CUTTING or SCRAPPING? Using Neural Networks to distinguish kinematics in use-wear analysis.

In this paper, we apply supervised neural networks (Backprop. learning algorithm) to the classical problem of statistical hypothesis testing. Processing experimental use wear in lithics we have found some contraintuitive results using standard tests, which can be solved using the non-linear discriminant power of Neural Networks. Specifically when archaeological data do not fit parametric distributions, Supervised Learning algorithms appear as an alternative approach. Our particular case study is a set of digital images of experimental data showing use wear as a result of work actions. We have used replicated lithic tools in order to find similarities between use wear identified in experimental data. Previous studies shown that there is not an single discrimination rule to associate cause (kinematics) and effect (wear).