Abstract

It is well known that women are physiologically and psychologically influenced by the menstrual cycle. In addition, the presence of background noise may affect task performance. So far, it has proven difficult to describe how the menstrual cycle and background noise affect task performance; some researchers have found an increment of performance during menstruation or during the presence of noise, others found performance deterioration, while other still have reported no dominant effect either of the menstrual cycle in performance or of the presence of noise. However, no study to date has investigated the combinational effect between the menstrual cycle and the presence of background noise in task performance. Therefore, the purpose of this study was to examine the combined factor effect of menstrual cycle and background noise on visual inspection task performance indices by Signal Detection Theory (SDT) metrics: sensitivity index (d') and response criteria index (beta). For this purpose, ten healthy female students (21.5+/-1.08 years) with a regular menstrual cycle participated in this study. A VDT-based visual inspection task was used for the experiment in 3x2 factorial designs. Two factors, menstrual phase, pre-menstruation (PMS), menstruation (M), and post-menstruation (PM) and background noise, with 80 dB(A) background noise and without noise, were analyzed as the main factors in this study. The results concluded that the sensitivity index (d') of SDT was affected in all the menstrual cycle conditions (p<0.01) and the presence of background noise (p<0.05) but no combinational effect of menstrual cycle and background noise was found in this study. On the other hand, no significant effect was observed in the subject's tendency in visual inspection, shown by beta along the menstrual cycle and the presence of background noise. According to the response criteria for each individual subject, the presence of noise affected the tendency of some subjects in detecting the object and making decision during the visual inspection task.

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