New neural network differentiates Middle and Late Stone Age toolkits


Date:
August 26, 2020
Source:
Max Planck Institute for the Science of Human History
Summary:
The change from Middle Stone Age (MSA) to Later Stone Age (LSA)
marks a major cultural change amongst our hunter-gatherer ancestors,
but distinguishing between these two industrial complexes is not
straightforward. New researc demonstrates how machine learning can
provide a valuable tool for archaeologists, and can identify what
differentiates the MSA and LSA.



FULL STORY ==========================================================================
MSA toolkits first appear some 300 thousand years ago, at the same time
as the earliest fossils of Homo sapiens, and are still in use 30 thousand
years ago.

However, from 67 thousand years ago, changes in stone tool production
indicate a marked shift in behaviour; the new toolkits that emerge
are labelled LSA and remained in use into the recent past. A growing
body of evidence suggests that the transition from MSA to LSA was
not a linear process, but occurred at different times in different
places. Understanding this process is important to examine what drives
cultural innovation and creativity, and what explains this critical
behavioural change. Defining differences between the MSA and LSA is an important step towards this goal.


========================================================================== "Eastern Africa is a key region to examine this major cultural change,
not only because it hosts some of the youngest MSA sites and some of the
oldest LSA sites, but also because the large number of well excavated and
dated sites make it ideal for research using quantitative methods," says
Dr. Jimbob Blinkhorn, an archaeologist from the Pan African Evolution
Research Group, Max Planck Institute for the Science of Human History
and the Centre for Quaternary Research, Department of Geography, Royal Holloway. "This enabled us to pull together a substantial database of
changing patterns of stone tool production and use, spanning 130 to
12 thousand years ago, to examine the MSA-LSA transition." The study
examines the presence or absence of 16 alternate tool types across 92
stone tool assemblages, but rather than focusing on them individually,
emphasis is placed on the constellations of tool forms that frequently
occur together.

"We've employed an Artificial Neural Network (ANN) approach to train and
test models that differentiate LSA assemblages from MSA assemblages,
as well as examining chronological differences between older (130-71
thousand years ago) and younger (71-28 thousand years ago) MSA assemblages
with a 94% success rate," says Dr. Matt Grove, an archaeologist at the University of Liverpool.

Artificial Neural Networks (ANNs) are computer models intended to mimic
the salient features of information processing in the brain. Like the
brain, their considerable processing power arises not from the complexity
of any single unit but from the action of many simple units acting in
parallel. Despite the widespread use of ANNs today, applications in archaeological research remain limited.

"ANNs have sometimes been described as a 'black box' approach, as even
when they are highly successful, it may not always be clear exactly
why," says Grove. "We employed a simulation approach that breaks open
this black box to understand which inputs have a significant impact
on the results. This enabled us to identify how patterns of stone tool assemblage composition vary between the MSA and LSA, and we hope this demonstrates how such methods can be used more widely in archaeological research in the future." "The results of our study show that MSA and LSA assemblages can be differentiated based on the constellation of artefact
types found within an assemblage alone," Blinkhorn adds. "The combined occurrence of backed pieces, blade and bipolar technologies together
with the combined absence of core tools, Levallois flake technology,
point technology and scrapers robustly identifies LSA assemblages,
with the opposite pattern identifying MSA assemblages. Significantly,
this provides quantified support to qualitative differences noted by
earlier researchers that key typological changes do occur with this
cultural transition." The team plans to expand the use of these methods
to dig deeper into different regional trajectories of cultural change
in the African Stone Age. "The approach we've employed offers a powerful toolkit to examine the categories we use to describe the archaeological
record and to help us examine and explain cultural change amongst our ancestors," says Blinkhorn.


========================================================================== Story Source: Materials provided by Max_Planck_Institute_for_the_Science_of_Human_History.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Matt Grove, James Blinkhorn. Neural networks differentiate between
Middle
and Later Stone Age lithic assemblages in eastern Africa. PLOS ONE,
2020; 15 (8): e0237528 DOI: 10.1371/journal.pone.0237528 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200826140909.htm

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