## 4 Discussion  
In the current study, we investigated the important question of whether cognitive (work)load or mental demands can be measured by pupil size, regardless of the emotional content of the stimuli used in a task. To this end, we selected a controlled but nonetheless more realistic scenario in which the measurement of cognitive load is of interest: clinical interviews. Under an applied perspective, the measurement of cognitive load in this situation is important for a variety of reasons, not least because the correct usage of data obtained in clinical interviews often also depends on a correct comprehension of the content of the messages conveyed in such an interview, be this through verbal or non-verbal communication [7]. It should be noted that the currently available self-report measures are not necessarily consistent [8], so an unobtrusive and objective measure would be desirable. With respect to our hypotheses, we found the following results. Regarding Hypothesis 1, we found evidence that the algorithm was partly able to differentiate between low- and high-cognitive workload manipulations. With respect to Hypothesis 2, however, numerically, although not statistically significant, we found that the effect was larger in neutral- rather than in negative-content conditions. In the negative-content conditions, pupil-size differences between workload conditions were absent. Concerning Hypothesis 3, nevertheless, we did find evidence that cognitive load can be successfully derived from measured pupillary responses. This was reflected in the main effect of cognitive load on an increased pupillary response. This pattern of results was  
Figure 13: Medians (horizontal lines), upper and lower quartiles (colored areas above and below the horizontal lines),maxima and minima (upper and lower whiskers), and outliers (gray diamonds) of self-assessed experiences (of happiness, arousal, and control) on the \(y\) axis as a function of condition/group (NEUL: neutral content/low load; NEUH: neutral content/high load; NEGL: negative content/low load; NEGH: negative content/high load) on the \(x\) axis. found using a conventional pupil-size measure of cognitive load--the difference in pupil size between the manipulation (here, the clinical interview proper) and a baseline phase during which the same light-elicited pupillary responses were observed as during the manipulation (but without the critical manipulation). In the current study, the baseline measure of pupillary response was recorded early during the interview, when questions concerned basic information about the interviewee. Regarding Hypothesis 4, importantly, the observed numerical pattern of an interaction between cognitive workload and emotional content of the interview became significant in an analysis based on arithmetically calculated cognitive-load measures. This was found when we applied an algorithm that uses the currently measured luminance to calculate a pupillary light response, which in turn is subtracted online from the measured pupil size to extract cognitive load. The current study, thus, shows that the algorithmic cognitive-load measure might be more sensitive to the true pattern within the numerical data than the conventional way of calculating a pupillary cognitive-load response (i.e., based on an offline subtraction of pupil sizes during baseline from pupil sizes during manipulation phases). Finally, with respect to Hypothesis 5, the algorithmic online measure was externally validated by reasonably high and significant correlations with the standard offline measure of cognitive workload in all of the groups and for each of our participants. In summary, pupil sizes are a promising tool for the relatively unobtrusive measurement of cognitive load in applied scenarios, such as clinical interviews, and it might even be possible to derive estimates of the loads online, during data recording.