Over 40,000 years ago, our early ancestors were already carving signs into tools and sculptures. According to a new analysis by linguist Christian Bentz … and archaeologist Ewa Dutkiewicz …, these sign sequences have the same level of complexity and information density as the earliest proto-cuneiform script that emerged tens of thousands of years later, around 3,000 B.C.E….
New findings show that these marks are there for a reason – Stone Age humans used them to convey information and to record their thoughts. ‘Our research is helping us uncover the unique statistical properties – or statistical fingerprint – of these sign systems, which are an early predecessor to writing,’ explains [Benz]….
The researchers analysed more than 3,000 geometric signs found on around 260 objects using computational approaches. Their aim was not to uncover the concrete meaning of the signs, which have not been deciphered….
‘Our analyses demonstrate that these sign sequences have nothing to do with the writing systems of today, which represent spoken languages and are characterized by high information density. In contrast, the signs on the archaeological objects are frequently repeated – cross, cross, cross, line, line, line. This type of repetition is not a feature found in spoken language,’ explains Christian Bentz. ‘However, our findings also show that Palaeolithic hunter-gatherers developed a system of symbols that has an information density that is statistically comparable to the earliest proto-cuneiform tablets from ancient Mesopotamia, which came 40,000 years later. Sign sequences in proto-cuneiform script are also repetitive and the individual signs are repeated at a similar rate. In terms of complexity, the sign sequences are comparable,’ says Bentz….
That also means that little changed between the Old Stone Age and the emergence of the first proto-cuneiform scripts. ‘Then, about 5,000 years ago, a new system emerged relatively suddenly that represents spoken language. The new system therefore has completely different statistical characteristics,’ explains Bentz.
For their research, the team digitalizes the sign sequences on archaeological objects in a database, which they then use to assess statistical properties in the Stone Age sign inventories. Using computer-assisted methods, Bentz looked into the potential to express information using the signs and compared this to the potential allowed by early cuneiform sequences and by modern writing. In their analysis, the researchers applied approaches from quantitative linguistics such as statistical modelling and machine learning classification algorithms.
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