Abstract:

A new method is proposed to analyze the correlation between scenario and human error. The scenario is decomposed into six aspects, which are operator, machine, task, organization, environment and assistant devices. Based on the scenario decomposition, a taxonomy of performance shaping factor is constructed, which includes thirty-eight items and can provide a reference template for the investigation of human error causes. Based on the skill-based, rule-based and knowledge-based (SRK) model, the slip/lapse/mistake framework is introduced to classify human errors, which are categorized as skill-based slip and lapse, rule-based slip and mistake, and knowledge-based mistake. Grey relational analysis is introduced to analyze the correlation between performance shaping factors and human error types, in which the correlations of “consequent-antecedent” and “antecedent-consequent” are both analyzed. By this method, performance shaping factors related to some specified human error type and human error types caused by some specified performance shaping factor both can be sorted according to their correlation degrees. A case study is provided, which shows that the proposed method is applicable in analyzing the correlation between scenario and human error, and can provide some important implications for human error prediction and human error reduction.