Low mood, not anxiety, connected with micro facial expression recognition

Low mood, not anxiety, connected with micro facial expression recognition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Low mood, not anxiety, connected with micro facial expression recognition Kasia Wezowski, Ian S. Penton-Voak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7149123/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Previous research has demonstrated that facial expression recognition, an invaluable social skill, may be impaired amongst people suffering from anxiety. Research surrounding this relationship is equivocal and little attention has been given to the effects of anxiety on the recognition of micro expressions. Thus, the present study investigated this relationship. Based on previous research, we expected that participants with high trait anxiety will show a) poorer overall micro expression recognition and b) better angry face recognition. 431 participants completed measures of trait and state anxiety, depression, micro facial expression recognition and indicated demographic information. The results of the study supported neither of the two hypotheses. Combined with previous findings, these results indicate that trait anxiety does not have a robust effect on either general emotion recognition or anger recognition. Previous positive findings may potentially be a consequence of unaccounted effects of low mood or age. On the other hand, the results did show effects of low mood on improved overall micro expression recognition scores and sad face recognition, and bias towards recognizing neutral faces as sad. These findings may be attributed to biases arising from the effects of mood congruence. Health sciences/Health care Biological sciences/Psychology Social science/Psychology 1. Introduction Facial expressions are a basic, relatively universal way of expressing emotions 1 . Their accurate interpretation is a crucial skill in social interactions 2 , 3 , which not all people are equally good at 4 . The ability may be reduced in those suffering from a variety of mental disorders, including depression and anxiety 5 . Reduced social aptitude is a characteristic of various mental health issues 6 , including anxiety, a debilitating disease affecting an large number of people, with the Global Burden of Disease Study 2019 estimating 45.82 million incident cases of anxiety disorders worldwide 7 . The literature on the relationship between various forms of anxiety and facial expression recognition is far from unequivocal. One of the earliest studies on the topic demonstrated that there were differences in the speed of recognizing happy and sad faces depending on the levels of social anxiety, but that there were no differences in the error rate 8 . A lack of differences between more and less anxious participants in the overall accuracy in recognizing faces was detected in many studies since 9 – 11 . However, other research has indicated there is an effect of anxiety on overall emotion recognition 12 – 14 . A meta-study from 2010 also indicated that there was such a relationship, but that it was weaker than the same effect registered in depression 5 . A potential reason for the mismatch in study findings is that some of the research shows that there are differences in emotion recognition between more and less anxious people only when the shown emotions are low in intensity 9 , 11 , 15 . When it comes to specific emotions, a common finding is that more socially anxious people are more accurate when detecting angry faces 16 – 21 , but no such effect has been found for diagnosed anxiety 15 or state or trait anxiety 13 . A study 17 showed that anxiety leads to higher anger sensitivity only in same-race faces, but not in out-group ones. Furthermore, some studies showed higher responsiveness to fear in high trait anxiety participants 22 – 24 , as well as participants diagnosed with social phobia 25 . On the other hand, others have not found such an effect 15 . There are also findings on an increased sensitivity to sadness amongst participants with high anxiety 14 , 26 . A third category of findings in the literature regards bias in emotion recognition that arises from anxiety. A common finding is that people with higher general anxiety 27 or social anxiety 16 , 18 , 28 tend to detect neutral faces as angry. A similar bias towards interpreting happy faces as angry was found in a study in which state anxiety was induced using CO 2 inhalation 12 . However, other studies found no such bias in people with high general 29 or social anxiety 30 , 31 . Anxiety is not the only mental health disorder that has been linked to differences in facial expression recognition. Depression, the fourth leading contributor to the global disease burden 32 , has also been shown to have a relationship with facial expression recognition. Similarly to anxiety, it has been shown to have three main types of effects. One is an overall effect on reduced capability of emotion recognition 5 , 33 – 35 , which is usually attributed to a deficit in cognitive processing associated with depression. The second effect is one on easier recognition of specific emotions, which has most commonly been recognized for sadness 5 , 36 , but also for other emotions, such as disgust 37 , fear 35 , 38 , and surprise 39 . Finally, people with higher depression scores tend to have a bias in expression recognition towards sadness – seeing happy faces as neutral, and neutral faces as sad 35 , 37 , 39 . An important and less researched aspect of facial expressions is the domain of micro expressions. Micro expressions are short (< 0.5 s), spontaneous presentations of the seven basic emotions 40 – 43 . Even if a person is trying to hide their true emotions, micro expressions still appear 44 , making their detection advantageous in social situations. In the recent years, a novel method of measuring micro expressions recognition, the Micro Expression Training Videos (METV) has been developed 45 – 47 . This method utilizes short videos of emotions instead of static images like Ekman’s Micro Expression Training Tools 48 , which improves its ecological validity, since people normally do see facial expression in a dynamic manner. The importance of micro expression, as measured by the METV, is also reflected in the fact that it has been demonstrated that people with low mood have altered micro expression recognition 47 , suggesting a link with mental health. In spite of this, there is no previous research on the relationship between anxiety and micro expressions. Here we focus on two of the most salient findings in the literature. The first is that there might be a relationship between anxiety and overall facial emotion recognition 12 – 14 with some research indicating a lack of an effect as well 9 – 11 . The studies which found the effect found it either for one of the subscales (trait or state) of the state-trait anxiety inventory 12 , 13 or for diagnosed social anxiety disorder 14 . As the scope of the study does not allow for clinical estimation and diagnostics, trait anxiety was seen as the most adequate independent variable to use. Therefore, the first hypothesis of the study was: H1 There will be a negative relationship between trait anxiety and global METV scores. A second common finding in the literature is that people suffering from higher levels of anxiety tend to be more sensitive towards angry face recognition 16 – 21 . While different measures of social and trait anxiety were used in previous research, we have opted to keep trait anxiety as the main independent variable for the sake of consistency and simplicity. Thus, the second research hypothesis was: H2 There will be a positive relationship between trait anxiety and angry face METV scores. Although there are other findings in the literature indicating other effects of anxiety on facial recognition, we abstained from creating study hypotheses for each of the potential effects. Instead, the abovementioned two are the main hypotheses of the study, while other potential relationships that may arise will be determined in exploratory analyses of the data. As studies have also indicated effects of state anxiety 12 , 13 and depression 5 , 34 , 35 , we have also measured these constructs to be used as control variables and in exploratory analyses. Additionally, we attempted to replicate the findings of a previous which investigated the relationship between low mood and micro expression recognition 47 . By testing the same hypotheses as those explored in the previous study, the present one will test the robustness of its findings and bring additional clarity to the relationships between micro expression recognition and mental health. Hence, these are the additional hypotheses, meant to replicate the mentioned previous study 47 . Low mood will be connected to lower overall METV scores Low mood will be connected to higher sad face score Low mood will be connected to mistaking happy faces for neutral ones Low mood will be connected to mistaking neutral faces for sad ones 2. Results 2.1. Sample characteristics The initial sample consisted of 448 participants. 17 participants (3.8% of overall sample) were removed as they had a missing global METV score, suggesting either problems with the presentation of videos or inattention. No further extreme outliers were identified. Thus, the final sample consisted of 431 participants. Since 2 respondents did not report their gender, analyses adjusted for gender as a control variable pertain to smaller samples of 429 respondents (214 female). Participants had an average age of 43.60 (range 18–88, SD = 15.11). Neither of the two independent variables (trait subscale of STAI, BDI-II) showed normal distribution (Shapiro-Wilk p < .001), which is why the continuous variables were transformed into dichotomous groups. The distribution of scores and the descriptive statistics across the two study groups regarding trait anxiety, as well as the two study groups regarding low mood, may be found in the supplementary materials (Tables S1 to S4, Figures S1 and S2). While there are some differences between groups on demographic variables, these differences will be controlled for in the adjusted models presented in the following sections. This was done to prevent possible confounding effects of these variables, as they have been found to influence facial expression recognition or cognitive performance in general in previous research 49 , 50 Table 1 Descriptive statistics of key study variables Variable Mean Std. dev. Min Max METV first correct answers 6.69 2.56 0 16 Angry face first correct answers 0.21 0.21 0 1 Trait anxiety 43.34 4.55 25 55 State anxiety 44.98 4.99 30 62 Depression symptoms (BDI-II) 13.36 12.28 0 49 2.2. Association between trait anxiety and METV performance Prior to interpreting the results, it was determined that no assumptions of regression analyses were broken. The Durbin-Watson statistic was 1.859, indicating no violation of autocorrelation of residuals, VIF values were close to one, and residuals demonstrated a normal distribution upon visual inspection of Q-Q plots. No evidence was found for a negative relationship between trait anxiety and recognising human emotions, in the unadjusted or adjusted (age, gender, education, and low mood) regression analyses. Similarly, no evidence was found for an association between overall anxiety or state anxiety and global METV scores. In the adjusted analyses, very strong evidence was found for an association between age and METV performance of participants ( B = -0.039, CI = -0.054 – -0.023, p < .001), indicating that younger participants display higher levels of competence in recognising human emotions from facial expressions. There was also a marginal level of evidence for the impact of low mood on METV results, with participants in the low mood group showing higher scores. Full details of the models are reported in Table 2 . Table 2 Regression models predicting METV first correct answers Predictors Unadjusted Model 1 Model 2 b [95% CI] p b [95% CI] p b [95% CI] p High trait anxiety group -0.011 .966 0.033 .516 0.073 .767 [-0.501, 0.480] [-0.450, 0.516] [-0.410, 0.556] Age -0.039 < .001 -0.032 < .001 [-0.054, -0.023] [-0.049, -0.015] Gender -0.161 .507 -0.136 .576 [-0.639, 0.316] [-0.613, 0.341] Education 0.062 0.612 0.071 .563 [-0.179, 0.303] [-0.170, 0.311] Low mood group 0.505 0.055 [-0.12, 1.021] Note. n = 429. Ordinary Least Squares regression. Model 1: Adjusted for age, gender, and education level. Model 2: Adjusted for depression symptoms (low mood/control), age, gender, and education level. 2.3. Association between trait anxiety and angry face METV scores Prior to interpreting the results, it was determined that no assumptions of regression analyses were broken. The Durbin-Watson statistic was 2.017, indicating no violation of autocorrelation of residuals, VIF values were close to one, and residuals demonstrated a normal distribution upon visual inspection of Q-Q plots. Neither unadjusted nor adjusted (age, gender, education, low mood) regression analyses have provided any evidence in support of the second hypothesis of an association between trait anxiety and angry face METV scores. In the adjusted analyses, evidence was found for an association between age and angry face METV performance of participants ( B = -0.001, CI = -0.003 – <-0.001, p = 0.039), indicating that younger participants may display higher levels of competence in recognising anger from facial expressions. Full details of the models are reported in Table 3 . Table 3 Regression models predicting angry faces first correct answers Predictors Unadjusted Model 1 Model 2 b [95% CI] p b [95% CI] p b [95% CI] p High trait anxiety group 0.006 .765 0.007 .749 0.006 .759 [-0.035, 0.047] [-0.034, 0.048] [-0.035, 0.048] Age -0.001 .038 -0.001 .049 [-0.003, 0.000] [-0.003, 0.00] Gender -0.029 .162 -0.029 0.161 [-0.070, 0.012] [-0.070, 0.012] Education 0.001 .955 0.001 .959 [-0.020, 0.021] [-0.020, 0.021] Low mood group -0.003 .888 [-0.047, 0.041] Note. n = 429. Ordinary Least Squares regression. Model 1: Adjusted for age, gender, and education level. Model 2: Adjusted for depression symptoms (low mood/control), age, gender, and education level. 2.4. Associations between low mood and METV test performance Exploratory analyses were undertaken in order to investigate the effects of low mood on micro expression recognition. We tested whether there were effects on overall METV scores, sad face recognition, and biases of perceiving neutral faces as sad and happy faces as neutral. The details of these analyses may be found in supplementary materials. A linear regression indicated strong evidence that low mood was predictor of overall METV scores ( B = .872, p < .001) when not controlling for other variables (full models in Table S5). After introducing the demographic variables and trait anxiety, the evidence of relationship became weaker ( B = .505, p = .055). When adjusted for sadness recognition, there is no evidence of an effect ( p = .913). Another linear regression (full models in Table S6) indicated strong evidence that low mood predicted sad face recognition both before ( B = 0.100, p < .001) and after ( B = 0.086, p = .002) controlling for other variables. A binary logistic regression showed no evidence for effects of low mood on mistaking happy faces as neutral, either before (Exp( B ) = 0.597, p = .327) or after (Exp( B ) = 0.536, p = 0.273) controlling for other variables (full models in Table S7). Finally, there was evidence of an effect of low mood on mistaking neutral faces for sad ones after (but not before) controlling for demographics and trait anxiety (Exp ( B ) = 0.253, p = .031). Based on the Exp( B ) coefficient, participants in the low mood group were four times as likely to have mistaken the neutral face as a sad one (Full models in Table S8). We also tested for an interaction effect of low mood and anxiety on all four dependent variables using a series of ANCOVAs and found no significant interaction effects (all p > .05). 3. Discussion The present study examined the effects of anxiety on micro facial expression recognition. The main study hypotheses were that trait anxiety would have a negative effect on micro facial expression recognition and a positive effect on the recognition of angry faces. The study results did not support either of the two hypotheses. Further analyses were conducted in order to attempt replication of a previous study 47 by assessing the effects of low mood on micro expression recognition. The finding that there was no effect of anxiety on overall micro expression recognition is in line with many previous studies 9 – 11 . Other research did find such a relationship 12 – 14 . The differences can be accounted for by the fact that these effects may have been a consequence of either induced anxiety 12 , 13 or an interplay between low mood and anxiety 14 . A meta-analysis 5 also indicated a general effect of anxiety on emotional recognition. However, the studies which their assessment is based off are mostly those showing an effect on bias, and not on general emotion recognition 18 , 25 , 51 – 53 . Some of them also showed effects limited to only certain emotions 24 or ones that can be better explained by depression 54 . In short, although Demenescu et al. 5 state that anxiety leads to a “moderate impairment of facial emotion recognition in adults” (p. 3), this impairment may only be an effect of bias in interpretation, and not a deficit in facial emotion expression recognition per se . Based on our findings and in agreement with those of previous research 9 – 11 , it seems that there is no robust association between anxiety and the overall recognition of facial expressions. The present study’s results additionally support this notion by showing that the relationship is absent in micro expression recognition as well. As we have also demonstrated a marginal effect of low mood on general micro expression recognition, it may be that the previous studies which reported effects of anxiety simply failed to account for their participants’ depression symptoms. Additional investigations may be needed in order to determine if there is any effect of anxiety on facial expression recognition that can be isolated from the common comorbidity with depression and its symptoms. The second study hypothesis, which postulated that angry face recognition would be improved amongst participants with higher trait anxiety, was not supported by the study findings. Participants with lower and higher levels of trait anxiety were equally sensitive to micro expressions of anger, which is in line with some 13 , 15 , but out of line with other previous research 16 – 21 . This is likely a consequence of the fact that all but two 17 , 21 of the cited studies which found this result utilized measures or diagnoses of social anxiety disorders. Some of the previous studies which used measures of general anxiety also found no effects on higher sensitivity towards anger 13 . Another relevant aspect of the studies is that all but one of them 19 showed the strongest effects of anxiety on low-intensity anger. This can be interpreted as a consequence of increased vigilance to threat 20 which is notable with low-intensity depictions of anger, but not with those of higher intensity, as the emotions are too clear. In sum, while social anxiety may have an effect on increasing vigilance towards angry faces, general anxiety does not seem to. This is likely because general anxiety is focused on aspects of life that do not involve social interactions, and is, thus, not as connected to the hypervigilance to threat from others. In addition to examining the effects of anxiety on emotion recognition, the present study also investigated depressive symptoms to replicate previous findings 47 . Consequently, these results will be primarily discussed in relation to that study. The present study’s results showed evidence of an effect of low mood on METV scores in the unadjusted model. When adjusted for demographic characteristics and trait anxiety, the evidence of the relationship became weaker, with age emerging as the strongest predictor, indicating that the effects of age and low mood are connected. The positive effect of low mood on overall METV scores is a consequence of the increased accuracy in recognizing sad faces, which is proven by the fact that the association is lost once the analysis is adjusted for sadness recognition. These findings contrast with those of the replicated study 47 , which reported a negative effect of low mood on micro expression recognition, as measured by the METV. The authors attributed this decline to cognitive impairment associated with low mood. However, that study found no evidence of an association between low mood and improved sadness recognition. The discrepancy between the studies may be due to differences in average depression scores, which were somewhat lower in the present study. This suggests that in the replicated study, cognitive decline associated with depression may have overshadowed any heightened sensitivity to sadness, whereas the opposite pattern emerged here. However, this interpretation remains speculative and would require further research explicitly accounting for cognitive impairment. Age has been found to be an important, negative predictor of micro expression recognition. Previous research agrees with this finding 50 , 55 , 56 and has indicated that the likely reason for this is that the volume of the “social brain”, mainly located in frontal and temporal lobes, is reduced with age 57 . Our study also found significant indicators of interpretation and attention biases in participants with higher BDI-II scores. There was a higher sensitivity for sad faces and a propensity to misjudge neutral faces as sad. There was no bias in interpreting happy faces as neutral. These findings are in line with previous research on sad face sensitivity 58 and biases towards recognizing neutral faces as sad 35 , 37 , 39 , 47 . The previous study 47 which attempted to find these increased sensitivities found only the bias in interpreting neutral faces as sad, but not an overall increased sensitivity towards sadness, and the difference might be due to the larger sample size in the present study. Both of the findings may be interpreted through mood congruence, which affects the way a person perceives, interprets, or remembers things, by painting them with their current emotional state 59 . The present study finds a stark difference in the effects of anxiety and low mood. While they are both mental health disorders that are thought to similarly affect emotion perception, the mechanism by which this occurs is different. Negative attentional bias and the coloured perception of neutral or happy facial expressions can occur differently in individuals who suffer from anxiety and those who suffer from depression. This may explain the difference in results of the study between the two disorders. A number of studies 60 – 62 find that anxiety often manifests in heightened sensitivity to threat cues, leading to a bias towards interpreting ambiguous situations as threatening. Conversely, a number of studies find depression is characterized by deficits in emotional processing 33 – 35 , such as reduced reactivity to positive stimuli and increased attentional focus on negative information. Thus, the mechanisms by which they affect emotion recognition vary. While the necessary contextual factors for the effect of low mood on emotion recognition may have been present in the study, the same cannot be said about anxiety. The heightened sensitivity required in the case of anxiety may not have been elicited by the images used in the METV test, while no such arousal of threat response is necessary in the case of depression. These attention and interpretation biases may be contributing to the development and continuation of depressive symptomatology. Thus, it could be beneficial to train people in reducing these biases, which could have the potential to improve their overall well-being. This idea would yet need to be explored in additional research. The present study had some limitations. One is that we were not able to collect a sample from a clinically diagnosed population with anxiety disorders. Instead, we used online convenience sampling and a survey to measure the variance in anxiety amongst this unknown population. Thus, as the maximum trait anxiety in the sample was 55, and the theoretical maximum of the trait STAI sub-scale is 80, it is clear that we have not had people with very high anxiety in the sample. Hence, this lack of variance in trait anxiety may have contributed to the lack of evidence of the relationships. Yet, while limiting our findings to a non-clinical population, the study sample was still sufficiently large and heterogenous to capture some existing effects. Another limitation is that we have not included a measure of social anxiety, which may be necessary in order to fully understand the relationship between anxiety and facial expression recognition. Yet, there is certainly collinearity between trait anxiety and social anxiety, and the present study focused on the former. Thus, the investigation of the effect of social anxiety on micro expression recognition should be conducted in a future study. Lastly, the hypotheses about low mood that were tested were not set forward prior to the study, but were added after the data collection. While they are still based on previous research, they were not pre-planned, which introduces some potential bias. The study also had several strengths. One was that we utilized a sufficiently large sample, which was projected from previous research on the power of expected results. Thus, the absence of support for our hypotheses may not be attributed to an insufficiently large sample. The sample was also heterogenous in terms of age, gender and education. Another strength is that we utilized STAI and BDI-II, questionnaires with well-supported validity and reliability from previous research. We also utilized METV, a relatively new instrument that has shown reliability and validity as well, and is further validated by the findings of the present study which indicate that it shows very similar relationships as those found in previous research. Furthermore, a part of the study was a replication of previous findings, which is very important for the development of psychological sciences 63 . Finally, we included demographic and other control variables in all regression models, which prevented potential false positive findings, thus improving the validity of the present results. 4. Conclusions The present study had the main objective of investigating the effects of trait anxiety on general micro facial expression recognition, as well as on the recognition of angry faces. Controlling for demographic variables and low mood, we found no such effects. Hence, the main novel finding of the present study is the lack of association between micro expression processing, as measured by METV, and anxiety. A secondary objective of the study was to replicate previous findings on the relationship between low mood and METV performance. In this regard, we found effects of low mood on overall METV performance, sad face sensitivity, and a bias towards recognizing sadness in neutral faces, which is in only partially in line with the findings of the replicated study, demonstrating a necessity for additional research. Our findings indicate that trait anxiety may not be relevant for micro facial expression recognition. However, low mood likely has an important effect on attention and interpretation biases, and these effects are not yet fully understood, given the fact that the findings of the present study are only partially aligned with those of the one we replicated. Hence, additional research should be conducted in order to understand the details of the relationship between facial expression recognition and various issues with mental health. Such studies will help us work on various programs that could improve the social functioning and quality of life of persons struggling with these debilitating conditions. 5. Methods The study was pre-registered on OSF: https://osf.io/mqud7/ . Ethics approval was obtained from the Faculty of Science Research Ethics Committee at the University of Bristol (Approval Code: 2023-15983-17913). The study was conducted according to the revised Declaration of Helsinki (2013) and the 1996 ICH Guidelines for Good Clinical Practice E6(R2). All methods were performed in accordance with the relevant guidelines and regulations. Data collection started on 26/10/2023, and ended on 10/12/2023. The investigator explained the nature, purpose and risks of the study to the participants in an online information sheet before they consented to participate in the study by clicking a button. Participants were informed that they were free to withdraw at any time by simply closing the web page. Thus, all participants were provided informed consent during the study. All data was anonymized before analysis. 5.1. Study Design This study used an observational, cross-sectional design. The primary measures in the study were the participants’ overall scores on the METV micro expression recognition test, the score on the anger faces, and the groups based on the scores of the trait anxiety sub-scale of the State-Trait Anxiety Inventory (STAI). The main independent variable of the study was the anxiety group created by splitting the sample into two equally-sized parts, based on the participants’ trait anxiety score. Thus, the trait anxiety group was a categorical, nominal variable with two levels (low anxiety/high anxiety). The first dependent variable of the study was the participants’ overall METV score, which was calculated as the proportion of correct answers given to the test on the first try. Thus, it is a numeric variable measured on the ratio level. The theoretical minimal and maximal scores are 0 and 1. The second dependent variable was the participants’ score on angry faces, which is also calculated as the proportion of correct answers given on the first try. It is also a numeric variable measured on the ratio level, with the theoretical score ranging from 0 to 1. 5.2. Participants and Recruitment Participant recruitment was done through Prolific. Initial screening done by the platform allowed us to exclude participants who currently used psychotropic medication, as research demonstrated that it has an effect on emotion recognition 64 . We also used the pre-screening to create a diverse sample in terms of their self-reported anxiety. Due to the limited nature of the way in which it was measured on the platform (a simple yes/no answer), this was not taken into account when creating participant groups in the study, but instead their scores on the STAI were used for that purpose. Furthermore, as these pre-screening questionnaires were completed an unknown time before recruitment into the study, medication use was checked in the study survey. Only participants over 18 were recruited for the study, they had to be fluent in English, and they were reimbursed £4. 5.2.1. Sample size determination It has been shown in a meta-study that the average effect size for the effect of anxiety on facial expression recognition is d = -0.35 5 . Using the G*Power software, we determined that the sample size needed to achieve this effect size (with the assumption that this effect size will be similar for micro expression recognition) at 0.95 power is 428. Therefore, the aimed sample size was 450 (225 per group), so that there would be a sufficient number of participants after outliers and non-completers are removed from the sample. 5.2.2. Withdrawal of participants Participants were informed that they were able to withdraw from the study at any time by leaving the study webpage. Participants who opted out before completing the survey did not receive a reimbursement. 5.3. Measures and Materials 5.3.1. Micro Expressions Training Videos (METV) The METV 46 measured micro expression recognition ability based on the detection of micro expressions on videos. It is based on the Facial Action Coding System rules (Ekman & Friesen, 1978). There were 20 videos, each showing a male or female white person presenting a single micro expression, for 0.5 seconds or shorter. Since each emotion can be demonstrated by a different number of micro expressions 65 , 66 , the emotions were not represented in equal numbers of stimuli – there were 4 micro expressions of anger, 4 of sadness, 3 of fear, 3 of surprise, 2 of disgust, 2 of contempt, 1 of happiness, and 1 neutral face. Once the emotion is presented, the participants answer by selecting one of the eight possible answers (7 emotions + neutral). Completing the test took about 10 minutes. While additional answers (up to 3) are allowed to participants if they make a mistake on the first try, only the proportion of correct answers on the first try was used as the dependent variable in the present study. 5.3.2. The State-Trait Anxiety Inventory (STAI) The State-Trait Anxiety Inventory (STAI) is one of the most commonly used measures of trait and state anxiety 67 . It consists of 40 items, 20 measuring state anxiety (example: “I am worried”) and 20 measuring trait anxiety (example: “I worry too much over something that really doesn’t matter”). 5.3.3. The Beck Depression Inventory-II (BDI-II) The Beck Depression Inventory-II (BDI-II) was used to measure depressive symptoms. The questionnaire contains 21 items, on which the frequency of experiencing different symptoms of depression within the past two weeks are indicated on a Likert-type scale ranging from 0 to 3 68 . One item (Q9 – suicidal ideation) was removed from the questionnaire in in response to local ethics review. The scale has excellent reliability (average Cronbach’s α in a meta-study 0.9) and validity 69 . 5.3.4. Sociodemographic Data Sociodemographic factors were also measured, which included age in years, gender (male, female, other), and highest level of education. 5.4. Procedure The study involved a single online session lasting approximately 20 minutes. After filling in the demographic questions, participants were administered the State-Trait Anxiety Inventory and the Beck Depression Inventory-II. Then, the participants went through 20 videos with the force choice option on the METV test under a separate link. In each part of the survey, participants were required to share their email address and name. These were used in order to merge the data from the different measures, and then the data was anonymized. 5.5. Data Analysis 5.5.1. Data screening Two data screening criteria were used in order to clean the data. First, extreme outliers – defined as those participants whose METV and/or trait STAI test scores lie more than 3 times the interquartile range below the first quartile or above the third quartile – were removed from the dataset. Less extreme outliers – defined as those participants whose METV and/or trait STAI test scores lie between 1.5 and 3 times the interquartile range below the first quartile or above the third quartile – were included in primary analyses but their influence was investigated through sensitivity analyses that exclude them. Participants that did not comply with the basic instructions of the study (e.g., used Chrome browsers or smart phones, which are not compatible with the METV), were removed from the dataset. Furthermore, for all statistical analyses conducted for this study, all relevant assumptions (such as normality of data and homoscedasticity of the residuals) were verified prior to analyses. 5.5.2. Analysis For the data analyses, we allocated people into two equal groups (low anxiety, high anxiety) based on the trait anxiety sub-scale of the State-Trait Anxiety Inventory (STAI). Similarly, two equal groups were created based on the BDI-II scores (low mood, control). Both separations of participants into groups were done by using the median value. For trait anxiety, the median value was 44. As 55% of participants had a score of 44 or less, we have performed all subsequent analyses with both 44 and 43 as the cut-off value, in order to prevent any bias due to more participants being in one of the two groups. There were no differences in results, and the presented results were done with low anxiety group containing participants who scored 44 and less, while the high anxiety group contained participants who scores 45 and more. The median score on BDI-II was 10, with 52.2% of participants scoring 10 or less on BDI-II. Since 49.4% of participants scored 9 or less, we classified participants scoring 10 or more into the low mood group, while those with scores 9 and below were classified as the control group. This was done in order to create groups as equal in size as possible. Regression analyses were utilized to check for the relationship of trait anxiety (high, low) with the overall METV score, as well as the anger recognition score. A series of Ordinary Least Squares (OLS) linear regression analyses was conducted on the obtained study data. To test H1 and obtain the primary outcome of the study, we compared the METV test scores between the two groups made based on trait anxiety. For H2, the two anxiety groups were compared on the accuracy in detecting angry faces. In the complete models for both hypotheses, the results were adjusted for low mood and the demographic control variables age, sex, and level of education. Declarations Funding We received no funding for the present study. Acknowledgements We would like to thank FACS Certified Coders, Thomas Nichols from the USA and Tainã Veloso from Portugal for their help with the verification of micro facial expressions shown in the METV. This study was supported by the National Institute for Health and Care Research Bristol Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Author contributions Kasia Wezowski: Conceptualization, Methodology, Data curation, Formal Analysis, Writing- Original draft preparation, Investigation. Ian S. Penton-Voak: Supervision, Reviewing and Editing. Data availability statement Study data and code used for analysis is available on OSF: https://osf.io/mqud7/. Additional Information Informed consent All participants were provided informed consent during the study. 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Education and cognitive functioning across the life span. Psychological Science in the Public Interest 21 , 6–41 (2020). Tucker-Drob, E. M., Brandmaier, A. M. & Lindenberger, U. Coupled cognitive changes in adulthood: A meta-analysis. Psychological bulletin 145 , 273 (2019). Ruffman, T., Henry, J. D., Livingstone, V. & Phillips, L. H. A meta-analytic review of emotion recognition and aging: Implications for neuropsychological models of aging. Neuroscience & Biobehavioral Reviews 32 , 863–881 (2008). Nakamura, A., Takizawa, R. & Shimoyama, H. Increased sensitivity to sad faces in depressive symptomatology: A longitudinal study. Journal of Affective Disorders 240 , 99–104 (2018). LeMoult, J. & Gotlib, I. H. Depression: A cognitive perspective. Clinical psychology review 69 , 51–66 (2019). Sussman, T. J., Szekely, A., Hajcak, G. & Mohanty, A. It’s all in the anticipation: How perception of threat is enhanced in anxiety. Emotion 16 , 320–327 (2016). Reuman, L., Jacoby, R. J., Fabricant, L. E., Herring, B. & Abramowitz, J. S. Uncertainty as an anxiety cue at high and low levels of threat. Journal of Behavior Therapy and Experimental Psychiatry 47 , 111–119 (2015). O’Donovan, A., Slavich, G. M., Epel, E. S. & Neylan, T. C. Exaggerated neurobiological sensitivity to threat as a mechanism linking anxiety with increased risk for diseases of aging. Neuroscience & Biobehavioral Reviews 37 , 96–108 (2013). Fletcher, S. C. The role of replication in psychological science. Euro Jnl Phil Sci 11 , 23 (2021). Sabino, A. D. V., Chagas, M. H. N. & Osório, F. L. Effects of psychotropic drugs used in the treatment of anxiety disorders on the recognition of facial expressions of emotion: Critical analysis of literature. Neuroscience & Biobehavioral Reviews 71 , 802–809 (2016). Ekman, P. & Friesen, W. V. Unmasking the Face: A Guide to Recognizing Emotions from Facial Clues . vol. 10 (Ishk, 2003). Wezowski, K. & Wezowski, P. The Micro Expressions Book for Business: How to Read Facial Expressions for More Effective Negotiations, Sales and Recruitment . (New Vision, 2012). Spielberg, C., Gorsuch, R., Lushene, R., Vagg, P. & Jacobs, G. Manual for the State-Trait Anxiety Inventory (STAI Form Y). (1983). Beck, A. T., Steer, R. A. & Brown, G. Beck depression inventory–II. Psychological assessment (1996). Wang, Y.-P. & Gorenstein, C. Psychometric properties of the Beck Depression Inventory-II: a comprehensive review. Rev. Bras. Psiquiatr. 35 , 416–431 (2013). Additional Declarations Competing interest reported. Kasia Wezowski is the co-founder of the Center for Body Language where she gives training and coaching. Ian S. Penton-Voak declares no competing interests. 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Kasia Wezowski is the co-founder of the Center for Body Language where she gives training and coaching.\nIan S. Penton-Voak declares no competing interests.","formattedTitle":"Low mood, not anxiety, connected with micro facial expression recognition","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFacial expressions are a basic, relatively universal way of expressing emotions\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Their accurate interpretation is a crucial skill in social interactions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, which not all people are equally good at\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The ability may be reduced in those suffering from a variety of mental disorders, including depression and anxiety\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Reduced social aptitude is a characteristic of various mental health issues\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, including anxiety, a debilitating disease affecting an large number of people, with the Global Burden of Disease Study 2019 estimating 45.82\u0026nbsp;million incident cases of anxiety disorders worldwide\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe literature on the relationship between various forms of anxiety and facial expression recognition is far from unequivocal. One of the earliest studies on the topic demonstrated that there were differences in the speed of recognizing happy and sad faces depending on the levels of social anxiety, but that there were no differences in the error rate\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. A lack of differences between more and less anxious participants in the overall accuracy in recognizing faces was detected in many studies since\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, other research has indicated there is an effect of anxiety on overall emotion recognition\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A meta-study from 2010 also indicated that there was such a relationship, but that it was weaker than the same effect registered in depression\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. A potential reason for the mismatch in study findings is that some of the research shows that there are differences in emotion recognition between more and less anxious people only when the shown emotions are low in intensity\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhen it comes to specific emotions, a common finding is that more socially anxious people are more accurate when detecting angry faces\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, but no such effect has been found for diagnosed anxiety\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e or state or trait anxiety\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A study\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e showed that anxiety leads to higher anger sensitivity only in same-race faces, but not in out-group ones. Furthermore, some studies showed higher responsiveness to fear in high trait anxiety participants\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, as well as participants diagnosed with social phobia\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. On the other hand, others have not found such an effect\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. There are also findings on an increased sensitivity to sadness amongst participants with high anxiety\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA third category of findings in the literature regards bias in emotion recognition that arises from anxiety. A common finding is that people with higher general anxiety\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e or social anxiety\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e tend to detect neutral faces as angry. A similar bias towards interpreting happy faces as angry was found in a study in which state anxiety was induced using CO\u003csub\u003e2\u003c/sub\u003e inhalation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, other studies found no such bias in people with high general\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e or social anxiety\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnxiety is not the only mental health disorder that has been linked to differences in facial expression recognition. Depression, the fourth leading contributor to the global disease burden\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, has also been shown to have a relationship with facial expression recognition. Similarly to anxiety, it has been shown to have three main types of effects. One is an overall effect on reduced capability of emotion recognition\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which is usually attributed to a deficit in cognitive processing associated with depression. The second effect is one on easier recognition of specific emotions, which has most commonly been recognized for sadness\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, but also for other emotions, such as disgust\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, fear\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and surprise\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Finally, people with higher depression scores tend to have a bias in expression recognition towards sadness \u0026ndash; seeing happy faces as neutral, and neutral faces as sad\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAn important and less researched aspect of facial expressions is the domain of micro expressions. Micro expressions are short (\u0026lt;\u0026thinsp;0.5 s), spontaneous presentations of the seven basic emotions\u003csup\u003e\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Even if a person is trying to hide their true emotions, micro expressions still appear\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, making their detection advantageous in social situations. In the recent years, a novel method of measuring micro expressions recognition, the Micro Expression Training Videos (METV) has been developed\u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This method utilizes short videos of emotions instead of static images like Ekman\u0026rsquo;s Micro Expression Training Tools\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, which improves its ecological validity, since people normally do see facial expression in a dynamic manner. The importance of micro expression, as measured by the METV, is also reflected in the fact that it has been demonstrated that people with low mood have altered micro expression recognition\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, suggesting a link with mental health. In spite of this, there is no previous research on the relationship between anxiety and micro expressions.\u003c/p\u003e\u003cp\u003eHere we focus on two of the most salient findings in the literature. The first is that there might be a relationship between anxiety and overall facial emotion recognition\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e with some research indicating a lack of an effect as well\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The studies which found the effect found it either for one of the subscales (trait or state) of the state-trait anxiety inventory\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e or for diagnosed social anxiety disorder\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. As the scope of the study does not allow for clinical estimation and diagnostics, trait anxiety was seen as the most adequate independent variable to use. Therefore, the first hypothesis of the study was:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003eThere will be a negative relationship between trait anxiety and global METV scores.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eA second common finding in the literature is that people suffering from higher levels of anxiety tend to be more sensitive towards angry face recognition\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. While different measures of social and trait anxiety were used in previous research, we have opted to keep trait anxiety as the main independent variable for the sake of consistency and simplicity. Thus, the second research hypothesis was:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003eThere will be a positive relationship between trait anxiety and angry face METV scores.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAlthough there are other findings in the literature indicating other effects of anxiety on facial recognition, we abstained from creating study hypotheses for each of the potential effects. Instead, the abovementioned two are the main hypotheses of the study, while other potential relationships that may arise will be determined in exploratory analyses of the data. As studies have also indicated effects of state anxiety\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and depression\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, we have also measured these constructs to be used as control variables and in exploratory analyses.\u003c/p\u003e\u003cp\u003eAdditionally, we attempted to replicate the findings of a previous which investigated the relationship between low mood and micro expression recognition\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. By testing the same hypotheses as those explored in the previous study, the present one will test the robustness of its findings and bring additional clarity to the relationships between micro expression recognition and mental health. Hence, these are the additional hypotheses, meant to replicate the mentioned previous study\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLow mood will be connected to lower overall METV scores\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLow mood will be connected to higher sad face score\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLow mood will be connected to mistaking happy faces for neutral ones\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLow mood will be connected to mistaking neutral faces for sad ones\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Sample characteristics\u003c/h2\u003e\u003cp\u003eThe initial sample consisted of 448 participants. 17 participants (3.8% of overall sample) were removed as they had a missing global METV score, suggesting either problems with the presentation of videos or inattention. No further extreme outliers were identified. Thus, the final sample consisted of 431 participants. Since 2 respondents did not report their gender, analyses adjusted for gender as a control variable pertain to smaller samples of 429 respondents (214 female). Participants had an average age of 43.60 (range 18\u0026ndash;88, SD\u0026thinsp;=\u0026thinsp;15.11). Neither of the two independent variables (trait subscale of STAI, BDI-II) showed normal distribution (Shapiro-Wilk \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), which is why the continuous variables were transformed into dichotomous groups. The distribution of scores and the descriptive statistics across the two study groups regarding trait anxiety, as well as the two study groups regarding low mood, may be found in the supplementary materials (Tables S1 to S4, Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). While there are some differences between groups on demographic variables, these differences will be controlled for in the adjusted models presented in the following sections. This was done to prevent possible confounding effects of these variables, as they have been found to influence facial expression recognition or cognitive performance in general in previous research\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u0026gt;\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of key study variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMETV first correct answers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngry face first correct answers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression symptoms (BDI-II)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Association between trait anxiety and METV performance\u003c/h2\u003e\u003cp\u003ePrior to interpreting the results, it was determined that no assumptions of regression analyses were broken. The Durbin-Watson statistic was 1.859, indicating no violation of autocorrelation of residuals, VIF values were close to one, and residuals demonstrated a normal distribution upon visual inspection of Q-Q plots. No evidence was found for a negative relationship between trait anxiety and recognising human emotions, in the unadjusted or adjusted (age, gender, education, and low mood) regression analyses. Similarly, no evidence was found for an association between overall anxiety or state anxiety and global METV scores. In the adjusted analyses, very strong evidence was found for an association between age and METV performance of participants (\u003cem\u003eB\u003c/em\u003e = -0.039, CI = -0.054 \u0026ndash; -0.023, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that younger participants display higher levels of competence in recognising human emotions from facial expressions. There was also a marginal level of evidence for the impact of low mood on METV results, with participants in the low mood group showing higher scores. Full details of the models are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression models predicting METV first correct answers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e [95% CI]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e [95% CI]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e [95% CI]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh trait anxiety group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[-0.501, 0.480]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.450, 0.516]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.410, 0.556]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.054,\u003c/p\u003e\u003cp\u003e-0.023]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.049,\u003c/p\u003e\u003cp\u003e-0.015]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.639, 0.316]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.613,\u003c/p\u003e\u003cp\u003e0.341]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.563\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.179, 0.303]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.170, 0.311]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLow mood group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.12, 1.021]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote. n\u003c/em\u003e\u0026thinsp;=\u0026thinsp;429. Ordinary Least Squares regression. Model 1: Adjusted for age, gender, and education level. Model 2: Adjusted for depression symptoms (low mood/control), age, gender, and education level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here\u0026gt;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Association between trait anxiety and angry face METV scores\u003c/h2\u003e\u003cp\u003ePrior to interpreting the results, it was determined that no assumptions of regression analyses were broken. The Durbin-Watson statistic was 2.017, indicating no violation of autocorrelation of residuals, VIF values were close to one, and residuals demonstrated a normal distribution upon visual inspection of Q-Q plots. Neither unadjusted nor adjusted (age, gender, education, low mood) regression analyses have provided any evidence in support of the second hypothesis of an association between trait anxiety and angry face METV scores. In the adjusted analyses, evidence was found for an association between age and angry face METV performance of participants (\u003cem\u003eB\u003c/em\u003e = -0.001, CI = -0.003 \u0026ndash; \u0026lt;-0.001, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039), indicating that younger participants may display higher levels of competence in recognising anger from facial expressions. Full details of the models are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression models predicting angry faces first correct answers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnadjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e [95% CI]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e [95% CI]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eb\u003c/em\u003e [95% CI]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHigh trait anxiety group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.759\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[-0.035, 0.047]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.034, 0.048]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.035, 0.048]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.003, 0.000]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.003, 0.00]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.070, 0.012]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.070, 0.012]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[-0.020, 0.021]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.020, 0.021]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLow mood group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[-0.047, 0.041]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote. n\u003c/em\u003e\u0026thinsp;=\u0026thinsp;429. Ordinary Least Squares regression. Model 1: Adjusted for age, gender, and education level. Model 2: Adjusted for depression symptoms (low mood/control), age, gender, and education level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u0026lt;Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here\u0026gt;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Associations between low mood and METV test performance\u003c/h2\u003e\u003cp\u003eExploratory analyses were undertaken in order to investigate the effects of low mood on micro expression recognition. We tested whether there were effects on overall METV scores, sad face recognition, and biases of perceiving neutral faces as sad and happy faces as neutral. The details of these analyses may be found in supplementary materials. A linear regression indicated strong evidence that low mood was predictor of overall METV scores (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.872, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) when not controlling for other variables (full models in Table S5). After introducing the demographic variables and trait anxiety, the evidence of relationship became weaker (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.505, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.055). When adjusted for sadness recognition, there is no evidence of an effect (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.913). Another linear regression (full models in Table S6) indicated strong evidence that low mood predicted sad face recognition both before (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.100, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and after (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.086, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) controlling for other variables. A binary logistic regression showed no evidence for effects of low mood on mistaking happy faces as neutral, either before (Exp(\u003cem\u003eB\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;0.597, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.327) or after (Exp(\u003cem\u003eB\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;0.536, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.273) controlling for other variables (full models in Table S7). Finally, there was evidence of an effect of low mood on mistaking neutral faces for sad ones after (but not before) controlling for demographics and trait anxiety (Exp (\u003cem\u003eB\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;0.253, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.031). Based on the Exp(\u003cem\u003eB\u003c/em\u003e) coefficient, participants in the low mood group were four times as likely to have mistaken the neutral face as a sad one (Full models in Table S8). We also tested for an interaction effect of low mood and anxiety on all four dependent variables using a series of ANCOVAs and found no significant interaction effects (all p\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe present study examined the effects of anxiety on micro facial expression recognition. The main study hypotheses were that trait anxiety would have a negative effect on micro facial expression recognition and a positive effect on the recognition of angry faces. The study results did not support either of the two hypotheses. Further analyses were conducted in order to attempt replication of a previous study\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e by assessing the effects of low mood on micro expression recognition.\u003c/p\u003e\u003cp\u003eThe finding that there was no effect of anxiety on overall micro expression recognition is in line with many previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Other research did find such a relationship\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The differences can be accounted for by the fact that these effects may have been a consequence of either induced anxiety\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e or an interplay between low mood and anxiety\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A meta-analysis\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e also indicated a general effect of anxiety on emotional recognition. However, the studies which their assessment is based off are mostly those showing an effect on bias, and not on general emotion recognition\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Some of them also showed effects limited to only certain emotions\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e or ones that can be better explained by depression\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In short, although Demenescu et al.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e state that anxiety leads to a \u0026ldquo;moderate impairment of facial emotion recognition in adults\u0026rdquo; (p. 3), this impairment may only be an effect of bias in interpretation, and not a deficit in facial emotion expression recognition \u003cem\u003eper se\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eBased on our findings and in agreement with those of previous research\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, it seems that there is no robust association between anxiety and the overall recognition of facial expressions. The present study\u0026rsquo;s results additionally support this notion by showing that the relationship is absent in micro expression recognition as well. As we have also demonstrated a marginal effect of low mood on general micro expression recognition, it may be that the previous studies which reported effects of anxiety simply failed to account for their participants\u0026rsquo; depression symptoms. Additional investigations may be needed in order to determine if there is any effect of anxiety on facial expression recognition that can be isolated from the common comorbidity with depression and its symptoms.\u003c/p\u003e\u003cp\u003eThe second study hypothesis, which postulated that angry face recognition would be improved amongst participants with higher trait anxiety, was not supported by the study findings. Participants with lower and higher levels of trait anxiety were equally sensitive to micro expressions of anger, which is in line with some\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, but out of line with other previous research\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This is likely a consequence of the fact that all but two\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e of the cited studies which found this result utilized measures or diagnoses of social anxiety disorders. Some of the previous studies which used measures of general anxiety also found no effects on higher sensitivity towards anger\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Another relevant aspect of the studies is that all but one of them\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e showed the strongest effects of anxiety on low-intensity anger. This can be interpreted as a consequence of increased vigilance to threat\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e which is notable with low-intensity depictions of anger, but not with those of higher intensity, as the emotions are too clear. In sum, while social anxiety may have an effect on increasing vigilance towards angry faces, general anxiety does not seem to. This is likely because general anxiety is focused on aspects of life that do not involve social interactions, and is, thus, not as connected to the hypervigilance to threat from others.\u003c/p\u003e\u003cp\u003eIn addition to examining the effects of anxiety on emotion recognition, the present study also investigated depressive symptoms to replicate previous findings\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Consequently, these results will be primarily discussed in relation to that study. The present study\u0026rsquo;s results showed evidence of an effect of low mood on METV scores in the unadjusted model. When adjusted for demographic characteristics and trait anxiety, the evidence of the relationship became weaker, with age emerging as the strongest predictor, indicating that the effects of age and low mood are connected. The positive effect of low mood on overall METV scores is a consequence of the increased accuracy in recognizing sad faces, which is proven by the fact that the association is lost once the analysis is adjusted for sadness recognition. These findings contrast with those of the replicated study\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, which reported a negative effect of low mood on micro expression recognition, as measured by the METV. The authors attributed this decline to cognitive impairment associated with low mood. However, that study found no evidence of an association between low mood and improved sadness recognition. The discrepancy between the studies may be due to differences in average depression scores, which were somewhat lower in the present study. This suggests that in the replicated study, cognitive decline associated with depression may have overshadowed any heightened sensitivity to sadness, whereas the opposite pattern emerged here. However, this interpretation remains speculative and would require further research explicitly accounting for cognitive impairment. Age has been found to be an important, negative predictor of micro expression recognition. Previous research agrees with this finding\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e and has indicated that the likely reason for this is that the volume of the \u0026ldquo;social brain\u0026rdquo;, mainly located in frontal and temporal lobes, is reduced with age\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study also found significant indicators of interpretation and attention biases in participants with higher BDI-II scores. There was a higher sensitivity for sad faces and a propensity to misjudge neutral faces as sad. There was no bias in interpreting happy faces as neutral. These findings are in line with previous research on sad face sensitivity\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and biases towards recognizing neutral faces as sad\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The previous study\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e which attempted to find these increased sensitivities found only the bias in interpreting neutral faces as sad, but not an overall increased sensitivity towards sadness, and the difference might be due to the larger sample size in the present study. Both of the findings may be interpreted through mood congruence, which affects the way a person perceives, interprets, or remembers things, by painting them with their current emotional state\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe present study finds a stark difference in the effects of anxiety and low mood. While they are both mental health disorders that are thought to similarly affect emotion perception, the mechanism by which this occurs is different. Negative attentional bias and the coloured perception of neutral or happy facial expressions can occur differently in individuals who suffer from anxiety and those who suffer from depression. This may explain the difference in results of the study between the two disorders. A number of studies\u003csup\u003e\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e find that anxiety often manifests in heightened sensitivity to threat cues, leading to a bias towards interpreting ambiguous situations as threatening. Conversely, a number of studies find depression is characterized by deficits in emotional processing\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, such as reduced reactivity to positive stimuli and increased attentional focus on negative information. Thus, the mechanisms by which they affect emotion recognition vary. While the necessary contextual factors for the effect of low mood on emotion recognition may have been present in the study, the same cannot be said about anxiety. The heightened sensitivity required in the case of anxiety may not have been elicited by the images used in the METV test, while no such arousal of threat response is necessary in the case of depression.\u003c/p\u003e\u003cp\u003eThese attention and interpretation biases may be contributing to the development and continuation of depressive symptomatology. Thus, it could be beneficial to train people in reducing these biases, which could have the potential to improve their overall well-being. This idea would yet need to be explored in additional research.\u003c/p\u003e\u003cp\u003eThe present study had some limitations. One is that we were not able to collect a sample from a clinically diagnosed population with anxiety disorders. Instead, we used online convenience sampling and a survey to measure the variance in anxiety amongst this unknown population. Thus, as the maximum trait anxiety in the sample was 55, and the theoretical maximum of the trait STAI sub-scale is 80, it is clear that we have not had people with very high anxiety in the sample. Hence, this lack of variance in trait anxiety may have contributed to the lack of evidence of the relationships. Yet, while limiting our findings to a non-clinical population, the study sample was still sufficiently large and heterogenous to capture some existing effects. Another limitation is that we have not included a measure of social anxiety, which may be necessary in order to fully understand the relationship between anxiety and facial expression recognition. Yet, there is certainly collinearity between trait anxiety and social anxiety, and the present study focused on the former. Thus, the investigation of the effect of social anxiety on micro expression recognition should be conducted in a future study. Lastly, the hypotheses about low mood that were tested were not set forward prior to the study, but were added after the data collection. While they are still based on previous research, they were not pre-planned, which introduces some potential bias.\u003c/p\u003e\u003cp\u003eThe study also had several strengths. One was that we utilized a sufficiently large sample, which was projected from previous research on the power of expected results. Thus, the absence of support for our hypotheses may not be attributed to an insufficiently large sample. The sample was also heterogenous in terms of age, gender and education. Another strength is that we utilized STAI and BDI-II, questionnaires with well-supported validity and reliability from previous research. We also utilized METV, a relatively new instrument that has shown reliability and validity as well, and is further validated by the findings of the present study which indicate that it shows very similar relationships as those found in previous research. Furthermore, a part of the study was a replication of previous findings, which is very important for the development of psychological sciences\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Finally, we included demographic and other control variables in all regression models, which prevented potential false positive findings, thus improving the validity of the present results.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe present study had the main objective of investigating the effects of trait anxiety on general micro facial expression recognition, as well as on the recognition of angry faces. Controlling for demographic variables and low mood, we found no such effects. Hence, the main novel finding of the present study is the lack of association between micro expression processing, as measured by METV, and anxiety. A secondary objective of the study was to replicate previous findings on the relationship between low mood and METV performance. In this regard, we found effects of low mood on overall METV performance, sad face sensitivity, and a bias towards recognizing sadness in neutral faces, which is in only partially in line with the findings of the replicated study, demonstrating a necessity for additional research. Our findings indicate that trait anxiety may not be relevant for micro facial expression recognition. However, low mood likely has an important effect on attention and interpretation biases, and these effects are not yet fully understood, given the fact that the findings of the present study are only partially aligned with those of the one we replicated. Hence, additional research should be conducted in order to understand the details of the relationship between facial expression recognition and various issues with mental health. Such studies will help us work on various programs that could improve the social functioning and quality of life of persons struggling with these debilitating conditions.\u003c/p\u003e"},{"header":"5. Methods","content":"\u003cp\u003eThe study was pre-registered on OSF: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/mqud7/\u003c/span\u003e\u003cspan address=\"https://osf.io/mqud7/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Ethics approval was obtained from the Faculty of Science Research Ethics Committee at the University of Bristol (Approval Code: 2023-15983-17913). The study was conducted according to the revised Declaration of Helsinki (2013) and the 1996 ICH Guidelines for Good Clinical Practice E6(R2). All methods were performed in accordance with the relevant guidelines and regulations. Data collection started on 26/10/2023, and ended on 10/12/2023. The investigator explained the nature, purpose and risks of the study to the participants in an online information sheet before they consented to participate in the study by clicking a button. Participants were informed that they were free to withdraw at any time by simply closing the web page. Thus, all participants were provided informed consent during the study. All data was anonymized before analysis.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Study Design\u003c/h2\u003e\u003cp\u003eThis study used an observational, cross-sectional design. The primary measures in the study were the participants\u0026rsquo; overall scores on the METV micro expression recognition test, the score on the anger faces, and the groups based on the scores of the trait anxiety sub-scale of the State-Trait Anxiety Inventory (STAI). The main independent variable of the study was the anxiety group created by splitting the sample into two equally-sized parts, based on the participants\u0026rsquo; trait anxiety score. Thus, the trait anxiety group was a categorical, nominal variable with two levels (low anxiety/high anxiety).\u003c/p\u003e\u003cp\u003eThe first dependent variable of the study was the participants\u0026rsquo; overall METV score, which was calculated as the proportion of correct answers given to the test on the first try. Thus, it is a numeric variable measured on the ratio level. The theoretical minimal and maximal scores are 0 and 1. The second dependent variable was the participants\u0026rsquo; score on angry faces, which is also calculated as the proportion of correct answers given on the first try. It is also a numeric variable measured on the ratio level, with the theoretical score ranging from 0 to 1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Participants and Recruitment\u003c/h2\u003e\u003cp\u003eParticipant recruitment was done through Prolific. Initial screening done by the platform allowed us to exclude participants who currently used psychotropic medication, as research demonstrated that it has an effect on emotion recognition\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We also used the pre-screening to create a diverse sample in terms of their self-reported anxiety. Due to the limited nature of the way in which it was measured on the platform (a simple yes/no answer), this was not taken into account when creating participant groups in the study, but instead their scores on the STAI were used for that purpose. Furthermore, as these pre-screening questionnaires were completed an unknown time before recruitment into the study, medication use was checked in the study survey. Only participants over 18 were recruited for the study, they had to be fluent in English, and they were reimbursed \u0026pound;4.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e5.2.1. Sample size determination\u003c/h2\u003e\u003cp\u003eIt has been shown in a meta-study that the average effect size for the effect of anxiety on facial expression recognition is \u003cem\u003ed\u003c/em\u003e = -0.35\u003csup\u003e5\u003c/sup\u003e. Using the G*Power software, we determined that the sample size needed to achieve this effect size (with the assumption that this effect size will be similar for micro expression recognition) at 0.95 power is 428. Therefore, the aimed sample size was 450 (225 per group), so that there would be a sufficient number of participants after outliers and non-completers are removed from the sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e5.2.2. Withdrawal of participants\u003c/h2\u003e\u003cp\u003eParticipants were informed that they were able to withdraw from the study at any time by leaving the study webpage. Participants who opted out before completing the survey did not receive a reimbursement.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Measures and Materials\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e5.3.1. Micro Expressions Training Videos (METV)\u003c/h2\u003e\u003cp\u003eThe METV \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e measured micro expression recognition ability based on the detection of micro expressions on videos. It is based on the Facial Action Coding System rules (Ekman \u0026amp; Friesen, 1978). There were 20 videos, each showing a male or female white person presenting a single micro expression, for 0.5 seconds or shorter. Since each emotion can be demonstrated by a different number of micro expressions\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, the emotions were not represented in equal numbers of stimuli \u0026ndash; there were 4 micro expressions of anger, 4 of sadness, 3 of fear, 3 of surprise, 2 of disgust, 2 of contempt, 1 of happiness, and 1 neutral face. Once the emotion is presented, the participants answer by selecting one of the eight possible answers (7 emotions\u0026thinsp;+\u0026thinsp;neutral). Completing the test took about 10 minutes. While additional answers (up to 3) are allowed to participants if they make a mistake on the first try, only the proportion of correct answers on the first try was used as the dependent variable in the present study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2. The State-Trait Anxiety Inventory (STAI)\u003c/h2\u003e\u003cp\u003eThe State-Trait Anxiety Inventory (STAI) is one of the most commonly used measures of trait and state anxiety\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. It consists of 40 items, 20 measuring state anxiety (example: \u0026ldquo;I am worried\u0026rdquo;) and 20 measuring trait anxiety (example: \u0026ldquo;I worry too much over something that really doesn\u0026rsquo;t matter\u0026rdquo;).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e5.3.3. The Beck Depression Inventory-II (BDI-II)\u003c/h2\u003e\u003cp\u003eThe Beck Depression Inventory-II (BDI-II) was used to measure depressive symptoms. The questionnaire contains 21 items, on which the frequency of experiencing different symptoms of depression within the past two weeks are indicated on a Likert-type scale ranging from 0 to 3\u003csup\u003e68\u003c/sup\u003e. One item (Q9 \u0026ndash; suicidal ideation) was removed from the questionnaire in in response to local ethics review. The scale has excellent reliability (average Cronbach\u0026rsquo;s α in a meta-study 0.9) and validity\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e5.3.4. Sociodemographic Data\u003c/h2\u003e\u003cp\u003eSociodemographic factors were also measured, which included age in years, gender (male, female, other), and highest level of education.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Procedure\u003c/h2\u003e\u003cp\u003eThe study involved a single online session lasting approximately 20 minutes. After filling in the demographic questions, participants were administered the State-Trait Anxiety Inventory and the Beck Depression Inventory-II. Then, the participants went through 20 videos with the force choice option on the METV test under a separate link. In each part of the survey, participants were required to share their email address and name. These were used in order to merge the data from the different measures, and then the data was anonymized.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.5. Data Analysis\u003c/h2\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e5.5.1. Data screening\u003c/h2\u003e\u003cp\u003eTwo data screening criteria were used in order to clean the data. First, extreme outliers \u0026ndash; defined as those participants whose METV and/or trait STAI test scores lie more than 3 times the interquartile range below the first quartile or above the third quartile \u0026ndash; were removed from the dataset. Less extreme outliers \u0026ndash; defined as those participants whose METV and/or trait STAI test scores lie between 1.5 and 3 times the interquartile range below the first quartile or above the third quartile \u0026ndash; were included in primary analyses but their influence was investigated through sensitivity analyses that exclude them. Participants that did not comply with the basic instructions of the study (e.g., used Chrome browsers or smart phones, which are not compatible with the METV), were removed from the dataset. Furthermore, for all statistical analyses conducted for this study, all relevant assumptions (such as normality of data and homoscedasticity of the residuals) were verified prior to analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e5.5.2. Analysis\u003c/h2\u003e\u003cp\u003eFor the data analyses, we allocated people into two equal groups (low anxiety, high anxiety) based on the trait anxiety sub-scale of the State-Trait Anxiety Inventory (STAI). Similarly, two equal groups were created based on the BDI-II scores (low mood, control). Both separations of participants into groups were done by using the median value. For trait anxiety, the median value was 44. As 55% of participants had a score of 44 or less, we have performed all subsequent analyses with both 44 and 43 as the cut-off value, in order to prevent any bias due to more participants being in one of the two groups. There were no differences in results, and the presented results were done with low anxiety group containing participants who scored 44 and less, while the high anxiety group contained participants who scores 45 and more. The median score on BDI-II was 10, with 52.2% of participants scoring 10 or less on BDI-II. Since 49.4% of participants scored 9 or less, we classified participants scoring 10 or more into the low mood group, while those with scores 9 and below were classified as the control group. This was done in order to create groups as equal in size as possible. Regression analyses were utilized to check for the relationship of trait anxiety (high, low) with the overall METV score, as well as the anger recognition score. A series of Ordinary Least Squares (OLS) linear regression analyses was conducted on the obtained study data. To test H1 and obtain the primary outcome of the study, we compared the METV test scores between the two groups made based on trait anxiety. For H2, the two anxiety groups were compared on the accuracy in detecting angry faces. In the complete models for both hypotheses, the results were adjusted for low mood and the demographic control variables age, sex, and level of education.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe received no funding for the present study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank FACS Certified Coders, Thomas Nichols from the USA and Tain\u0026atilde; Veloso from Portugal for their help with the verification of micro facial expressions shown in the METV. This study was supported by the National Institute for Health and Care Research Bristol Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKasia Wezowski: Conceptualization, Methodology, Data curation, Formal Analysis, Writing- Original draft preparation, Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIan S. Penton-Voak: Supervision, Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy data and code used for analysis is available on OSF: https://osf.io/mqud7/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were provided informed consent during the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKasia Wezowski is the co-founder of the Center for Body Language where she gives training and coaching.\u003c/p\u003e\n\u003cp\u003eIan S. Penton-Voak declares no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEkman, P. 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(New Vision, 2012).\u003c/li\u003e\n\u003cli\u003eSpielberg, C., Gorsuch, R., Lushene, R., Vagg, P. \u0026amp; Jacobs, G. Manual for the State-Trait Anxiety Inventory (STAI Form Y). (1983).\u003c/li\u003e\n\u003cli\u003eBeck, A. T., Steer, R. A. \u0026amp; Brown, G. Beck depression inventory\u0026ndash;II. \u003cem\u003ePsychological assessment\u003c/em\u003e (1996).\u003c/li\u003e\n\u003cli\u003eWang, Y.-P. \u0026amp; Gorenstein, C. Psychometric properties of the Beck Depression Inventory-II: a comprehensive review. \u003cem\u003eRev. Bras. Psiquiatr.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 416\u0026ndash;431 (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7149123/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7149123/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious research has demonstrated that facial expression recognition, an invaluable social skill, may be impaired amongst people suffering from anxiety. Research surrounding this relationship is equivocal and little attention has been given to the effects of anxiety on the recognition of micro expressions. Thus, the present study investigated this relationship. Based on previous research, we expected that participants with high trait anxiety will show a) poorer overall micro expression recognition and b) better angry face recognition. 431 participants completed measures of trait and state anxiety, depression, micro facial expression recognition and indicated demographic information. The results of the study supported neither of the two hypotheses. Combined with previous findings, these results indicate that trait anxiety does not have a robust effect on either general emotion recognition or anger recognition. Previous positive findings may potentially be a consequence of unaccounted effects of low mood or age. On the other hand, the results did show effects of low mood on improved overall micro expression recognition scores and sad face recognition, and bias towards recognizing neutral faces as sad. These findings may be attributed to biases arising from the effects of mood congruence.\u003c/p\u003e","manuscriptTitle":"Low mood, not anxiety, connected with micro facial expression recognition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 08:50:08","doi":"10.21203/rs.3.rs-7149123/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T11:48:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T15:23:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T14:07:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T07:40:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T08:25:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78804526880990833371968614493961605023","date":"2025-08-20T10:11:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201190027066393018981353469268488428698","date":"2025-08-20T05:29:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76789807940909068439070932434070249269","date":"2025-08-19T13:33:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274929903802616808798124774440807597324","date":"2025-08-19T12:13:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T11:51:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-23T11:46:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-21T05:59:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T12:18:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-17T12:31:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28299517-34c9-496c-84aa-2f95e7d8a4f3","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53702368,"name":"Health sciences/Health care"},{"id":53702369,"name":"Biological sciences/Psychology"},{"id":53702370,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-12-01T16:04:23+00:00","versionOfRecord":{"articleIdentity":"rs-7149123","link":"https://doi.org/10.1038/s41598-025-26921-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-28 15:58:29","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-08-27 08:50:08","video":"","vorDoi":"10.1038/s41598-025-26921-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-26921-1","workflowStages":[]},"version":"v1","identity":"rs-7149123","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7149123","identity":"rs-7149123","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}