Right or left? Determining the hand holding the tool from use traces

Alice Rodriguez, Emmanuelle Pouydebat, M. Gema Chacón, Marie Hélène Moncel, Raphaël Cornette, Ameline Bardo, Laurence Chèze, Radu Iovita, Antony Borel

    Research output: Contribution to journalArticlepeer-review


    Currently, approximately 90% of the human population is right-handed. This handedness is due to the lateralization of the cerebral hemispheres and is controlled by brain areas involved in complex motor tasks such as making stone tools or in language. In addition to describing the evolution of laterality in humans, identifying hand preference in fossil hominids can improve our understanding of the emergence and development of complex cognitive faculties during evolution. Several fields of prehistory like palaeoanthropology or lithic analysis have already investigated handedness in fossils hominins but they face limitations due to either the incomplete or the composite state of the skeleton remains or to results replication or method application failure. Wear analysis could provide new complementary data about hand preference evolution and the development of certain complex cognitive functions using indirect evidence (use traces, micro-scars in particular) of the hand holding the stone tool during use. Controlled experiment has been carried out in order to establish a reference collection of tools used with the left and tools used with the right hand. Wear analysis was performed on this corpus using “classical” microscopic approach and geometric morphometric analysis. A machine learning algorithm, the k-NN method, was applied to verify if use traces (micro-scars) could help determine the hand holding the tool during use. The best model, based on parameters referring to invasiveness of micro-scars, was able to correctly determine the hand holding the tool with 75% accuracy.

    Original languageEnglish (US)
    Article number102316
    JournalJournal of Archaeological Science: Reports
    StatePublished - Jun 2020


    • Experimentation
    • Geometric morphometric
    • Hand preference
    • Machine learning
    • Tool use
    • Use-wear

    ASJC Scopus subject areas

    • Archaeology
    • Archaeology


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