## 1 Introduction
For instance, the electrodermal activity or visual behaviour can reveal different internal states, such as variations in the level of stress [9; 10], affective state (i.e., arousal and valence) [11], or mental workload [12; 13]. A reductionist approach could lead to the misunderstanding of data, causing a failure to support users when needed, or implementing adaptive features when they are not necessary. To overcome this problem, the combination of various sources of information could be the most suitable solution. It is possible to pair users' implicit and explicit (I/E) data to achieve the richest level of comprehension [5]. Regarding the second limit, namely the system transparency, it is relevant to clarify to the users the reasons and the modalities of the machine's actions that modify the \"state\" of the world. This characteristic allows the users to carefully decide which data to provide to the devices and how to use the information coming from the devices [14]. Whenever this transparency is lacking, the system becomes a black box, and the user is not capable of understanding the causes of modifications in the system's functioning. To test this construct, we could compare if the users' perceptions (i.e., self-reported data acquired offline) match the detected changes in the I/E data. The exploitation of these user's personal and sensitive data raises a series of issues related to ethics, privacy violations, and misuse of the processed data, which are intrinsically connected to the symbiotic system's main advantage, namely, its capability to act bypassing the user [14; 15]. To consider the critical aspects of the symbiotic system mentioned above, we needed to identify a reliable construct that could be assessed based on implicit, explicit, and subjective measures [16]. Within the work context [8], the impact of mental workload (MWL) on performance has been analyzed [17; 18; 19], especially regarding its optimization for users operating in the environment of so-called Industry 4.0 [20]. In the ergonomics domain, MWL is defined as the mental demands imposed on workers by accomplishing tasks, and refers to the operators' cognitive resources needed to meet the working requests [21]. One of the most interesting features of the MWL is that it can be exploited to evaluate how users' responses are affected by the difficulty of a task [17]. Recent literature reviews have reported a series of implicit measures of MWL that are affected by variations in the imposed level of mental workload [12; 22], such as the heart rate (HR), which increases with higher task demands [23] and multitasking conditions [24]. Another example is the skin conductance (SC, or electrodermal activity; EDA) which has been shown to increase in different situations such as complex driving environments [21] and the presence of secondary tasks [18; 25]. Considering eye-related metrics, modulations due to a high level of mental workload are shown in pupil diameter (i.e., increment [20]), fixation duration and frequency (i.e., decrement [25]), saccadic frequency (decrement [26]), blink duration (decrement [22]) and frequency (increment [27; 28]), and the nearest neighbor index (NNI) [13; 29; 30]. MWL is generally assessed explicitly considering the user's performance, such as time on task and accuracy [31]. Concerning the self-reported measures of MWL, one of the commonly employed tools is the NASA task load index (NASA-TLX) questionnaire [32]. This comprises six sub-scales, eventually providing a single score. Some studies utilize the overall scoring (i.e., computed considering all the sub-scales), the so-called task load index. Recently, [PERSON] and colleagues [33] have pointed out the importance of evaluating each dimension of the NASA-TLX questionnaire independently (e.g., mental demand, physical demand, effort, etc.). By doing so, it is possible to assess how the single sub-scale scores may be differently affected by the mental workload related to a specific task.