ISSN : 1226-9654
오류의 원인을 이해하고 예측하는 것은 일상생활이나 산업현장에서 일어날 수 있는 사고를 방지하는 데 중요하다. 최근 오류 예측 연구에서는 고정된 시행 간 간격(inter-trial interval, ITI)의 인지 과제를 사용하여 성공적으로 오류를 예측하였지만, ITI을 고려하지 않아 오류 예측 모형을 일반적인 상황까지 적용하기가 어려움이 있다. 본 연구에서는 오류 이전 여섯 시행들로부터 얻어진 반응시간과 ITI 추세, 그리고 오류 시행에서의 과제조건을 이용하여 기계학습을 통해 오류를 예측하고자 하였다. 그 결과, 다양한 오류의 유형이 분류 및 예측되었으며, 특히 ITI 추세에서 반복적인 ITI 패턴이나 시간 압력의 변화와 같은 요인들이 오류와 관련되는 것으로 나타났다. 본 연구는 변화하는 ITI 변인이 오류예측에 중요한 역할을 할 뿐만 아니라 오류 발생과 관련된 피험자의 정신적 상태와 관련이 있다는 것을 확인하였다. 따라서 본 연구는 다양한 환경에서 다양한 유형의 오류를 예측할 수 있으며, 이러한 오류들이 다양한 원인으로 발생할 수 있음을 확인하였다는데 의의가 있다.
It is important to understand causes of human errors and to predict errors in order to prevent various accidents in our daily lives and industrial fields. Although a previous study employing a fixed inter-trial interval (ITI) in a cognitive task successfully predicted errors, it is unlikely to generalize from the previous results to other situations. The current study sought to predict errors by reaction times, task conditions, and ITIs extracted from six consecutive trials preceding error trials, in the context of machine learning. The results showed that various types of errors could be observed and predicted. Especially, presence of repeated patterns or time-pressure chances in the ITI trends might be related to errors. This is interpreted that ITI variation is important to predict errors as well as related to participants’ mental states affecting errors. Therefore, this study suggests that various types of errors in a variety of situations can be predicted, in which those errors would be caused by various factors.
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