Interdisciplinary Center for Archaeology and the Evolution of Human Behaviour);
- 3TraCEr. MONREPOS, RGZM;
- 4Universidade Autónoma de Lisboa;
- 5South Tyrol Archeological Museum, Bozen, Italy. 17 6 Institute of Evolutionary Medicine (IEM) – University of Zurich (UZH), Switzerland. 18 7 Institute for Mummy Studies, Eurac Research, Bozen, Italy.
- TraCErMonreposLEIZA
Protocol Citation: David Nora, Joao Marreiros, Walter Gneisinger, Telmo Pereira, Antonella Perdergnana 2025. The Dichotomy of Human Decision-Making: The Impact of Lithic Raw Material Properties on Stone Tool Efficiency.. protocols.io https://dx.doi.org/10.17504/protocols.io.bp2l6djq1vqe/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: March 04, 2025
Last Modified: March 19, 2025
Protocol Integer ID: 123772
Keywords: Experimental Archaeology, Controlled Experiments, Lithic Raw Materials, Stone Tool Edge Efficiency, Effectiveness, Durability, Human Decision-Making, stone tool efficiency, including hardness, impact of lithic raw material property, lithic raw material property, different lithic raw material, significant differences in the efficiency, mechanical performance, physical properties of various raw material, distinct raw material, edge efficiency, various raw material, raw material, different types of raw material, efficiency, quartzite, durability, physical property, grain size, edge wear, ratio between effectiveness, tool
Funders Acknowledgements:
Seed Fund’21 of ICArEHB
Grant ID: UIDP/04211/2020
European Research Council - TransCause-Investigating Pleistocene population dynamics in the Southern Caucasus
Grant ID: 948015
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Abstract
This protocol reports a controlled experiment to test the mechanical performance, focusing on the efficiency (ratio between effectiveness and durability) of four distinct raw materials (quartzite, dacite, flint, and obsidian). Our study addresses the null hypothesis: "Edge efficiency does not vary according to the different lithic raw materials." Efficiency is accessed by the combination of penetration depth (proxy to measure effectiveness) and edge wear (proxy to measure durability). These two proxies were measured, and the results correlated to the physical properties of various raw materials, including hardness and grain size. Our results show significant differences in the efficiency between the different types of raw materials.
This experiment was performed sequentially in 3 stages, and each sample was recorded at stage 0 (before the experiment), stage 1 (125 cycles), stage 2 (125 cycles), and stage 3 (500 cycles after the experiment was complete).
Guidelines
All the guidelines are present in the following steps.
Materials
1 Gutanasar Block 1 Dacite Block 1 Flint Block 1 Quartzite Block
Protocol materials
Acetone ≥99.5 %Carl RothCatalog #5025.6
Plurafac LF 901BTC Europe GmbH, Rheinpromenade 1, D-40789 MonheimCatalog #-
Troubleshooting
Safety warnings
No warnings
Samples Preparation
Selection and Preparation of Lithic raw materials samples
Selection of Lithic Raw Materials:
End of step 1.
Sample preparation:
The goal of step 2 is to ensure uniformity in morphology and length of the edges, all specimens were saw-cut with a length of 30 mm, a width of 25 mm, a thickness of 10 mm, and a 45° unifacial edge (representative of the active edge, see Conford (1986, pp.34)) by a diamond band saw. Each piece was assigned a unique ID and a bar code.
Blank preparation:
After block selection, we used a lapidary rock saw to create a parallelepiped morphology of 60 mm (length), 25 mm (width), and 10 mm (thickness).
3 Gutanasar parallepepide blank morphology
3 Dacite parallelepiped blank morphology
3 Flint parallelipiped blank morphology
3 Quartzite parallelepiped blank morphology
Note: Add an ID to each blank / Weight each sample.
Equipment
Goliath 450
NAME
Lapidary rock saw
TYPE
Steinschleifmaschinen & Lapidary tools Ltd.
BRAND
-
SKU
LINK
-
SPECIFICATIONS
Leeb Reboung Hardness test:
Leeb Hardness Test (LHT). LHT method is built on the dynamic impact principle: An impact body's rebound velocity (Vrebound)is recorded and reported relative to its downward, or impact velocity (Vimpact). The impact body rebounds faster from harder surfaces than softer ones. The magnitude of Vrebound depends on the elasticity of the surface and any energy loss by plastic deformation. For more please check the following literature:
Citation
LINK
Citation
LINK
The equipment used to measure the rebound hardness
Equipment
Equotip 550
NAME
Portable hardness tester
TYPE
Proceq
BRAND
-
SKU
LINK
Leeb C probe
SPECIFICATIONS
a) Calibration of the LHT device.
b) Since the blank samples did not fulfill the industrial requirements for minimum sample size and weight, coupling paste (proceq) was used between the sample and the base (granite base of known hardness).
c) Couple the blank (#2.1) with the granite slab and measure. (Repeat the measure procedure 10 times)
For each blank (#2.1) the instrument Leeb hardness tester device was used vertically (Yaşar & Erdoğan, 2004).
d) Export to a .csv file for further statistical analysis and a .pdf report (see SOM4 ) for each sample, including the Leeb instrument reference and all additional information.
General view of the LHT devive with a probe C.
Standardization of the blank edge
Blanks from (#2.1) were clipped to a diamond saw machine to create an edge angle of 45 degrees.
Equipment
310 CP
NAME
Diamond band saw
TYPE
Exact
BRAND
-
SKU
LINK
After all blanks (#2.1) pass through the step 2.3, they return to the Goliath 450 to standardise the length to 30 mm.
Coordinate system by ceramic beads.
Three ceramic beads were added to the surface of each blank to facilitate comparison between experiment cycles. For more information, please see the following citation.
Citation
LINK
General photo of the Obsidian blank wiht the beads coordinates.
Goal of the step 2:
3 Gutanasar blank length 30 mm, a width 25 mm, thickness 10 mm, and a 45° edge
3 Dacite blank length of 30 mm, a width of 25 mm, a thickness of 10 mm, and a 45° edge
3 Flint blank length of 30 mm, a width of 25 mm, a thickness of 10 mm, and a 45° edge
3 Quartzite blank length of 30 mm, a width of 25 mm, a thickness of 10 mm, and a 45° edge
Preparation of lithic samples. (A) Standardised cutting of blocks into dimensions (30 mm x 25 mm x 10 mm) with a 45° edge. (B) Processed samples labelled and packed. (C) Array of prepared lithic samples from all raw materials.
End of step 2.
Cleaning
In this experiment, we applied the standard cleaning procedure of lithic samples practiced on the Tracer lab, Monrepos, LEIZA. (shared with protocols
Protocol
CREATED BY
Hannah Rausch
After sample preparation (#2), the saw-cut blank samples are cleaned to remove any residues that accumulated during preparation:
1. Blank samples are rinsed with tap water.
2. Tool samples are individually packed in plastic bags filled with demineralised water and a surfactant:
100 mL Cleaning solution
1 Mass / % volume Plurafac LF 901BTC Europe GmbH, Rheinpromenade 1, D-40789 MonheimCatalog #-
3. The bags are suspended in a preheated ultrasonic bath for 5 minutes at 100 KHz.
Equipment
Heated ultrasonic bath
NAME
Sonorex Digitec DT255H
BRAND
62083
SKU
00:05:00
40 °C
4. Lastly, each blank edge was treated with acetone applied with cotton swabs, and left to dry at room temperature.
Acetone ≥99.5 %Carl RothCatalog #5025.6
End of step 3.
Contact Material
As a passive sample we used a slab of pine wood (Pinus sylvestris) with the following dimensions:
20 mm Lenght
165 mm Width
20 mm Thickness
Note
Pine wood (Pinus sylvestris) plank was obtained from the Bauhaus commercial bricolage centre in Germany. European Pinewood has an average density of 550 kg⁄m³ and a moisture content
of 12 to 15%.
The contact material was used to perform all bi-directional movements.
Pine wood (Pinus sylvestris) plank.
Sample documentation
The documentation protocol was performed the same way for each stage of the
experimental design: cleaning of the sample, weighing, 3D scanning, and digital microscope imaging.
Documentation protocol
General Photo
Digital photos of the tool samples and the experiment workflow are taken to acquire general documentation images.
Equipment
D610
NAME
Digital camera
TYPE
Nikon
BRAND
-
SKU
Objective:
AF-S NIKKOR 24-85 mm 1:3,5-4,5G ED VR
Automatic focus setting
SPECIFICATIONS
2D imaging of the sample's edge
This is a standard procedure in the Tracer Laboratory to ensure data acquisition for future studies, several other projects share the same protocol, such as:
Protocol
CREATED BY
Hannah Rausch
Overview images of the edge are acquired for each side (dorsal, ventral) of the tool samples using a digital microscope Smartzoom5. The following steps are followed:
- The sample is calibrated with the coordinate beads using the 1.6x/0.1 objective.
- A 3D image of the edge is acquired with the 1.6x objective using the tile-stitching and z-stack functions.
- Settings: the ringlight brightness is identical for all images and the exposure is adjusted as needed. The blending function is activated to acquire smoother borders between the tiles.
- An EDF image of the edge is acquired and exported as CZI and PNG files.
Equipment
Smartzoom5
NAME
Digital microscope
TYPE
Zeiss
BRAND
https://www.zeiss.de/mikroskopie/produkte/imaging-
SKU
1.6x objective; EDF-stitching images
SPECIFICATIONS
ZEISS Smart zoom DAC3-2_left_back_view_Right_front_view_top_ to_bottom_cycle_0_125_250_500
ZEISS Smart zoom DAC3-2_Profile view, A – Sample_ ID; B – cycle 0; C – cycle 125; D - cycle 250; E – cycle 500
3D Surface morphology documentation
All three-dimensional acquisition was done with the following equipment and followed these steps:
A). Turning on the system
Before use, the 3D structured light scanner must be warmed up one hour in advance.
B). Calibration
The scanner was calibrated with the appropriate calibration plate. The software would do an automatic accuracy check.
C). Placing the sample
Each sample was placed in the middle of the turntable, and all samples were scanned upright.
- The tool samples are scanned with the automatic measuring function and the turntable, which takes scans while rotating 360°. Depending on the final point cloud, we did 8 to 10 stops in this setting.
- The scans are merged at the working computer using the DAVID-4-PRO software and exported as STL files.
Equipment
HP 3D Structured Light Scanner Pro S3 DAVID SLS-3
NAME
Structured Light Scanner
TYPE
HP
BRAND
SLS-3
SKU
LINK
FOV: up to 120 mm / Resolution: up to 0.06 mm
SPECIFICATIONS
Software
DAVID-4-PRO
NAME
HP
DEVELOPER
Edge efficiency Experiment
General variables settings:
3 Gutanasar
3 Flint
3 Dacite
3 Quartzite
Experimental workflow
Experiment setup
Sample Attachment and Measurement:
- Attach the samples (plank and lithic) to the machine, ensuring proper alignment.
- Measure the sample (3 cm).
- Set a safety zone on the Z-axis (50 cm).
- Establish the point of contact with the contact material (130 cm).
- Define the maximum penetration depth (14 cm).
Alignment: Ensure all samples are precisely aligned with the contact material’s surface to achieve a clean and straight cut at the start of the experiment.
Running Stages: Each sample undergoes three running stages:
0-250 strokes
251-500 strokes
501-1000 strokes
Experiment setup
Stroke and Cycle Definition:
A single stroke is one complete linear bidirectional movement from point A to point B.
Drive measurements are recorded in cycles, where one cycle equals the path length from point A to point A.
Stroke and Cycle Definition
Data Recording and Export:
Up to 20 points are recorded per cycle path.
All recorded points are exported to a .txt file, containing measurements for each selected sensor.
Repeat Imaging Step
Repeat
Each lithic sample is removed and documented at every experiment's stop.
Data processing
Point cloud and Mesh treatment
For point cloud and mesh treatment we use the following software and workflow:
Software
GOM Inspect
NAME
Hotfix 2, Rev. 111729, build 2018-08-22
OS
GOM
DEVELOPER
GOM Inspect Workflow
Mesh Treatment
- Import the stage 0 .stl file (alternative: ply) import STL as target element type: mesh → ok
- Eliminate Mesh Errors a. RMC (right mouse click) Select all points of Element b. Operations → Mesh → Other → Eliminate Mesh Errors → Apply
- Align 3D model a. RMC on the Left back of the screen X, Y, Z icon to establish Matrix b. RMC Select all points of Element c. Operations → Alignment → Manual Alignment → Set Matrix (Settings1) d. Change Rotation values until your object is aligned e. Change Translation to set your object to the 0/0/0 (zero) point2 → Ok
- Cut 3D model a. RMC → Select/Deselect Trough Surface (Ctrl+Shift+Space) → Close the select area LMC b. Delete select 3D area (Ctrl+Del)
- Export Mesh as a .stl file. (File → Export → .stl).
- Close Project.
Aligned Cut
- Cut 3D Models aligned a. Import the stage 0 (zero) .stl file aligned cut (add Part) b. RMC Select all points of Element → Operations → CAD → Actual Mesh to CAD
- Import mesh stg250_rawdata .stl file (second 3D model) a. Operations → Alignment → Initial Alignment → Prealignment3 (At this stage both 3D models are overlap). b. Cut the mesh at the same line as the stage 0 (zero) → RMC → Select/Deselect Trough Surface (Ctrl+Shift+Space) → Close the select area LMC
- Export Mesh as a .stl file. (File → Export → .stl)
- Repeat the aligned cut for the other 3D models.
FLT10-stg0
Measuring Efficiency
In this experiment, to address the lithic raw material efficiency, we suggest the
following equation:
Effectiveness (the degree to which something is effective/done, is represented by penetration depth (PD).
Durability (the degree at which deterioration occurs or volume loss) is represented by edge wear (Ew).
Durability data acquisition
Durability is the sum of material loss from the edge measured on each sample after and before use.
CloudCompare (C2M)
CouldCompare is a software application that processes three-dimensional point clouds and triangle meshes. The C2M (cloud to mesh) tool was employed in the present study to assess the surface deviation between triangular meshes. The comparisons were conducted for all lithic samples using combinations of 0 to 125 cycles, 125 to 250 cycles, and 250 to 500 cycles.
CloudCompare workflow:
- Import the two .stl files (before and after
- Align 3D models
a.Select the two meshes.
b. Registration and Match Bounding-box centers
c. Registration and Align (points pair picking)
d. Registration and Fine registration (ICP)
3. Compare the two meshes1
a. Select the two meshes
b. Compute Cloud/Mesh distance
c. Compute
d. Select only the “registered” mesh
e. In Properties/SF display params/Display ranges
i. Set displayed values to 0.2 mm (3D Scanner accuracy and baby powder layer)
ii. Make the Color Scale visible
iii. In the Parameters bar, unselect “show NaN/ out of range values in grey”
4. Get comparison data
a. Select only the “registered” mesh
b. Show Histogram
c. Export Histogram to a .CSV file
d. Export Histogram to image to .png
Combinations: Stage 1(0-125 cycles); Stage 2 (126-250 cycles), Stage 3 (251-500 cycles) and Stage 1 with Stage 3 (0-500 cycles)
Citation
LINK
Software
CloudCompare
NAME
v2.13.alpha [macOS 64-bit] Compiled with Qt 5.15.2 License: GNU GPL
DEVELOPER
SOURCE LINK
Example of the C2M workflow.
Effectiveness data acquisition
Effectiveness (penetration depth) is measured by the depth sensor, which is not an absolute figure but the measured penetration depth minus blade attrition for each cycle. From each stroke, the Smarttester takes twenty measures. Those same measures are then recorded and saved to be imported for quantification and visualisation for every stroke and then grouped for the representation of each lithic raw material.
SMARTTESTER‱ data analysis
All CSV files with experimental data (step #6) are combined into one CSV file. The sensor data recorded throughout the experiment is then analysed in R. The analysis includes descriptive statistics and plotting the variables velocity, force, penetration depth, and friction data. All packages used are cited in the R script (see #10).
Example of the varaiables plotted
Data analysis
The data acquired within the experiment (#6) and from the data processing (#9) were analysed in R to test the null hypothesis that "Edge efficiency does not vary according to the different lithic raw materials."
The following group of analyses is proposed:
Analysis for LHT) This script reads and plots the data from the Leeb Rebound hardness. Hardness was measured on each rock using the device Equotip 550 Leeb Impact device C (HLC).
Analysis of 3D) This script reads and plots the data from the 3D volume loss, which was calculated using CloudCompare cloud-to-mesh distance tool.
Relationship Analysis) This script analyses the relationship between the different variables.
For more in-depth info please check: https://github.com/jmmarreiros/noraetalstonedura2025.
Software
R Studio Desktop
NAME
The R Studio, Inc.
DEVELOPER
SOURCE LINK
Results
Available in open access on Zenodo:
Citations
Step 2.2
A. G. Corkum · Y. Asiri· H. El Naggar · D. Kinakin. The Leeb Hardness Test for Rock: An Updated Methodology and UCS Correlation
10.1007/s00603-017-1372-2Step 2.2
Sasan Ghorbani · Seyed Hadi Hoseinie · Ebrahim Ghasemi · Taghi Sherizadeh · Christina Wanhainen. A new rock hardness classification system based on portable dynamic testing.
10.1007/s10064-022-02690-3Step 2.5
Ivan Calandra & Lisa Schunk & Alice Rodriguez & Walter Gneisinger & Antonella Pedergnana & Eduardo Paixao & Telmo Pereira & Radu Iovita & Joao Marreiros. Back to the edge: relative coordinate system for use-wear analysis
https://doi.org/10.1007/s12520-019-00801-yStep 9.1
David Nora. The role of lithic raw materials on tool performance and use: The efficiency and durability on stone tools edge.
http://hdl.handle.net/10400.1/17581