## 1 Introduction
Yet, it stands as external validation by comparison with a more conventional pupil-size measure. This was what we did in the current study: We compared how the algorithm faired in comparison with a standard measure of pupil dilation under the relatively uncontrolled conditions of an interview. In addition, we took various measures of objective task performance, such as the interviewer's correct classifications of both the verbal (in L and H groups) as well as nonverbal responses (H groups only) during the interview, and of the interviewer's performance in a subsequent memory test about the content of the interview. We also collected several measures of the interviewers' self-assessment regarding both the emotions experienced during the interview as well as the felt subjective demands imposed by the interview and tasks. The following were our hypotheses:  
**Hypothesis 1**. : _If the algorithm for automatic extraction of the cognitive load through pupillary responses serves its purpose, we expected to see the above-chance classification of our participants into those that experienced higher processing demand versus those that experienced lower processing demand. If external validity is granted, we also expected a good correspondence between the algorithmic measure and the more conventional measurements of cognitive load based on subtracting pupillary light responses as a baseline from conditions in which the same pupillary light response is likely elicited but on top of this cognitive load triggers a pupil dilation._  
**Hypothesis 2**. : _However, because emotions can elicit pupil dilation, just as the task demands, we also expected this classification to work better under conditions with neutral rather than emotional (here, negative) response content. As a ground truth, we also used raw pupil size measured by the eye-tracker itself, without the preprocessing steps taken by the algorithm such as its automatic modeling of an impulse corresponding to the pupillary light response. To this end, raw pupil sizes during a baseline phase of the interview were subtracted from raw pupil sizes measured during the manipulated parts of the interviews (\"during manipulation\" in short) that varied according to response content (neutral vs. negative) and task or processing demands (low vs. high). The resulting differences were analyzed with a 2 \(\times\) 2 analysis of variance (ANOVA), with between-participant variables processing demand (low vs. high) and verbal content (neutral vs. negative)._  
**Hypothesis 3**. : _Here, we expected a main effect of task load/processing demand, with larger pupil sizes under high- than low-load conditions (cf. [34]) and possibly a main effect of emotion, with larger pupil sizes under negative than under neutral-content conditions (e.g., [25]; but see [35] for an example of failure to observe emotion-elicited pupil dilations with verbal material of varying task difficulty)._  
**Hypothesis 4**. : _In addition, we expected to see an interaction, for example, a compromised cognitive-load effect under emotional conditions compared to neutral conditions if the emotion-elicited pupil dilation can mask or compromise the processing demands._  
**Hypothesis 5**. : _Finally, we looked into the correlation between the algorithmic cognitive-load measure and the ground truth and expected a significant and substantial correlation between the measures, corresponding to external validation of the algorithmic cognitive-load measure._  
Besides these most important analyses, we also analyzed objective task performance and subjective assessments, where we would have expected better objective performance under low than high task-load conditions, lower interviewers' self-assessed effort and demands under low- than under high-load conditions, or more positive ratings under neutral-than under negative-content conditions.