One would expect that trained professionals would be able to follow best interviewing practice guidelines and conduct unbiased interviews. However, field research has repeatedly shown that across countries, practitioners ask large proportions of option-posing and suggestive questions during forensic interviews (e.g., Korkman et al., 2006; Baugerud et al., 2022, 2023). More generally, forensic experts can be biased in terms of their observations, decision-making and question formulation, even without realizing it, and as a result an entire investigation can be impacted (Huang, 2021). In fact, even trained interviewers sometimes fail to follow best practice guidelines without continuous feedback and involvement in multiple training modules (Powell et al., 2008; Lamb, 2016) and tend to ask close-ended, leading, and repeated questions, thus increasing the likelihood of gathering only partial or even misleading information (Santtila et al., 2004; Bruck et al., 2006; Thoresen et al., 2009; Leach et al., 2022). To address these issues, great effort has been invested to design more effective interview training programs (e.g., Powell et al., 2014; Pompedda et al., 2015; Haginoya et al., 2020; Hassan et al., 2022) where continuing practice and ongoing feedback have been used to sustain the training effects (for a recent review, see Powell et al., 2022). Beyond the established efficacy of these training programs, serious gaming (Wouters et al., 2013) involving simulated avatar interviews (Pompedda et al., 2015) can be used as a tool to study the fundamental psychological processes, such as emotional impact on decision making during interviews (Zhang et al., 2022; Segal et al., 2023). For example, in an analysis of 2,084 simulated CSA interviews Zhang et al. (2022) found that interviewers’ preliminary assumption of sexual abuse having taken place predicted more frequent use of not recommended questions, bias toward a conclusion of abuse after the interview, higher confidence in the conclusion as well as a decreased likelihood of reaching a correct conclusion given the same number of available relevant details.

As it may be expected from trained professionals to follow best practice guidelines in forensic interviewing, it may also be expected that expert decision-making should be immune to any subjectivity and would be based purely on scientific standards, latest research findings and generally on an unbiased approach (requirements, which are also documented in code of ethics and the laws regulating the work of specialists and experts). One might assume, that areas such as DNA testing or judicial decision-making are exactly those areas where no doubts should be raised regarding the decision and interpretation of the results. However, it was repeatedly demonstrated, that even such areas can be affected by biases that are inherent to humans (Ask and Granhag, 2005; Dror and Hampikian, 2011; Jeanguenat et al., 2017). Even judges who must be impartial and must make decisions purely based on the evidence that is presented to them are being affected by biases that are being influenced by their attitudes, ideology, backgrounds and previous experiences (Berryessa et al., 2023).

A common idea prevails that in order for the legal system to function and the law to perform an objective function in the society, so that the public would trust and comply with the law – emotions must be “put aside” (Blix and Wettergren, 2016). However, when decisions are being made by human beings, it is doubtful that it is possible to “put” emotions aside, and thus it may well be assumed, that emotions will be experienced by the one who is making the decision as well as by those who are dependent on the decision. Thus, the role of emotions in decision-making should not be underestimated. Moreover, findings from areas related to forensic decision-making contexts (Dror et al., 2005; Charlton et al., 2010; Dror, 2020) support the hypothesis that interpretation and selection of information can be affected by emotional states. For example, in a study by Semmler and Hurst (2016) angry mock-jurors were more prone to carefully processing of evidence, detected more inconsistencies and recalled significantly more trial details as well as endorsed more of guilty verdicts.

Cognitive contamination or bias is present in all human beings, including forensic scientists (Jeanguenat et al., 2017). In a recent paper about sources of biases and fallacies, Dror (2020) explains, that experts may become biased due to various reasons, for example, reference materials, contextual information, organizational factors and others. Some of those sources of bias are related to emotional reactions by the experts, such as the data that possibly contains emotionally charged information (e.g., audio recording, that can reveal a brutal assault; gang-rape mixture DNA evidence) that can evoke experts’ emotions and subsequently impact decision-making. Further, Birze et al. (2022) reported, that professionals who analyze video evidence of violent crimes experience enduring impact of those videos. Constant viewing narrows the emotional proximity to violence and pushes to develop embodied reexperiences of the originally viewed traumatic events, for days, weeks, months and even more, following exposure (Birze et al., 2022). Thus, it is clear, that forensic experts are not immune to emotional impact (Dror and Hampikian, 2011), and it is important to acknowledge the existence of biases while not having a bias blind spot (Dror, 2020).

Some research has demonstrated that emotions may influence the interviewer’s attitudes during the interview (Oxburgh et al., 2006). An analysis of thirty-four investigative interviews with alleged sex offenders revealed a significant effect of prior acquaintance with the victim in that a greater number of negative emotional utterances (e.g., contempt, anger, and disgust) were used by interviewers who had not previously interviewed the victim (Oxburgh et al., 2006). Other research on emotions in investigative interviewing context, indicated that interviewers’ emotional reactions affected how information was obtained from the interviewee as well as how the gathered information was assessed (Ask and Granhag, 2007; Magnusson et al., 2021). Lerner and Keltner (2000) described dimensions of anger that distinguish it from other negative emotions. Anger is characterized by appraisals of someone other than the self being responsible for a negative event; that the negative event was under some individual‘s control; and by certainty/or confidence about what happened (Lerner and Keltner, 2000). Opposite to anger, sadness is characterized by a greater sense of uncertainty and is more associated with situational causes (Ellsworth and Smith, 1988). Ask and Granhag (2007) also reasoned that people who feel angry tend to perceive another/specific person to be the cause of an unwanted event while feelings of sadness do not specifically lead to blaming someone else for what has happened. In their experiment, Ask and Granhag (2007) found that sad investigators - compared to angry investigators - analyzed case materials more thoroughly while the conclusions drawn by angry participants were not affected by the (in)consistency between suspects’ statements and the hypothesis. Magnusson et al. (2021) assessed the relationship between evoked emotions and interview tactics in suspect interviews in a sample of Swedish and Norwegian police and found that interviewers who experienced more negative emotions of anger, frustration and disgust employed more confrontational interviewing tactics such as emphasizing the seriousness of the crime, with the goal of obtaining a confession. This suggests that within an ongoing interview, we can expect the anger and disgust to be more likely to precede closed (suggestive) (vs. open) questions whereas sadness would be more likely to precede open (vs. closed) questions.

Forensic child interviewing might give rise to emotions experienced by the interviewer, for example if the nature of the alleged crime is perceived as immoral and self-relevant in any way (Hutcherson and Gross, 2011). CSA is an emotionally charged topic not only for the public (McCartan et al., 2015) but also for professionals who interview allegedly abused children (Korkman et al., 2008; Magnusson et al., 2021). In a recent study Segal et al. (2022) explored how participants emotionally respond to child avatars with sexual abuse (vs. no abuse) scenarios. The main goal of that study was to examine whether participants respond emotionally to virtual child avatars with alleged CSA scenarios, and if they do, to analyze the patterns and differences of subjectively perceived feelings and objectively observed facial emotional expressions in response to either abuse or no-abuse narratives. In that study participants were presented with a number of child avatars describing either abuse or no-abuse details and their emotional responses were measured using automated analysis of facial expressions as well as self-reports. Self-report data revealed that participants reacted with greater relief when CSA was disconfirmed by the details revealed by the avatar but with stronger negative emotions of anger, sadness and disgust when CSA was confirmed. Although, the results from objectively expressed emotions were less clear. Segal et al. (2022) also expected surprise to be more likely after details disconfirming CSA. However, the difference in surprise in the two conditions did not reach statistical difference, however it was in the expected direction. Also, it was found that higher general emotionality related to CSA and higher perceived realness of the avatars made the differences clearer. Also, Segal et al. (2022) concluded that child avatar interviews are emotionally engaging and open a path to further research on the effects of interviewer’s emotions on interviewing. A second study (Segal et al., 2023) found a similar pattern of emotions in response to participants watching an actual dynamic interview. Participants responded with more sadness and disgust to confirmed CSA and with more relief to disconfirmed CSA interview outcomes. As expected by the authors, CSA scenario interviews were perceived as less suggestive than no-CSA scenarios. However, surprisingly, objectively more suggestive interviews were perceived as less suggestive and more appropriate b. This suggests that confirmation bias in the context of CSA interviews can have a seriously detrimental impact on the assessment of such interviews.

Emotions in the studies described above were elicited as a reaction to hearing a child avatar describe abuse or non-abuse details. However, the results may be relevant for formulating hypotheses about how emotions preceding a question could impact whether the question is biased and aims at confirming a CSA hypothesis or whether the question is open. Within the context of CSA interviews, we expected having an assumption of abuse, that is, thinking about CSA, to give rise to the same emotional pattern as listening to CSA details. In a corresponding fashion, we expected that thinking about the possibility of CSA not having happened would give rise to the same emotional pattern as listening to non-CSA details. We therefore expected that more of the negative emotions of anger and disgust would precede closed, CSA confirming questions while more of the positive emotions of relief and surprise would precede more open questions. Also, during an interview, CSA related details might be revealed by the avatar which would give rise to this emotional pattern reinforcing confirmation bias further which would in its turn again increase the probability of a closed question being asked next. Though, the expected role of sadness in this context seems somewhat unclear. On one hand, there is evidence of sadness resulting in more diligence and a stronger belief that the accused is guilty when analyzing the available data of an alleged crime (Ask and Granhag, 2007). Also, sadness is often accompanied with increased empathy (Nen et al., 2011) which is related to increased desire to help and support a victim (Bottoms, 1993; Huey and Kalyal, 2017). It can be assumed that if sadness is more related to the victim, then the interviewer will be more motivated to find support for the abuse hypothesis.

According to Arnold (1960) to arouse an emotion, an object or event must be appraised as affecting oneself personally as an individual with particular experiences and aims. Lazarus (1982) added that minimal information is needed to activate emotion which can be automatic and fast, and therefore not necessarily exceed the threshold of consciousness. Further, Lazarus (1966) defined the concepts of primary appraisal, secondary appraisal and reappraisal. Primary appraisal refers to the evaluation of a stimulus as significant to oneself. Secondary appraisal is related to coping potential: evaluation of the resources an individual has at their disposal to encounter with the situation. However, reappraisal may lead to changes of the primary and/or secondary appraisals. Based on appraisal theories of emotion (Arnold, 1960; Lazarus, 1966, 1982), our previous research (Segal et al., 2022; Zhang et al., 2022; Segal et al., 2023) and the literature mentioned above, we have drawn a preliminary model of the role of emotions and psychophysiological parameters on confirmation bias in question formulation in investigative CSA interviews (see Figure 1). A preliminary assumption of abuse is likely to result in more negative emotions of anger, disgust and sadness. Emotions of anger and disgust are associated with interviewers’ tendency to employ confrontational interviewing tactics and seeking for a confession of a committed crime. It suggests that these emotions would most likely reinforce the tendency to ask closed questions that support the abuse hypothesis, especially if the task demand components consume too much cognitive resources. Sadness on the other hand might result either in reinforcing the abuse hypothesis using closed questions when the feeling is directed toward the victim, or open-ended questions when the feeling is related to more openness regarding the overall case. On the other hand, an assumption of no-abuse would be associated with the more positive emotions of surprise and relief which are associated with alleviation of negative emotional state. Therefore, these emotions would be associated with less biased hypothesis testing.

Physiological parameters of heart rate (HR) and galvanic skin response (GSR) resistance, depending on their intensity might also influence either CSA confirming or CSA disconfirming questions (see Figure 1). It seems that lower HR (−) and higher GSR resistance lead toward CSA confirming questions, while higher HR and lower GSR resistance (−) lead toward CSA disconfirming questions. Further, we suppose that CSA confirming questions are more likely to elicit a CSA confirming response. However, there is a smaller possibility that a CSA disconfirming response might follow, thus changing emotional and psychophysiological reactivity of the interviewer.

As mentioned above, there are studies analyzing subjective and facially expressed emotional responses to CSA details revealed during interviewing. However, to our knowledge no studies have been conducted on interviewers’ psychophysiological responses and whether these could have an impact on subsequent question formulation in an ongoing CSA interview or in investigative interviews in general. However, given that emotions have psychophysiological correlates (Myroniv et al., 2017) and given that psychophysiological parameters are also related to stress and can especially be indicators of the cognitive demands of the task exceeding the available resources of a person (Jo and Kim, 2020), it is interesting to investigate whether they are related to an interviewer’s performance during an interview. In sum, previous research has not studied how psychophysiological parameters may affect question formulation during the interview process and what is their connection with confirmation bias in CSA interviewing.

It is known that physiological parameters, such as GSR (Shi et al., 2007) and HR reflect changes in the autonomic nervous system (Lee et al., 2006) and are valid indicators of stress, arousal levels and experienced levels of cognitive load (Healey and Pickard, 2005; Shi et al., 2007; Michels et al., 2013). Research indicates that GSR conductance (opposite to GSR resistance) can be a reliable measure of physiological stress (Han et al., 2020) and indicator of emotions, such as anger (Jha et al., 2018), sadness and happiness (Das et al., 2016). Feldman et al. (1999) meta-analytic study showed that one pathway through which stressors are thought to influence physiology is through their effects on emotion. Increases in negative emotions are associated with increases in cardiovascular (CV) responses to stress (Feldman et al., 1999). Research suggests that heart rate (HR) and skin resistance can be used as measures of worrying, that is, the higher the HR and the lower the skin resistance is (higher tendency to sweat), the higher levels of worrying it indicates and vice versa (Dua and King, 1987). Shirke et al. (2020) demonstrated that a sudden decrease in GSR resistance is associated with negative emotions while for positive emotions levels of GSR resistance tends to stay the same or change little in either direction. In any case, the changes are not sudden. Also, it is known that a lower level of anxiety (Chalmers et al., 2014) is associated with greater capacity for regulating emotional responding (Appelhans and Luecken, 2006). Some research has also shown that mean GSR conductance across studies increases as cognitive load increases (Shi et al., 2007). This leads to an assumption that when one experiences lower levels of anxiety and nervousness (associated with higher GSR resistance and lower HR) in combination with lower levels of cognitive capacity to elaborate on what actually happened in an alleged CSA situation, the interviewer might be more influenced by confirmation bias (higher levels of cognitive load), thus tempted to ask more closed questions confirming abuse hypothesis.

Simulated avatar interviews were created as an alternative to theoretical training and practical training using adult actors to improve the quality of investigative interviews in alleged CSA cases (Santtila et al., 2012). This form of training, also known as serious gaming (Wouters et al., 2013) is a simulation training when training with real objects or human beings is expensive, risky or unethical (e.g., flying an aircraft, performing complex operations or interviewing allegedly abused children) and allows practitioners to acquire and improve complex practical interviewing skills in a cost-effective way and is potentially more scalable. When the training starts, the participants first read a background scenario concerning CSA allegations and afterward answer several questions regarding their preliminary assumption and level of certainty. Afterward, participants start to interview the avatar using natural spoken language. Each avatar has predefined either abuse or no-abuse scenario related memory contents that are (or are not) revealed according to the interviewers’ questions. Each question asked by the interviewer is categorized and coded by a trained human operator (Table 1) and afterward a probabilistic response algorithm associated with the question type determines the avatars’ response. If the interviewer asks a recommended (open) question the avatar is likely to provide a response containing a relevant or neutral detail. However, if the interviewer asks a non-recommended (usually closed) question, the avatar provides an affirmative or non-affirmative (yes or no) response with some of these responses being incorrect when compared to the memory contents of the avatar. Therefore, the interviewer can sometimes elicit a wrong detail in this situation. An operator also has the ability to manually provide additional narrative details unrelated to the suspected abuse labeled side details. After each interview participants provide a final decision about the presence or absence of abuse, their certainty level about the decision and a brief report of what they think has happened to the avatar (Pompedda et al., 2015; Pompedda, 2018; Zhang et al., 2022; Segal et al., 2023).

Based on previously discussed studies about interviewers’ emotional reactions, general emotionality, perceived realness, assessment of simulated CSA interviews with child avatars (e.g., Oxburgh et al., 2006; Segal et al., 2022), as well as interviewing tactics (e.g., Ask and Granhag, 2007; Magnusson et al., 2021) and psychophysiological responses to emotional stimuli (e.g., Shi et al., 2007) we formulated the following expectations:

Besides the a priori hypotheses, we explored gender differences and changes from first to the second interview. Here, our general expectation was that women would express emotions more strongly (Parkins, 2012) and that the strength of the expressed emotions would reduce from the first to the second interview because of habituation.

The sample consisted of psychology students (N = 60 (47 women), age range 19–44 years, M _age = 22.75, SD _age = 6.74) recruited from Mykolas Romeris university. The invitation to participate in the study was sent out directly to all psychology bachelor students via e-mail. The data collection stopped when 60 participants took part in the study. None of the participants had any experience or training in child interviewing. Participants were granted a 10-euro shopping center gift voucher for their participation in the study.

The data of the present study were collected at the Applied Psychology Research Laboratory of Mykolas Romeris University in Lithuania by the first three authors. The study received ethical approval from the Ethics Committee for Psychological Research at Mykolas Romeris University, and documented in Decision Nr. 7/-2021.

Participants completed the Cognitions and Emotions about Child Sexual Abuse (CE-CSA) (Gewehr et al., 2021) questionnaire which consists of three subscales: (1) Naive confidence (NC), (2) Emotional Reactivity (ER) and (3) Justice System Distrust (JSD). Responses were scored on a six-point scale ranging from 1 “fully disagree” to 6 “fully agree.” For the purposes of this study, we used only the emotional reactivity (ER) scale, which allowed us to measure baseline emotionality regarding CSA. The scale demonstrated good reliability (α = 0.88) and convergent validity [Emotional Reactivity subscale correlated positively and quite strongly with measures of empathy (r = 0.38, −0.42), and anger about sexual assault (r = 0.28)] (Gewehr et al., 2022). CE-CSA was originally created in German and translated into English by the authors of the questionnaire. CE-CSA in the present study was translated by the first author from English into Lithuanian and afterward it was back translated from Lithuanian to German to Lithuanian by another German-speaking colleague. When comparing the original German version and back translated German version the items were very similar.

Perception of avatar realism was measured using one-item, as in the previous experiments with Avatars on emotions (Segal et al., 2022). Participants were asked to answer how realistic they perceived the avatar to be. Responses were scored on a five-point scale ranging from 1 “not realistic at all” to 5 “very realistic.”

The whole study was designed and implemented using iMotions Biometric Research Platform 9.3 (iMotions, 2022) and every participant was recorded throughout the experiment. Afterward the video recordings were rendered and imported into Affectiva facial expression recognition engine, which is an integrated component of iMotions This software has built-in recognition for anger, sadness, disgust and surprise, but not for relief. For relief we extracted the most common facial action unit – AU12 Lip Corner Puller (Krumhuber and Scherer, 2011) which corresponds in iMotions to Lip Corner Depressor, and considered it as a facial reaction of relief. Emotion measurements do not depend upon a particular theory of emotion (Coppin and Sander, 2021).

Throughout the experiment the participants wore a portable Shimmer3 GSR + unit. The GSR sensor measures and captures GSR in real time. It monitors skin conductivity utilizing two reusable electrodes attached to two fingers of one hand. When stimuli activate the sweat glands, the increasing moisture will increase the conductance of the skin, allowing the current to flow more readily. Another reusable electrode was attached to the ear lobe and was used to capture Optical Pulse/PPG (Photoplethysmogram) that was converted to heart rate (HR). For the analyses of psychophysiological states, we used GSR and HR parameters.

For the analysis of facial and psychophysiological states we chose 5-s intervals prior to first five closed questions preceding a confirmatory answer and 5-s intervals prior to first five open questions preceding any relevant details provided as a response by the child avatar to the questions that were asked (Picture 1). For some of the participants the number of coded time intervals was lower due to them not asking enough questions. The same number of closed and open questions were included from the participant in these cases (see Table 2 for the numbers of coded questions).

Figure 2 shows that in real time a research participant conducting an interview with an avatar, while their facial expressions and psychophysiological indicators were captured on a storyboard. Later, iMotions allowed the overlaying of annotations of different time intervals on the timeline at points that will be used for further analysis. In our study, all annotations were of 5s length. Annotations in the timeline were placed prior to closed and open questions (max up to 5 + 5). The raw data showing the level of each facial expression and physiological indicators were later processed, extracting average means of each measured indicator per annotation.

By using measures mentioned above, we were able to gather facial expression recordings (objective) and psychophysiological measurements (objective).

Upon arrival, each participant received a short explanation and instruction about the study. Participants were told:

Every participant signed an informed consent form before proceeding with the experiment. All data were collected in a laboratory setting and all of the questionnaires and measurements were conducted in the iMotions platform. After the completion of experiment, participants were debriefed with the interview outcomes and the aim of the study. Every participant then received a 10-euro gift coupon. The experiment included also other components not reported in the present paper.

Generalized estimating equations using the autoregressive relationship (AR) working correlation matrix to represent the within-subject dependencies were used to analyze the data. With this matrix, the repeated measurements have a first-order autoregressive relationship. It is defined so that the correlation between any two elements is equal to rho for adjacent elements, rho² for elements that are separated by a third, and so on (IBM, 2021).

For the first interview (Table 2), the overall mean was 7.70 (SD = 5.41) for the recommended and 17.79 (SD = 10.42) for not recommended questions (see Table 1); 1.47 (SD = 1.58) for the relevant, 1.77 (SD = 1.70) for the neutral and 3.98 (SD = 3.25) for the wrong elicited details; and 68.25% (SD = 15.60) confidence level regarding abuse assumption (M = 1.46; SD = 0.50). The overall number of correct decisions was 25 (Table 3).

For the second interview (Table 2), the overall mean was 6.54 (SD = 5.21) for the recommended and 18.72 (SD = 10.70) for the not recommended questions; 1.72 (SD = 2.21) for the relevant, 1.63 (SD = 1.82) for the neutral and 4.51 (SD = 3.48) for the wrong elicited details; and 64.04% (SD = 14.38) confidence level regarding abuse assumption (M = 1.51; SD = 0.50). The overall number of correct decisions was 34 (Table 3).

Before analyzing the potential impact of emotions and psychophysiological parameters on question formulation during the interviews, we explored possible gender and interview order effects on the measured variables. Generalized estimating equations controlling for dependence of measurements from the same participant and from the same interview were used when comparing male and female participants’ emotions and psychophysiological parameters. Women exhibited more sadness (M = 0.979, SE = 0.224) than men (M = 0.418, SE = 0.153), χ² (1) = 4.261, p = 0.039 as well as more lip-corner depressor activity (relief) (M = 2.741, SE = 0.741) than men (M = 0.161, SE = 0.076), χ² (1) = 11.994, p < 0.001. Women also had higher GSR resistance (M = 176.338, SE = 13.992) than men (M = 104.989, SE = 10.744), χ² (1) = 16.358, p < 0.001. There were no significant differences in anger, surprise, disgust or HR.

Next, we used generalized estimating equations controlling for dependence of measurement from the same participants while comparing the parameters from the two interviews. The participants’ disgust levels reduced from the first interview (M = 1.107, SE = 0.436) to the second interview (M = 0.359, SD = 0.216), χ² (1) = 4.200, p = 0.040. Their level of surprise also reduced from the first interview (M = 3.764, SE = 0.923) to the second interview (M = 1.097, SD = 0.318), χ² (1) = 11.102, p < 0.001.

In contrast, the participants’ sadness increased from the first interview (M = 0.495, SE = 0.170) to the second interview (M = 1.273, SD = 0.338), χ² (1) = 11.083, p < 0.001. Also, lip-corner suppressor activity (relief) increased from the first interview (M = 0.901, SE = 0.397) to the second interview (M = 3.711, SD = 1.161), χ² (1) = 7.255, p = 0.007.

The participants’ GSR resistance reduced from the first interview (M = 169.119, SE = 17.313) to the second interview (M = 152.594, SE = 13.979), χ² (1) = 11.082, p < 0.001. Also, their heart rate reduced from the first interview (M = 85.298, SE = 2.794) to the second interview (M = 79.244, SE = 2.876), χ² (1) = 13.601, p < 0.001.

Table 4 shows correlations between general emotionality and perceived realness and the emotion and the psychophysiological parameters as well as the inter-correlations between the latter variables. As expected, general emotionality was associated with more strongly facially expressed emotions. The associations were significant for all other emotions with the exception of sadness. General emotionality also had a negative association with heart rate. We also expected that perceived realness would be associated with more strongly facially expressed emotions. However, this was only true for surprise and lip-corner suppressor activity (relief). However, perceived realness was associated with both lower GSR resistance and lower heart rate. In terms of the interrelationships between the different emotions and psychophysiological parameters, we found that anger was positively correlated with both disgust and sadness. Further, sadness was strongly associated with lip-corner suppressor activity (relief), raising doubts about whether this measure is a valid indicator of relief. Finally, there were few associations between the psychophysiological parameters and the facially expressed emotions. Higher GSR resistance was associated with both more disgust and more lip-corner suppressor activity (relief).

For the main analyses of interest, we again used generalized estimating equations controlling for dependence of measurement from the same participants and from the same interview. For each dependent variable, we first ran a Question Type × Gender analysis to understand whether closed and open questions were preceded by different types of emotional expressions or psychophysiological responses. After this, we ran Question Type × General Emotionality and Question Type × Perceived Realness analyses to see if the participants’ general emotionality regarding CSA or the extent to which they found the avatars to be convincing and realistic would moderate the associations.

Figures 3, 4 presents the differences in emotions and psychophysiological parameters preceding open and closed questions.

As expected, the participants expressed more anger in their faces preceding closed as opposed to open questions, χ² = 2.917, p = 0.088. This effect was not moderated by gender (p = 0.216), general emotionality (p = 0.399) or perceived realness (p = 0.123). However, contrary to expectations, there were no differences in expressed disgust (p = 0.599), sadness (p = 0.416), surprise (p = 0.931), or lip-corner depressor activity (relief) (p = 0.959). Also, none of these effects were significantly moderated by gender (all ps > 0.316), general emotionality (all ps > 0.163) or perceived realness (all ps > 0.155).

As expected, the participants had higher GSR resistance preceding closed as opposed to open questions, χ² = 11.350, p < 0.001. This effect was not moderated by gender (p = 0.132), general emotionality (p = 0.898) or perceived realness (p = 0.765). Also, as expected, the participants had lower heart rate preceding closed as opposed to open questions, χ2 = 13.496, p < 0.001. Also, this effect was not moderated by gender (p = 0.715), general emotionality (p = 0.345) or perceived realness (p = 0.261).