Participants showed significantly greater attention to their self-option than to the other options according to the analysis (t(40)= 6.49, p <0.0001, SD= 78.62), as illustrated in Fig. 2a. Furthermore, as shown in Fig. 2b, we observed a noticable increase in attentiveness towards the self-option in both chosen (t (40) = 7.27, p <0.0001, SD= 0.09) and unchosen situations (t (40) = 7.42, p <0.0001, SD= 0.05) compared to the other option. Moreover, our results indicated that the chosen option was viewed more frequently than the unchosen option (t (40)= 14.82, p <0.0001,SD= 0.06).
Generalized Linear Mixed-effects Model (GLMM)
To rigorously investigate these observations, we conducted a mixed-effects regression analysis, which is summarized in Table 2. In specification (1), the dependent variable is the RSV , and we included beta, alpha, advantages and disadvantages, as well as absolute utility, as covariates (see Equation 2).
Our findings reveal that both the alpha and beta parameters exert a negative influence on the RSV . In simpler terms, as these parameters increase, the amount of relative attention devoted to the self-option decreases.Furthermore, we observed relationship between the difficulty of the decision does not reach to the significance level, as measured by the absolute utility variable, and the RSV. In advantageous situations, individuals tend to exhibit a higher RSV, whereas in disadvantageous situations, this relative amount decreases.
In specification (2) and (3), We conducted the same analysis for RSV whithin chosen option and unchosen option separately (see Euation 2).
The findings indicated that the absolute value of utility only has an impact on the realtive self-view on unchosen options. In other words, the more difficult the options (i.e., the lower the absolute utility value), the greater the effect on the unchosen options.
Furthermore, we conducted a comparison to determine if there is a discrepancy in attention towards the self-option based on whether participants were in an advantageous or disadvantageous condition. Our findings demonstrate that participants exhibited a higher level of attentiveness towards the self-option in advantageous situations compared to disadvantageous situations, as depicted in Fig.3
In other words, the pay-off associated with each option can be a decisive factor in determining the level of focus on one's self-option versus the other option. It appears that when the subject's payoff exceeds that of the other person, they tend to pay more attention to their self option. Conversely, when the other person's payoff is higher, individuals tend to direct their attention towards the other option. Basically, attention seems to be influenced by the relative payoff, with a greater focus on options associated with higher payoffs.
With respect to reaction time (specification (4)) ,the positive effect of "Alpha" suggests that higher values of this variable lead to longer response times. On the other hand, "Beta" does not appear to have a significant impact on the response time. The coefficient for "Advantage" is estimated at -0.443 (p < 0.01), indicating a significant negative relationship with the response time. This suggests that when the dictator's payoff is greater than the receiver's, the response time tends to decrease. On the other hand, the coefficient for "disadvantage" is estimated at -0.223 (p > 0.05), indicating a non-significant relationship with the response time. This suggests that when the receiver's payoff is greater than the dictator's, it does not significantly impact the response time.
Additionally , our findings suggest that in the mixed situation, the reaction time is longer compared to both the advantageous and disadvantageous situations as illustrated in Fig.4.
This indicates that advantagous and disadvantageous conditions are relatively easier than mixed one and higher reaction time depends on the task difficuly.
Roles of BIS/ BAS as a predictor of Reaction time and Pupil dilation
The BIS is conceptualized as an attentional system which is sensitive to the fear of punishment. The BAS is understood as a motivational system which explores whether people behavior are motivated by the desire for signals of rewards (Amodio et al., 2007).There is evidence that individual differences in BIS/BAS can affect the way options are evaluated in decision-making (D. Y. Kim & Lee, 2011; Li et al., 2021). It is also possible to link the BIS / BAS system to the eye movements (Rauthmann et al., 2012).
To scrutinize the relationship between RT, Pupil size and BIS / BAS scales in greater detail,We employed a comprehensive mixed-effects regression analysis. The results of this analysis are presented in Table 3, which offers a concise summary of our findings. In specification (5), the dependent variable is the RT, and we included BIS/ BAS items , advantages and disadvantages, as well as absolute utility, as covariates (see Equation 3).
Table 3 Results of Mixed-Effects Regression Analysis: RT and pupil size as Dependent Variables with Covariates
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)
The BAS- Drive variable showed a positive association with both RT and pupil size. The BAS- Reward and BAS Fun Seeking variables did not have a significant impact on RT or pupil size. Additionally, the absolute utility variable demonstrated a negative relationship with both RT and pupil size. The advantage variable was negatively associated with pupil size, while the disadvantage variable did not significantly affect either RT or pupil size. These findings suggest that individual differences in BIS/BAS systems, as well as the consideration of absolute utility and advantage, may influence both cognitive and physiological responses.