Wolfgang Heidl, Stefan Thumfart, Christian Eitzinger, Edwin Lughofer, and Erich P. Klement
Machine Learning, Intelligent Data Analysis, Gender Differences, Visual Inspection
Among manufacturing companies there is a widespread consensus that women are better suited to perform visual quality inspection, having higher endurance and making decisions with better reproducibility. Up to now gender-differences in visual inspection decision making have not been thoroughly investigated. We propose a machine learning approach to model male and female decisions with classification trees and base the analysis of gender-differences on the identified model structure. A study with 50 male and 50 female subjects on a visual inspection task of stylized die-cast parts revealed highly significant structural gender-differences (p=0.00005), in spite of non-significant differences in overall accuracy (p=0.34). Going beyond asking which sex is better at a given ability, our results suggest that classifier-based modeling is a promising approach to analyze differences in the structure of cognitive abilities.
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