## 1 Introduction  
In research on ergonomics in general and human-machine interactions in particular, but also in many clinical or educational settings, technical applications aim to track humans' current processing demands under varying, real-world conditions. Examples are the presentation of information \"just in time\" (e.g., when users have a higher likelihood of processing the information), warning the user via visual signals in cases where cognitive load reaches levels that significantly increase the chances of missing crucial information, or monitoring an audience in an educational context to gauge the speed or rate of information presented and processed (cf. [1; 2; 3; 4; 5]). Cognitive demands or load and its tracking are therefore a major topic in medical education and applied clinical settings [6]. Clinical interviews, for example, are often conducted under highly distracting conditions (e.g., in an emergency room) and, yet, the correct processing of the information presented is of the highest relevance in this situation (e.g., to decide if a patient needs to be admitted to the hospital, yes or no) [7]. The problem is aggravated by the fact that self-report measures of cognitive load or demands do not necessarily converge. For instance, [PERSON] et al. [8] used different self-report measures of cognitive load in simulation-based medical training and found little convergence between these measures. For these reasons, researchers seek to identify unobtrusive, pervasive, and affordable technology to track processing demands or cognitive load in areas such as the workspace or educational institutions [9]. One such technology is eye-tracking (e.g., [10; 11; 12]). Here, one dependent variable suited to track processing demands or cognitive load is pupil diameter (cf. [13; 14; 15]). Higher cognitive load elicits a pupil dilation and, accordingly, research has indicated that measured pupil diameter can be used to successfully discriminate between more or less demanding conditions or conditions requiring more or less mental effort (e.g., [16; 17; 18; 19]). However, a general problem that complicates the read-out of processing demands or cognitive load from measured pupil sizes is that pupillary dilation is a relatively unspecific response elicited by a variety of different conditions, not all of which are diagnostic of cognitive load (e.g., [20]). Among the critical triggers of pupillary responses are changes in the ensuing luminance level (e.g., [21; 22]), saccades ([23; 24]), felt emotions (e.g., [25; 26; 27]), value (e.g., [28; 29]), arousal (e.g., [30; 31]), psychoactive substances (e.g., [32]), and pathological conditions on the side of the observer (e.g., [33]). Thus, many factors that independently modulate the pupillary response pose a threat to the successful measurement of cognitive load from pupil dilations. For example, if an emotion such as joy or anger, elicits a pupil dilation, there is potentially less room for a load-specific response on top of the one triggered by the emotion [34; 35]. This can be a problem, as the valence of the processed content can vary independently of cognitive load. To understand this, think of a clinical interview (e.g., [36; 37]). In such a situation, the demands imposed on the interviewer can vary depending on whether the interviewer has to encode only verbal responses of the patient or whether she also pursues additional aims, such as evaluating emotional content by classifying nonverbal expressions, such as mimic and gestures (cf. [38; 39; 40]). At the same time, in this situation, the emotional content of the answers may vary (cf. [41]). Sometimes the interviewee might relate emotionally neutral content such as her age or occupation, but, at other times, the interviewee might also talk about personal traumatic or stressful past experiences. The emotional valence of the responses can be entirely independent of the cognitive demands imposed by the task--for example, whether the interviewer processes only verbal or verbal and nonverbal communication.