The use of simulator technology has become popular in providing training, investigating driving activity and performing research as it is a suitable alternative to actual field study. The transferability of the achieved result from driving simulators to the real world is a critical issue considering later real-world risks, and important to the ethics of experiments. Moreover, researchers have to trade-off between simulator sophistication and the cost it incurs to achieve a given level of realism. This study will be the first step towards investigating the plausibility of different driving simulator configurations of varying verisimilitude, from drivers' galvanic skin response (GSR) signals. GSR is the widely used indicator of behavioural response. By analyzing GSR signals in a simulation environment, our results are aimed to support or contradict the use of simple low-level driving simulators. We investigate GSR signals of 23 participants doing virtual driving tasks in 5 different configurations of simulation environments. A number of features are extracted from the GSR signals after data preprocessing. With a simple neural network classifier, the prediction accuracy of different simulator configurations reaches up to 90% during driving. Our results suggest that participants are more engaged when realistic controls are used in normal driving, and are less affected by visible context during driving in emergency situations. The implications for future research are that for emergency situations realistic controls are important and research can be conducted with simple simulators in lab settings, whereas for normal driving the research should be conducted with full context in a real driving setting.