the pre-experiment Analysis
SPSS 25.0 (IBM Corp, Armonk, NY, USA) and Microsoft Excel 19.0 were used for the analysis. We followed Kalimo et al.33 , who applied the weighted scoring method, that is, the score of mental fatigue = 0.40 × emotional exhaustion + 0.30 × dehumanization + 0.30 × low sense of achievement. Through this analysis, we were able to select 22 participants with mild mental fatigue (mental fatigue scores ∈ [1, 1.49]), 22 with moderate mental fatigue (mental fatigue scoring ∈ [2.53, 2.63]), and 22 with severe mental fatigue (mental fatigue scores ∈ [3.41, 4.38]). Subsequently, we randomly selected 11 participants from these candidates according to their mental fatigue levels(Eleven participants were selected for each level of mental fatigue), enrolling 33 participants in the formal experiment. The remaining 33 participants took part in another experiment that ran simultaneously. The inclusion criteria were: regularly working underground in a coal mine, being right-handed, and having normal vision or corrected visual acuity. (Fig. 1).After excluding three participants who were not serious about the experiment, we obtained experimental data for 30 participants.
Descriptive statistical analysis of demographic variables
The 30 participants were male, right-handed, with normal or corrected visual acuity and no neurological disease. Participant ages were as follows: 10 participants were aged 24–30 years (33.33%), 16 were aged 31–40 (56.7%), and four participants were aged 41 or above (13.33%). Regarding education level, 14 people had technical secondary school education or below (46.7%), 12 had junior college education (40%), and four had undergraduate degrees (13.3%). Regarding job role, there were 24 normal workers (80%) and cadres six (20%). Nineteen participants (63.3%) had worked for 0-5 years; nine participants had worked 6–10 years (30%), and two had worked for more than 11 years (6.7%). Regarding years of working underground, 21 people had worked for 0–5 years (70%) and nine for 6–10 years (30%). Statistical analysis of participants’ demographic variables (Table 1) revealed no significant relationship between mental fatigue and age. However, the higher the education level and the greater the job responsibility, the higher the degree of mental fatigue. Further, increased mental fatigue was associated with more years worked and years of working underground.
The effect of mental fatigue did not significantly differ by age: F (3,30) = 1.325, P = 0.282; educational level: F (3,30) = 1.969, P = 0.159; job role: F (3,30) = 1.125, P = 0.339; years of working: F (3,30) = 1.385, P = 0.267; and rural and urban birth: F (3,30) = 0.22, P = 0.804. However, the effect of mental fatigue significantly differed by the number of years working underground: F = 3.745, P = 0.037 < 0.05. Therefore, the experience of the three groups (mild, moderate, and severe mental fatigue) was not significant in terms of demographic variables, such as age, educational level, job, working years, and birth in urban or rural areas, which undermines the influence of these factors on mental fatigue. However, the three groups of subjects differed significantly in terms of years working underground; thus, this variable was potentially related to mental fatigue. Further analysis was conducted of whether years working underground were correlated with the BART value and net score (Fig. 2).
Analysis of emotion scale
The data gathered on emotions was meant to eliminate the interference of emotion as a factor. A single factor ANOVA test for positive mood showed that the mild mental fatigue group (mean [M] = 29.5, standard deviation [SD] = 6.311), moderate mental fatigue group (M = 31.3, SD = 7.47), and severe mental fatigue group (M = 29.1, SD = 8.40) showed neither significant positive emotional experiences, F (3, 30) = 0.248, P = 0.782, nor negative emotional experiences (M = 18.8, SD = 5.12; M = 23.8, SD = 3.65; M = 21.6, SD = 6.58, for each group, respectively). Thus, the influence of emotions on mental fatigue was excluded: F (3,30) = 2.274, P = 0.122.
Analysis of BART values
Descriptive statistical analysis of BART values
Fig. 3 shows that with the increase of mental fatigue and the number of inflations and exploded balloons, the BART value gradually increases; the number of missed shots tends to decrease gradually. This suggests that with increased mental fatigue, the participants’ tendency to take risks is more obvious.
Descriptive statistical analysis of BART values for participants with mild, moderate, and severe mental fatigue including total number of inflations, total number of exploded balloons, total number of unexploded balloons, and BART values is exhibited in Table 2. The BART value is Mmild < Mmoderate < Msevere, that is, with the increase of mental fatigue, participants are more inclined to take risks
Correlation analysis between additional variables and BART values
We conducted correlation analysis between the four factors and BART values to exclude the interference and influence of non-research factors such as emotion, risk preference, trait self-control, and underground working years. The results are shown in Table 3. Trait self-control (R = 0.390, P < 0.05), risk preference (R = 0.334, P < 0.05), and positive emotion scores (R = 0.353, P < 0.05) were significantly correlated with BART values (R = 0.390, P < 0.05). Therefore, for the BART, risk preference, trait self-control, and positive emotion score were incorporated into the equation as covariables for analysis (Table 3).
Influence of mental fatigue on risk decision-making tendency
We found that BART values in the group with severe mental fatigue (M = 10.3, SD = 2.34) were greater than those in the moderate mental fatigue group (M = 8.65, SD = 2.81), which, in turn, were greater than those in the mild mental fatigue group (M = 6.19, SD = 2.12) (Fig. 4).
To further study the effect of mental fatigue on risk propensity in decision- making, we performed an ANOVA with the mental fatigue groups as the independent variables; BART values as the dependent variable; and trait self-control, willingness to take risks, and working for a fixed number of years as covariates. The results showed that the difference in BART values between groups was significant (F (3, 30) = 4.142, P < 0.05). The bar chart (95% confidence interval) of BART values of different groups is shown in Fig. 4.
Overall, the results suggest that mental fatigue has a significant effect on risk propensity in risky decision-making. The higher the level of mental fatigue, the more risk-taking behaviors (the higher the reward) and the higher the risk-seeking tendency of the participants in the BART.
Number of card choices
In the IGT, high- and low-frequency reward decks contained low- and high-frequency penalty cards, respectively. Therefore, it was necessary to analyze the selection times of different types of cards by the three groups of participants to investigate their card selection characteristics. Fig. 5 shows the selection times of different types of cards by participants with different degrees of mental fatigue. As shown, with a gradual increase in mental fatigue, the tendency to choose A/B cards decreased gradually, while the tendency to choose C/D cards increased gradually.
Taking the type and class of cards as independent variables and the number of cards selected for each type as dependent variables, 2 (card type) × 3 (group) mixed-design ANOVA and ANOVA were carried out. The results showed that the main effect between favorable and unfavorable cards of card type was significant: F (2, 30) = 83.235, P = 0.000. The group main effect was also significant: F (3,30) = 16.789, P = 0.000. As shown in Fig. 6, with the aggravation of mental fatigue, the number of unfavorable cards gradually increased, while the number of favorable cards gradually decreased. Thus, with increasing mental fatigue, participants’ risk decision-making tendency became stronger. Further, the more serious the mental fatigue, the more likely they were to take risks (Fig. 6).
Statistical analysis of the net score of cards
A repeated measures ANOVA was performed with groups as the independent variables and the net score of each decision module as the dependent variable. The results are shown in Fig. 7. The main effect between modules was significant, F = 5.944, P < 0.05, but the main effect between groups was not significant (F = 2.43, P = 0.107). There was no significant interaction between the groups and modules, F = 0.177, P = 0.839. The net scores of the participants in the mild, moderate, and severe mental fatigue groups were significantly different, as shown in Fig. 7. The overall net score of the bar graph increases with the increase in decision times. Participants’ net score decreased with higher mental fatigue, reducing the overall height of the histogram. This indicates that as they experienced higher mental fatigue, participants became more inclined to take risks.
Correlation analysis between additional variables and net score
A correlation regression analysis was conducted between the four variables and the net score to exclude the influence of non-research variables such as emotions, trait self-control, risk preference, and years of underground work on risk decision-making. The results are shown in Table 4. Positive emotion score and net score (R = 0.296, P = 0.056) were significantly marginal. Net score had no significant correlations with negative emotion score, risk preference, trait self-control, or years working underground.
Influence of mental fatigue on risk decision-making tendency
the net score values of different groups; specifically, the net score value of the mild mental fatigue group (M = 44.5, SD = 40.43) was greater than those of the moderate mental fatigue group (M = 23.5, SD = 33.08) and the severe mental fatigue group (M = 7.8, SD = 38.2). The lower the net score, the more frequently the participants chose unfavorable cards and the greater their risk tendency, indicating that with higher mental fatigue, risk tendency increased .
To further study the effect of mental fatigue on risk propensity in decision-making, the mental fatigue groups were taken as the independent variables, with net scores as the dependent variables. The results of the regression analysis showed that the net score value differences between groups were significant (F (3, 30) = 4.992, P < 0.05). This indicates that mental fatigue had a significant impact on risk-taking tendency in risk decision-making; the higher the participants’ mental fatigue, the riskier their behaviors in the IGT ( more frequently choosing unfavorable cards).
Comprehensive risk score
The BART value and the IGT net score were tested by a paired samples t-test. The test results showed that T (30) = -2.32, P < 0.05, F = -0.283, P = 0.130. There was a very significant correlation between BART values and IGT net scores. These results indicate that the BART and IGT can be used to effectively measure risk-taking tendency and provide new ideas for workers’ job arrangements.
Taking mental fatigue as the control variable and BART values and IGT net scores as dependent variables, the correlation analysis showed that mental fatigue was negatively correlated with IGT net scores (F = −0.387, P < 0.05). In other words, the more serious the mental fatigue, the lower the IGT net score, the more frequently the participants chose unfavorable cards, and the greater the risk decision-making. There was also a significant positive correlation between mental fatigue and BART values (F = 0.543, P < 0.05). In other words, the more severe the mental fatigue, the higher the BART value, and the greater the risk decision-making tendency.