From bugs to sickness: disgust evaluation of ancestral, modern, and pandemic threats

From bugs to sickness: disgust evaluation of ancestral, modern, and pandemic threats | 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 Research Article From bugs to sickness: disgust evaluation of ancestral, modern, and pandemic threats Janovcová Markéta, Polák Jakub, Anna Končická, Aleksandra Chomik, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5960947/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Sep, 2025 Read the published version in Evolutionary Psychological Science → Version 1 posted 9 You are reading this latest preprint version Abstract Disgust is a fundamental emotion that evolved to protect organisms from pathogens and toxins, shaping behaviours critical for survival. This study explores how ancestral, modern, and pandemic-related visual stimuli elicit disgust. Specifically, our goal was to validate the categorisation of disgusting stimuli, evaluate the intensity of emotional responses, and determine the contribution of individual differences. A sample of 262 participants from diverse educational and professional backgrounds ranked 60 visual stimuli, including ancestral (spoiled food, bugs), modern (toxic substances, radioactivity), and pandemic-related (sneezing, masks, hospitals) categories, on perceived disgust (pictures of leaves were used as controls). They also completed assessments of pathogen, core, and moral disgust, along with pandemic-related behaviours and stress. Results revealed distinct clusters of disgust stimuli, with spoiled food evoking the strongest repulsion, while modern threats, such as pollution and radioactivity, elicited weaker disgust. Pandemic-related stimuli formed a unique category, with visible infection cues (e.g., sneezing) triggering stronger disgust than abstract cues like masks or hospital environments. The findings highlight the evolutionary roots of disgust and its adaptation to modern contexts. They underscore the need to consider cultural, individual, and situational factors in public health strategies and interventions targeting hygiene and disease prevention behaviours. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Proximate and ultimate mechanisms of disgust Early humans faced significant threats from pathogens, contaminated food, and waste products leading to disgust as an adaptive response to such stimuli (Hlay et al., 2021). The emotion of disgust is therefore considered one of the most ancient emotional systems developed to promote the survival of organisms by avoiding intoxication or disease (Curtis et al., 2004; Schaller & Park, 2011). Emerging from evolutionary adaptations, disgust has developed into the behavioural immune system, a complex psychological and physiological construct affecting various human behaviour domains (Thiebaut et al., 2021). For instance, the universal facial expression associated with disgust includes a wrinkling of the nose and a down-turning of the mouth corners, which signals revulsion and a desire to avoid the source of disgust. Additionally, feelings of nausea and fear of contagion are common responses that further reinforce avoidance behaviour towards perceived threats (Davey, 2011). Recent findings further corroborate this view, showing that disgust sensitivity correlates with behaviours aimed at reducing pathogen exposure, such as enhanced hygiene practices (Stevenson et al., 2021). Evidence of its evolutionary roots can be traced before primates, back to early vertebrates, with some scholars proposing that even basic forms of aversion to harmful substances existed in ancestral species over 500 million years ago. Itoigawa and colleagues (2024) have reported that bitter taste receptors, which help organisms detect potentially toxic compounds, are present in a wide range of vertebrates, including sharks and rays. Among primates, disgust-like behaviours are well-documented, including avoidance of faecal matter, spoiled food, and contaminated objects, suggesting that this emotion emerged long before the divergence of humans and other primates during the Miocene epoch, approximately 23–5 million years ago (Sarabian et al., 2018). Studies in non-human primates, such as chimpanzees and bonobos, show advanced contamination sensitivity and nuanced avoidance behaviours that align with the parasite avoidance theory, highlighting a sophisticated evolutionary mechanism to minimise exposure to infectious agents (Sarabian et al., 2017, 2023; reviewed in Schwambergová et al., 2023). Additionally, the communicative role of disgust, such as through facial expressions that signal contamination risks to conspecifics, appears to be a shared trait among primates and underscores its adaptive value in social species (Preuschoft & van Hooff, 1995). Disgust is associated with distinct physiological responses, including nausea, heart rate deceleration, and changes in skin conductance (Stark et al., 2005), all commonly linked to parasympathetic nervous system activation (Cisler et al., 2009). However, findings from physiological studies vary. In her review, Kreibig (2010) identifies an alternative pattern involving partial sympathetic-parasympathetic co-activation, characterised by heart rate acceleration, faster breathing, and reduced inspiration - especially in response to contamination stimuli, as opposed to blood and injury. De Jong and colleagues (2011) investigated the autonomic nervous system responses to a disgust-inducing video and their relationship with individual differences in disgust propensity and sensitivity. Their study found that disgust elicited increased parasympathetic activity in both cardiac and digestive systems, alongside heightened sympathetic activation in the cardiac system. Interestingly, these physiological responses were not correlated with participants' self-reported disgust levels or their habitual disgust propensity or sensitivity, indicating that subjective experiences and physiological responses to disgust may operate independently. While debate continues about the role of parasympathetic activation in disgust responses, it is evident that disgust is a weaker sympathetic activator than fear (Simon et al., 2017). Unlike fear, disgust engages a distinct neural network that includes the anterior insular cortex, basal ganglia, ventrolateral and medial prefrontal cortex, anterior temporal cortex, and visual cortex (Wicker et al., 2003; Calder et al., 2007; Klucken et al., 2012; Koenigs, 2013; Gan et al., 2024). Interestingly, distinct spatiotemporal patterns of neural activity have been found for core and moral disgust (Yang et al., 2013). Neuroimaging studies consistently implicate the insula as a central hub in disgust processing, reflecting its role in integrating visceral and emotional signals (Wicker et al., 2003; Wright et al., 2004; cf. Schienle et al., 2002) and the signal of neural activity appears 300 ms after stimulus onset supporting the notion of disgust as a rapid, automatic response (Krolak-Salmon et al., 2003). Theories of disgust According to early research, disgust has oral origins and evolved primarily to prevent ingestion of harmful substances, such as spoiled food, which could lead to illness or even death (Rozin et al., 2009; for review see Curtis, 2011). Rozin and Fallon (1987) first proposed the concept of "core disgust" as a mechanism to guard the organism against contamination and maintain physical and symbolic boundaries. The three-domain model proposed by Tybur et al. (2009) has further refined this understanding, providing a structural framework for exploring the domains of disgust in relation to evolutionary pressures. In their model, disgust is understood to operate across three primary domains: pathogen disgust, which targets contamination risks; sexual disgust, which serves to avoid fitness-compromising reproductive choices; and moral disgust, which regulates social behaviours to maintain group harmony (reviewed in Chapman & Anderson, 2013). Each domain demonstrates unique triggers and consequences. For example, sexual disgust is particularly sensitive to cues indicating potential genetic incompatibility or disease, whereas moral disgust often responds to violations of fairness or harm (Tybur et al., 2013). While these domains are universal, cultural and environmental variabilities influence their expression (Haidt et al., 1997). For instance, pathogen disgust may manifest more strongly in societies with a high prevalence of infectious diseases. These societies also tend to be put a greater emphasis on social conformity, strong family ties, religiosity and traditional values (Fincher & Thornhill, 2012), which can be seen as a collective strategy to mitigate disease risk (Jędryczka, 2022). This may consequently shift the cultural orientation to collectivism, a phenomenon explained by the parasite-stress theory of values and sociality, suggesting that recurrent infectious diseases drive collectivist behaviours to mitigate risks (Cepon-Robins et al., 2021; Hlay et al., 2021; reviewed in Shapouri, 2023). However, recent findings by Shapouri and Rafiee (2024) challenge this hypothesis, reporting that ecological threats such as parasite stress (operationalized as the frequency of epidemics) and natural disasters could not significantly predict collectivism scores in a cross-national analysis of 188 countries. The results suggest that previous findings linking ecological threats to collectivism may be due to small, non-representative samples. As already mentioned, disgust functions not only as a disease-avoidance mechanism triggered in the presence of pathogens but also extends to social and moral domains, reflecting deeper cognitive processes that underpin judgments about fairness, harm, and purity (Haidt, 2001) and govern our interactions with others (Giner-Sorolla et al., 2018). For example, moral disgust has been shown to intensify punitive attitudes toward individuals who violate societal norms, reflecting its role in enforcing social order (Konishi et al., 2017; Oaten et al., 2018), which can be interpreted as an adaptive response to potential disease threats (Nussinson et al., 2018). This aversion can manifest in various social behaviours, including exclusionary attitudes towards outgroups – such as immigrants (Aarøe et al., 2017) or gays (Inbar et al., 2009b) – and increased conformity to social norms, particularly in environments perceived as high-risk for disease transmission (Kusche & Barker, 2019). Disgust is, therefore, critical in intergroup relations, contributing to prejudice and dehumanisation in historical and contemporary contexts (Kteily et al., 2015). Furthermore, studies have shown that individuals with heightened disgust sensitivity are more likely to hold conservative political views, as these often correlate with a preference for traditional norms and a wariness of change (Inbar et al., 2009a; Tybur et al., 2016; Rosenfeld & Tomiyama, 2021; O’Shea et al., 2022). This relationship suggests that the behavioural immune system may influence political ideologies by shaping perceptions of social threats and guiding responses to those threats. Ancestral, modern, and pandemic-related disgust elicitors Ancestral disgust elicitors, such as body waste, decaying organic matter, and infected wounds, evoke strong disgust reactions because they are directly tied to disease transmission risks (Curtis et al., 2004, 2011; Oaten et al., 2009). These elicitors engage sensory cues like foul odours or grotesque visuals that signal immediate contamination threats. In contrast, modern disgust elicitors, including chemical pollutants, industrial waste, and radioactivity, often trigger weaker disgust responses but are more likely to elicit fear and anger (Peléšková et al., 2024). These modern threats lack the visceral sensory characteristics of ancestral hazards and instead rely on abstract knowledge and higher cognitive processes to recognise their danger (Hacquin et al., 2022; Lanondová et al., 2025). Shapouri and colleagues (2023) examined affective responses to natural and technological disasters, showing that natural threats evoke primal fear, while technological disasters elicit complex emotions such as anxiety. In contrast, Landová et al. (2025) claim that the role of anxiety in response to various types of threats, including the modern ones, is relatively weak. Disgust sensitivity has been shown to increase during pandemics, likely as part of the behavioural immune system’s adaptive response to heightened disease threats, highlighting its role in motivating pathogen-avoidance behaviours (Makhanova & Shepherd, 2020; Kaňková et al., 2023). For example, individuals with higher disgust sensitivity reported greater adherence to hygiene measures such as frequent handwashing, mask-wearing, and social distancing during the COVID-19 pandemic (Stevenson et al., 2021). Beyond COVID-19, research on other diseases, such as Ebola, highlights the role of disgust in shaping social behaviour, including the avoidance of infected individuals and stigmatisation of outgroups perceived as disease carriers (Jalloh et al., 2017). Similar patterns were observed during the 2009 H1N1 influenza pandemic, where disgust responses correlated with protective behaviours and increased prejudice toward groups associated with the disease's origin (see the review by van Leeuwen et al., 2023). Recently, the COVID-19 pandemic has presented a unique challenge for human perception and emotional response, as the threat was invisible to the naked eye and primarily inferred from indirect cues. These modern signals, such as masks, sanitisers, news of viral spread, or overcrowded spaces, often evoke responses linked to ancestral disgust mechanisms. In this study, we aim to explore how ancestral, modern, and pandemic-related repulsive stimuli, presented as visual images, are subjectively evaluated on perceived disgust. Previously, it has been shown that pandemic-related threats presented as written scenarios align more closely with ancestral dangers (Peléšková et al., 2024; Landová et al., 2025). However, whether this pattern holds for visual stimuli, which engage different cognitive and emotional processes, remains uncertain. Understanding how disgust operates in the modern world and during crises like the COVID-19 pandemic is critical for uncovering its broader psychological and behavioural implications. Aims of study The present study seeks to advance our understanding of disgust by addressing three key objectives: 1. Categorization of Disgust Stimuli This study aims to validate and refine the categorisation of disgust-eliciting stimuli into predefined domains - ancestral (e.g., spoiled food, parasitic invertebrates, and visible parasites), modern (e.g., toxins, radioactivity, polluted environments, and pandemic-related cues such as fear of hospitalisation, death, masks, or sneezing). By examining the distinctiveness and coherence of these categories, we aim to establish whether they represent meaningful and separate dimensions of disgust. A central question guiding this investigation is whether pandemic-related threats, including invisible pathogens and fear-inducing modern scenarios, align more closely with modern disgust elicitors or exhibit similarities to ancestral categories. 2. Intensity of Emotional Responses to Different Stimuli This study will evaluate the relative intensity of emotional responses elicited by ancestral versus modern disgust stimuli. Building on theoretical predictions suggesting the heightened salience of ancestral threats due to their evolutionary significance, we will examine whether ancestral stimuli consistently evoke stronger emotional reactions than modern threats. Furthermore, we will address the role of pandemic-related threats by investigating how their emotional intensity compares to those of ancestral and modern categories. 3. Contribution of Individual Sensitivities to Emotional Responses This study will assess the role of individual differences and threat-specific sensitivities in shaping the perception and evaluation of visual representations of ancestral, modern, and pandemic-related threats. We aim to determine how variability in individual sensitivity modulates emotional reactions across these domains, shedding light on the interplay between personal characteristics and threat-specific disgust responses. Materials and methods Participants The testing procedure was completed by 262 respondents (181 women, 81 men). Before starting the experiment, we calculated the appropriate sample size using a priori power analysis. For this experiment, 105 respondents would be needed for large effects (effect size p = 0.40) and 233 respondents for medium effects (p = 0.25). Thus, our sample size is sufficient. All respondents were from Central Europe and completed the experiment in Czech or English. They were recruited from students at several universities, including a university of the third age, and their relatives. We also reached out to former participants from our previous projects. We could obtain participants of different age groups (age 18–79, mean 31.6, SD = 15.8) and educational backgrounds: biological (112 respondents), social science (60), general (29), technical (26), economic (24) and medical (11). Most of them have obtained a university degree (196 respondents), and 66 participants have completed secondary education. A complete list of respondents with their detailed socio-demographic characteristics is provided in Table S1 . Stimuli Photographs of disgusting objects/situations were used as stimuli in this project. The testing set contained a total of 60 pictures: 40 pictures representing four different categories of disgusting objects and 20 control pictures of leaves (10 images of a single leaf on a grey background and 10 images of multiple leaves in a natural context). These controls were used successfully in previous projects (Landová et al., 2020, 2023). The four categories of disgusting objects/situations containing 10 pictures each were: 1) spoiled food, 2) invertebrates and parasites, 3) polluted environments and modern toxic substances, and 4) images related to an airborne disease pandemic (hospital shots, people sneezing, people wearing masks). The images for the testing set were selected from published databases (DIRTI: Haberkamp et al., 2017; IAPS: Lang & Bradley, 2007; SMID: Crone et al., 2008; EmoMadrid: Carretié et al., 2019), internet sources (Pixabay, Wikimedia Commons, etc.), or taken by the authors and collaborators. A complete list of authors and photo sources is available in Table S2. The original images have been modified to print format, size 10x15 cm, 300 DPI; no other modifications have been made. The printed set of photographs was subsequently used for the testing procedure (for a sample of stimuli see Fig. 1 ). Assessment In addition to socio-demographic data (age, gender, level and field of education), an assessment battery was used to determine the respondents’ emotional sensitivity. Two commonly used questionnaires, the Three Domains of Disgust Scale (TDDS; Tybur et al., 2009; only the pathogen and more disgust subscale were used, the sexual disgust subscale was omitted as it was not relevant to our study) and the Disgust Scale - Revised, (DS-R: Haidt et al., 1994, modified by Olatunji et al., 2007; Czech translation: Polák et al., 2019) were selected to measure disgust propensity. Furthermore, a short version of the Spider Questionnaire (SPQ-12; original scale by Klorman et al., 1974; short version Polák et al., 2020) was used. Although the SPQ-12 focuses only on spiders, the total score is a good predictor of people's attitude towards small invertebrates, which are often considered disgusting (Landová et al., 2021). Regarding airborne disease pandemics, several questionnaires were selected to cover the period of the COVID-19 pandemic: the Coronavirus Safety Behaviours Scale (CSBS; Knowles & Olatunji, 2021; originally developed in the context of the Ebola and H1N1 epidemic by Wheaton et al., 2012 and Blakey et al., 2015), assessing behaviours that people performed during the pandemic, and the COVID Stress Scales (C-19SS; Taylor et al., 2020), which assesses fears, problems, and control behaviours during the pandemic. All questionnaires were administered in Czech or English. Procedure The research was conducted between January and October 2024, testing was conducted in the personal presence of the experimenters. First, each respondent signed an informed consent form and completed the socio-demographic information. The testing picture set was always laid on a well-lighted table for each respondent so he/she could see all the pictures clearly. The order of the pictures was randomised, and only the categories were considered, i.e. all the pictures from the same category would not be directly next to each other. The respondent was then asked to take a good look at the whole set and then to sort all the pictures according to perceived disgust into one packet so that the picture evoking the most disgust was on the top and the picture evoking the least disgust was on the bottom. No time limit was given to the respondents for the rating; on average, it took about 15 minutes to sort the pictures. Finally, each respondent completed the assessment battery. The complete data are available in Table S1 . Statistical analyses To determine the appropriate sample size, we used an a priori power analysis computed in G Power (Erdfelder et al., 1996). Kendall's Coefficient of Concordance (W) calculated using the R program, package irr (R Core Team, 2019; Gamer et al., 2019) was used to determine agreement among respondents on the ranking of stimuli by perceived disgust. Before further analyses, image-ordering data were square-root arcsine transformed. The mean for each stimulus was then calculated, and the final rank order of the ranked images was generated according to the mean rank. The average rating of the stimuli was used to compute linear models (LMs), the explanatory variable being the stimulus category. In the first analysis, the original categorisation (spoiled food, disgusting animals, polluted and toxic environment, airborne disease pandemic and its consequences, and two categories of controls) was used; in the second analysis, in addition, the airborne disease pandemic category was divided into three separate groups (images from hospitals, sneezing, people wearing masks). For a more detailed analysis of the relationships between groups, a post hoc Tukey test was performed (program R, package lsmeans; Lenth, 2016). Raw image sorting data were used in further analyses. To visualise the data structure and confirm the meaningfulness of the created categories, a cluster analysis was used, the distance matrix was calculated using Pearson correlations among ratings, and tree diagrams were built using Ward's method. Furthermore, a factor analysis was performed to show the detailed structure of the created image groups, the principal component extraction and varimax normalised rotation methods were used, and a parallel analysis was used to determine the number of factors. The cluster, factor, and parallel analysis were calculated using Statistica 10 (Stat Soft, 2011). A multivariate redundancy analysis (RDA) was used to analyse the effect of individual sensitivity on the ratings of disgusting images. The raw data of the ranking of images by individual respondents was the response variable, and the explanatory variables were the final scores from the assessments and the respondents’ characteristics (age, gender, education). The respondents' education was categorised by level (high school, university) and field of study (general, biological, medical, economic, social science, and technical). The statistical significance of the gradients was confirmed by permutation tests (number of permutations = 20000). Calculations were performed with R, MASS (Venables & Ripley, 2002) and vegan (Oksanen et al., 2020) packages. Results Agreement among respondents In total, 262 respondents rated 60 images according to perceived disgust using the stimulus ordering method. Agreement among respondents in ranking the whole set was very high, Kendall's W = 0.76, p < 0.0001. The complete picture set contained 20 control stimuli (leaves) that elicited the least disgust. When the control stimuli were removed in a subsequent analysis, the resulting inter-respondent agreement for the test images remained sufficiently high, W = 0.345, p < 0.0001. Validation and categorisation of disgust stimuli We first visualised the data using cluster analysis to test the meaningfulness and consistency of the proposed groups. The tree diagram showed that the selected image categories formed separated consistent groups, i.e. the images appropriately represented the selected stimulus categories (see Fig. 2 ). The exploratory factor analysis, preceded by the parallel analysis, determined five factors which explained 63.6% of the total variability. Factor 1 (24.7% of the explained variability) corresponded best with images depicting spoiled food. In comparison, factor 2 (17% of the explained variability) included disgusting animals on the one hand and polluted environment and toxicity on the other. The group of stimuli depicting a pandemic consisted of three separate factors: factor 3 (11.1% of the explained variability) corresponded to images depicting the consequences of illness (photos of a hospital environment and death), factor 4 (6.2%) was associated with a potential risk of airborne disease pandemic (people wearing masks), and factor 5 (4. 6%) pertained to the risk of direct infection (people sneezing) (see Table 1 ). The factor analysis showed that although the pandemic-related images were well distinguishable as a category from the other categories (animals, spoiled food, and polluted environment) in terms of disgust, the internal structure of this category was not consistent and was composed of three main subgroups. Table 1 Factor analysis results. Results for five factors are shown; the number of factors was determined by parallel analysis. The most significant values for each stimulus are highlighted in bold. Stimulus codes correspond to the original categories, Animal (disgusting animals), Food (spoiled food), Toxic (polluted environment and toxicity), Disease (airborne disease pandemics; three subcategories of stimuli considered here). Stimulus code Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Animal_01 0.120 -0.750 0.237 0.023 0.169 Animal_02 -0.014 -0.644 0.059 0.212 -0.030 Animal_03 0.153 -0.719 0.176 0.049 0.211 Animal_04 0.103 -0.621 0.149 0.018 0.085 Animal_05 0.079 -0.681 0.283 -0.005 0.243 Animal_06 0.136 -0.660 0.179 0.035 -0.075 Animal_07 0.166 -0.625 0.159 0.114 -0.009 Animal_08 0.127 -0.709 0.258 0.089 0.104 Animal_09 0.111 -0.548 0.005 0.106 -0.055 Animal_10 0.182 -0.713 0.138 0.060 0.021 Food_01 -0.800 0.105 0.114 -0.059 0.058 Food_02 -0.843 -0.009 0.087 0.062 0.011 Food_03 -0.540 -0.083 0.032 0.099 0.154 Food_04 -0.878 0.059 0.128 0.096 0.018 Food_05 -0.832 0.095 0.120 -0.047 0.032 Food_06 -0.806 0.044 0.169 0.015 -0.086 Food_07 -0.798 -0.050 0.107 0.040 0.016 Food_08 -0.823 0.047 0.175 -0.031 0.122 Food_09 -0.754 0.017 0.192 0.058 -0.030 Food_10 -0.840 0.047 0.158 0.059 0.013 Toxic_01 0.349 0.715 0.150 0.155 -0.052 Toxic_02 0.462 0.468 0.040 -0.044 0.216 Toxic_03 0.384 0.681 0.237 0.103 -0.034 Toxic_04 0.204 0.368 0.131 0.229 0.243 Toxic_05 0.502 0.566 0.077 -0.029 0.143 Toxic_06 0.396 0.516 0.131 -0.106 0.144 Toxic_07 0.273 0.656 0.189 0.156 -0.059 Toxic_08 0.356 0.428 0.190 -0.074 0.278 Toxic_09 0.342 0.559 0.086 0.132 -0.088 Toxic_10 0.495 0.542 0.044 0.125 0.083 Disease_01(hospital) 0.207 0.089 -0.856 -0.072 -0.150 Disease_02(hospital) 0.186 0.095 -0.895 -0.035 -0.090 Disease_03(sneezing) 0.130 0.156 -0.062 -0.108 -0.885 Disease_04(masks) 0.102 0.044 -0.127 -0.901 -0.085 Disease_05(sneezing) 0.113 0.114 -0.088 -0.168 -0.897 Disease_06(masks) 0.071 0.087 -0.084 - 0.906 -0.077 Disease_07(hospital) 0.259 0.117 -0.853 -0.100 -0.026 Disease_08(masks) 0.085 0.087 -0.102 -0.907 -0.105 Disease_09(hospital) 0.228 0.103 -0.873 -0.155 -0.125 Disease_10(hospital) 0.207 0.177 -0.780 -0.060 0.128 Intensity of emotional responses to different stimuli Next, we focused on the intensity of perceived disgust for each category. We used a linear model (LM) for the original stimulus categories (spoiled food, animals, toxicity and pollution, disease, and control stimuli). The results showed that the stimulus category affected the rating of perceived disgust (p < 0.0001) and confirmed that disgust images differed conclusively from the control stimuli (leaves). Pictures of spoiled food, one of the categories of ancestral disgust elicitors, evoked the greatest disgust in the respondents. Contrarily, as the other ancestral category, disgusting animals resembled in their mean ratings the pictures of polluted environments and toxicity, i.e., the modern stimuli. The weakest average response (except for the control) was found for the pictures depicting a pandemic situation and its consequences (see Fig. 3 A). Based on the factor analysis outcomes, we created a second linear model in which the pandemic-related category was divided into three subgroups. The results again confirmed the effect of these groups on ratings of perceived disgust (p < 0.0001) and the distinctiveness of all the categories from controls (see Table S3 for complete results of both models). Of the three categories of pandemic threat, images depicting people sneezing, i.e. the visible sign of infection, were rated as the most disgusting. The average rating corresponded to the disgust level for polluted environments (modern disgust) and small animals (ancestral disgust). Pictures of hospital environments, particularly the risk of an airborne disease pandemic represented by people wearing masks, elicited the weakest disgust response (see Fig. 3 B). For a detailed analysis of the relationships between categories, we used a post hoc Tukey test. The analysis confirmed that all categories differed from the controls. The two groups of control stimuli (a leaf on a grey background vs leaves on a natural background) did not differ from each other (p = 0.1172). Within the ancestral threats, there was no conclusive difference between the disgusting animals and spoiled food (p = 0.2517). Modern threats represented by polluted and toxic environments differed from the ancestral spoiled food group (p = 0.0142) but were not conclusively different from animals (p = 0.5554). Images depicting the threat of a pandemic were significantly different from the spoiled food (p < 0.0001), animals (p = 0.0001), and polluted environments (p = 0.0026), thus forming a separate group (See Table S4 for detailed results). Contribution of individual sensitivities to emotional responses Multivariate redundancy analysis (RDA) was used to analyse the effect of respondents' characteristics (age, gender, level, and field of education) and their sensitivity (assessment battery) on ratings of perceived disgust. The final model explained 12.4% of the total variability. The SPQ-12 (F = 10.512, p < 0.0001) and moral disgust subscale of the TDDS (F = 4.227, p = 0.0004) had the most significant effect on the evaluation of disgust. Furthermore, the respondents' field of education (F = 2.295, p < 0.0001), pathogen disgust subscale of the TDDS (F = 2.297, p = 0.0159), core disgust subscale of the DS-R (F = 2.238, p = 0.0182), level of stress evoked by the COVID-19 pandemic measured by the C-19SS (F = 2.644, p = 0.0066), and the respondent’s age (F = 2.046, p = 0.0282) also significantly affected the disgust evaluation. The first axis (RDA1) explained 6.1% of the total variability. It was best predicted by the relationship to invertebrates (SPQ-12), sensitivity to disgust at the ancestral level (TDDS pathogen disgust DS-R core disgust), and biology or humanities fields of education. The second axis (RDA2) explained 2.9% of the total variability. It was best predicted by the respondents' moral attitude (TDDS moral disgust), age, the stress of COVID-19 (C-19SS), and non-biological fields of education (technical, economic). A visualisation of the results is shown in Fig. 4 , and detailed results of the RDA analysis are shown in Table 5. Discussion The present study examined the subjective evaluation of disgust in response to ancestral, modern, and pandemic threats. The validation of stimulus categories through cluster and factor analyses revealed meaningful distinctions between the ancestral and contemporary threat stimuli. The clear grouping of ancestral (spoiled food and disgusting animals) and modern threats (polluted environments and pandemic-related stimuli) aligns with theoretical expectations. Disgust evolved primarily as a pathogen avoidance mechanism (Curtis et al., 2011), with ancestral elicitors such as spoiled food and small animals posing direct and immediate survival risks (Davey, 2004, 2011). The COVID-19 pandemic offered an unprecedented context for evaluating disgust responses to disease-related stimuli. The categorisation and evaluation of pandemic-related stimuli represent a key contribution of this study as it extends the findings of Peléšková et al. (2024) by highlighting the internal heterogeneity of pandemic-related stimuli. While the previous research focused on overall emotional responses, our factor analysis revealed three distinct subcategories within pandemic-related disgust: 1) visible signs of infection (e.g., sneezing), 2) potential airborne disease risks (e.g., people wearing masks), and 3) consequences of illness (e.g., hospital environments). Such a distinction offers novel insights emphasising the need for a more granular approach to understanding pandemic-related responses. From an evolutionary perspective, disgust serves as a protective mechanism to minimise contact with harmful pathogens and toxins. The study confirms that certain ancestral stimuli, particularly spoiled food, elicit the strongest disgust reactions, consistent with previous research highlighting the role of disgust in avoiding pathogens linked to foodborne illnesses (Curtis et al., 2004). This also aligns with prior research (Peléšková et al., 2024; Landová et al., 2025) showing that ancient threats like body waste products and worms trigger significant disgust responses. Thus, the pronounced disgust elicited by spoiled food reaffirms its status as a prototypical ancestral disgust elicitor due to its high pathogen load (Chapman et al., 2009; Tybur et al., 2013). According to Rozin and Fallon (1987), disgust originated as a food-related emotion, an oral defence mechanism primarily aimed at rejecting toxic or contaminated food and thus preventing foodborne illnesses. This adaptive response retains contemporary relevance, as diseases like botulism, linked to food contamination, are often lethal (Heilmann et al., 2024). Interestingly, ancestral disgust stimuli do not appear to form a uniform category but rather encompass distinct subcategories with varying emotional impacts. Stimuli representing disgusting animals, classified as an ancestral category and usually considered triggers of strong disgust (Davey & Marzillier, 2009, Polák et al, 2020), elicited responses comparable to those triggered by modern threats, such as polluted environments and toxic substances. The similar ratings for disgusting animals and polluted environments suggest that the sensory salience of certain modern threats may engage psychological mechanisms akin to those activated by ancestral disgust elicitors, albeit with reduced intensity. Modern threats like pollution and toxicity elicited comparatively weaker disgust responses than ancestral threats like spoiled food, potentially undermining protective behaviours against these hazards. This result is consistent with prior research suggesting that disgust as an evolved mechanism is less effective in addressing abstract or invisible dangers, such as chemical pollutants or radiation (Oaten et al., 2009; Peléšková et al., 2024). Modern threats often lack immediate sensory cues (e.g., smell or visual contamination) that traditionally trigger disgust, which may explain the weaker responses. A recent study showed that these categories of modern threats evoke more fear and anger rather than disgust (Landová et al., 2025). The limitations of disgust in addressing modern environmental challenges have significant implications for public health and environmental behaviour. The relatively low ratings for pandemic-related stimuli in our study may reflect the novel and less immediate nature of these threats compared to other categories. Mermin-Bunnell and Ahn (2022) posited that disgust is more attuned to organic and visible contamination than abstract or systemic risks. They found that presenting disgusting images related to COVID-19 increased public health compliance, especially among conservatives. Among unvaccinated conservative participants, these images significantly increased their willingness to be vaccinated compared to less disgusting images or perks offered for COVID-19 vaccines. The authors suggested that interventions emphasising sensory salience as such disgusting images in public health campaigns could improve compliance and help accelerate the end of the COVID-19 pandemic. Among the pandemic-related visual stimuli, the strongest disgust was elicited by visible infection cues (sneezing), demonstrating a perceptual link between observable pathogen presence (overt contagion cue) and disgust (Schaller & Park, 2011; Stevenson et al., 2021). However, weaker responses to hospital environments and individuals wearing masks suggest that these stimuli may evoke complex emotional reactions, blending disgust with fear or even social considerations, such as empathy or norms of care (Li, 2021). While associated with disease, masks may also convey protection and social responsibility, dampening their disgust-eliciting potential. Perceptions of mask-wearing during the COVID-19 pandemic evolved as they became normalised and valorised in many cultures (Capraro & Barcelo, 2021). Peléšková et al. (2024) reported that pandemic-related stimuli primarily elicited fear and anger rather than disgust, highlighting the multi-dimensional emotional responses to such threats. Our findings suggest that while pandemic-related stimuli can activate disgust, their internal heterogeneity reflects the complex interplay between sensory cues, cultural norms, and individual risk perceptions. Recent studies examined the impact of the COVID-19 pandemic on disgust sensitivity. For instance, Stevenson et al. (2021) found that individuals reported higher levels of disgust sensitivity during Australia's lockdown period. Furthermore, self-reported compliance with official recommendations during the COVID-19 pandemic was partly driven by individual differences in moral values, disgust sensitivity, and psychological reactance (Díaz and Cova, 2022). In contrast, Schwambergová et al. (2023) observed that the pandemic elevated sensitivity to moral disgust but not pathogen disgust. The division into subcategories in our study complements these findings by demonstrating that pandemic-related stimuli do not uniformly activate disgust mechanisms. The nuanced responses to pandemic-related stimuli highlight discrepancies with studies emphasising a more uniform disgust response to disease cues (Schaller & Park, 2011). These differences may stem from the unprecedented social dynamics of the COVID-19 pandemic, which introduced novel stimuli (e.g., masks) and altered contextual interpretations of disease-related behaviours. The multivariate redundancy analysis (RDA) revealed significant contributions of individual sensitivities and demographic characteristics to disgust evaluation. Fear of spiders, or more generally repulsion linked to invertebrates, as measured by the SPQ-12, emerged as the strongest predictor, underscoring the role of specific phobias in shaping disgust responses. The influence of the pathogen and core disgust subscales of the TDDS and DS-R align with the evolutionary framework, where heightened sensitivity to disease cues enhances survival. Respondents with higher pathogen disgust scores showed stronger reactions to ancestral stimuli, supporting that pathogen sensitivity is an adaptive trait linked to disease avoidance (Tybur et al. 2009; Oaten et al., 2009). Moral disgust also emerged as a significant predictor, particularly in responses to modern and pandemic-related threats. The effect of moral disgust, particularly in respondents with non-biological educational backgrounds, suggests an interaction between cognitive schemas and emotional responses. This aligns with Tybur et al. (2013), who proposed that moral disgust extends the scope of the emotion beyond pathogen avoidance to include social and ethical violations. There is evidence that moral disgust, while rooted in the same neural circuitry as pathogen disgust, can be modulated by cultural and educational factors (Chapman & Anderson, 2013). Age-related differences, though relatively minor, highlight the potential for life-stage-specific modulation of disgust sensitivity. While less reactive to immediate pathogen cues, older adults may exhibit stronger moral disgust, reflecting the growing importance of social and ethical considerations in later life, as Rozin et al. (2009) suggested. This trend is evident in the RDA’s second axis, which links moral disgust with age and non-biological education fields. Additionally, the influence of education and the field of study is noteworthy and highlights the role of cognitive and cultural factors in shaping emotional responses. In our study, variability in disgust responses among biologists was more influenced by the stress of COVID-19. Subsequently, they ranked as more repulsive the pandemic-related disgust images compared to disgusting animals, possibly due to greater exposure to or awareness of pathogen-related risks. On the other hand, economically and technically educated respondents were more sensitive to moral disgust and ranked modern threats such as toxic or radioactive pollution as more disgusting. Respondents with a background in social sciences were more sensitive to core and pathogen disgust, in which case they ranked higher disgusting animals. The significant effect of stress of the COVID-19 pandemic, as measured by the C-19SS, further underscores the dynamic nature of disgust. Elevated stress levels heightened sensitivity to pathogen cues, reflecting the adaptive recalibration of emotional responses during heightened disease threats. This finding is consistent with research on the behavioural immune system, which posits that environmental pathogen prevalence modulates disgust sensitivity (Ackerman et al., 2018; Cepon-Robins et al., 2021). Conclusion In conclusion, this study provides valuable insights into the complexities of disgust responses to various stimuli, including ancestral, modern, and pandemic-related threats. The results highlight distinct emotional reactions to these categories, with ancestral disgust stimuli such as spoiled food eliciting the strongest responses, whereas modern threats like pollution, radioactivity, and pandemic-related cues provoked weaker, more heterogeneous reactions. The factor analysis further revealed the internal variability within pandemic-related stimuli, with visible signs of infection (e.g., sneezing) generating stronger disgust compared to hospital environments or people wearing masks. Additionally, individual factors such as fear of spiders, moral disgust sensitivity, age, and educational background played significant roles in shaping disgust responses. Our findings highlight the complexity of disgust as an emotion shaped by evolutionary history, cultural influences, and individual differences. The differential responses to ancestral, modern, and pandemic-related threats underscore the adaptability of disgust mechanisms in responding to novel threats, such as pandemics, while also revealing their limitations in addressing modern environmental challenges. By integrating evolutionary theory with contemporary data, we can better understand the interplay between ancestral mechanisms and modern challenges in shaping human emotional responses. By bridging theoretical perspectives with empirical evidence, this research contributes to a more comprehensive understanding of the adaptive and maladaptive aspects of disgust in the modern world. Future directions The differentiation of pandemic-related disgust into subcategories offers a valuable framework for understanding the complexity of modern disease-related emotions including disgust, fear, and anxiety. For example, a study has shown that induced disgust increases self-reported anxiety across different types of stimuli, regardless of their relevance to disgust or fear (Davey et al., 2008). This interplay highlights the importance of understanding disgust not only as a standalone emotion but also as a component of broader emotional responses, particularly in the context of anxiety disorders. Thus, future research should explore the interplay between disgust and other emotions, such as fear and anxiety, in shaping responses to complex contemporary threats such as new pandemics. Future studies should also explore how cultural, temporal, and contextual factors shape responses to these stimuli. Additionally, longitudinal studies examining the impact of prolonged exposure to pandemic-related stimuli on disgust sensitivity could provide valuable insights into the dynamic nature of emotional responses and elucidate whether the observed patterns persist as societies adapt to pandemics and associated public health measures. Finally, the interaction between individual characteristics and disgust underscores the need for personalised health communication and intervention approaches. For instance, understanding the role of moral disgust in shaping attitudes toward public health measures could inform strategies to address vaccine hesitancy or compliance with hygiene practices. Declarations Ethical approval This study was carried out following the approval of the respective ethical committee (hidden for the review) and following the Declaration of Helsinki. All subjects provided informed consent with participation in the study and personal data processing. 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Sympathetic and parasympathetic cardiac responses to phobia‐relevant and disgust‐specific emotion provocation in blood‐injection‐injury phobia with and without fainting history. Psychophysiology , 54 (10), 1512-1527. https://doi.org/10.1111/psyp.12900 Stark, R., Walter, B., Schienle, A., & Vaitl, D. (2005). Psychophysiological correlates of disgust and disgust sensitivity. Journal of Psychophysiology , 19 (1), 50-60. https://doi.org/10.1027/0269-8803.19.1.50 Stat Soft, Inc. (2011). STATISTICA (Data Analysis Software System), Version 10. Stevenson, R. J., Saluja, S., & Case, T. I. (2021). The impact of the Covid-19 pandemic on disgust sensitivity. Frontiers in Psychology , 11 , 600761. https://doi.org/10.3389/fpsyg.2020.600761 Taylor, S., Landry, C. A., Paluszek, M. M., Fergus, T. A., McKay, D., & Asmundson, G. J. (2020). Development and initial validation of the COVID Stress Scales. Journal of Anxiety Disorders , 72 , 102232. https://doi.org/10.1016/j.janxdis.2020.102232 Thiebaut, G., Méot, A., Witt, A., Prokop, P., & Bonin, P. (2021). The behavioral immune system: How does it contribute to our understanding of human behavior. In A. M. Columbus, Advances in Psychology Research, Vol. 144 (pp. 1-59). Tybur, J. M., Lieberman, D., & Griskevicius, V. (2009). Microbes, mating, and morality: individual differences in three functional domains of disgust. Journal of Personality and Social Psychology , 97 (1), 103-122. https://doi.org/103.10.1037/a0015474 Tybur, J. M., Lieberman, D., Kurzban, R., & DeScioli, P. (2013). Disgust: evolved function and structure. Psychological Review , 120 (1), 65-84. https://doi.org/10.1037/a0030778 Tybur, J. M., Merriman, L. A., Hooper, A. E. C., McDonald, M. M., & Navarrete, C. D. (2010). Extending the behavioral immune system to political psychology: Are political conservatism and disgust sensitivity really related?. Evolutionary Psychology , 8 (4), 599-616. https://doi.org/10.1177/147470491000800406 Tybur, J., Inbar, Y., Aarøe, L., Barclay, P., Barlow, F., Barra, M., et al. (2016). Parasite stress and pathogen avoidance relate to distinct dimensions of political ideology across 30 nations. Proceedings of the National Academy of Sciences, 113 (44), 12408-12413. https://doi.org/10.1073/pnas.1607398113 van Leeuwen, F., Jaeger, B., & Tybur, J. M. (2023). A behavioural immune system perspective on disgust and social prejudice. Nature Reviews Psychology , 2 (11), 676-687. https://doi.org/10.1038/s44159-023-00226-4 Venables WN, Ripley BD (2002). Modern Applied Statistics with S, Fourth edition. Springer: New York. https://www.stats.ox.ac.uk/pub/MASS4/ Wheaton, M. G., Abramowitz, J. S., Berman, N. C., Fabricant, L. E., & Olatunji, B. O. (2012). Psychological predictors of anxiety in response to the H1N1 (swine flu) pandemic. Cognitive Therapy and Research , 36 , 210-218. https://doi.org/10.1007/s10608-011-9353-3 Wicker, B., Keysers, C., Plailly, J., Royet, J. P., Gallese, V., & Rizzolatti, G. (2003). Both of us disgusted in My insula: the common neural basis of seeing and feeling disgust. Neuron , 40 (3), 655-664. https://doi.org/10.1016/S0896-6273(03)00679-2 Wright, P., He, G., Shapira, N. A., Goodman, W. K., & Liu, Y. (2004). Disgust and the insula: fMRI responses to pictures of mutilation and contamination. Neuroreport , 15 (15), 2347-2351. https://doi.org/10.1097/00001756-200410250-00009 Yang, Q., Yan, L., Luo, J., Li, A., Zhang, Y., Tian, X., & Zhang, D. (2013). Temporal dynamics of disgust and morality: an event-related potential study. PLoS One , 8 (5), e65094. https://doi.org/10.1371/journal.pone.0065094 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5960947","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415108925,"identity":"0b848130-e580-4aeb-b527-b994ddfae28c","order_by":0,"name":"Janovcová Markéta","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Janovcová","middleName":"","lastName":"Markéta","suffix":""},{"id":415108926,"identity":"34f5761b-f16a-4bd8-ba2c-83f00565f848","order_by":1,"name":"Polák Jakub","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Polák","middleName":"","lastName":"Jakub","suffix":""},{"id":415108927,"identity":"12fb97ba-786f-4ff8-a040-6416f6a69671","order_by":2,"name":"Anna Končická","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Končická","suffix":""},{"id":415108928,"identity":"24c934d8-ee9d-4c13-beae-c8889838bb7a","order_by":3,"name":"Aleksandra Chomik","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Aleksandra","middleName":"","lastName":"Chomik","suffix":""},{"id":415108929,"identity":"75902b3d-1d38-43ed-95ab-e41a8fd3a7df","order_by":4,"name":"Šárka Kaňková","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Šárka","middleName":"","lastName":"Kaňková","suffix":""},{"id":415108930,"identity":"beb95ece-5acd-4d8a-b227-ebba61f44418","order_by":5,"name":"Daniel Frynta","email":"","orcid":"","institution":"Charles University","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Frynta","suffix":""},{"id":415108931,"identity":"00ab4c9d-be2a-4afb-a4c3-fed72009df40","order_by":6,"name":"Eva Landová","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYHACxgMMDMxAmvkA8XqgWtgSSNbCY0Cccn6J5AcHfu6wljM4fubzhw9/7sjrNjA//oBPi+SMNIODvWfSjQ3O5G6TnMHzzHDbATYDvPYZnDlgcIC37XDizIbcbcw8EocZtx3gYUjAp8X+zPEPB/+CtPS/efz5j8Fhe5CWA3htYe8xOAyypV8ih0GaIeFwIlALYwM+LRLHewoOy7alG/NLPDOT7DlwOHnbYTZjfDoY+JvZNz5822Ytx8af/PjDjz+Hbbcdb8YfYlgAM4nqR8EoGAWjYBRgAgD0clE3QAkjNQAAAABJRU5ErkJggg==","orcid":"","institution":"Charles University","correspondingAuthor":true,"prefix":"","firstName":"Eva","middleName":"","lastName":"Landová","suffix":""}],"badges":[],"createdAt":"2025-02-04 21:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5960947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5960947/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40806-025-00442-6","type":"published","date":"2025-09-03T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77037292,"identity":"79c2340b-ac82-441e-b437-72cda360c6a5","added_by":"auto","created_at":"2025-02-24 13:35:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2560464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamples of stimulus categories.\u003c/strong\u003e Authors of original photos: a) Tiia Monto, b), f), g) Markéta Janovcová, c) stimulus 9295, database IAPS, d) Mstyslav Chernov, e) Tereza Hladíková, h) Silvie Rádlová.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5960947/v1/2c0105a87ef6d7f376ee0373.png"},{"id":77037287,"identity":"e1d881d8-a222-4837-a43f-4366a63a08ae","added_by":"auto","created_at":"2025-02-24 13:35:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":335347,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster tree for rating all stimuli according to perceived disgust.\u003c/strong\u003e The graph shows a clear categorization of the stimuli into groups corresponding to the a priori proposed categories - disgusting animals (Animal), spoiled food (Food), polluted environment (Toxic), airborne disease pandemics (Disease) and controls (Leaf, Leaves).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5960947/v1/1ac930e3f29fb915e29d5fc3.png"},{"id":77037289,"identity":"d77a7a96-861c-4bf9-9ffb-840a25aa4e1f","added_by":"auto","created_at":"2025-02-24 13:35:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox-plots to compare ratings of stimulus categories. \u003c/strong\u003ePlot\u003cstrong\u003e a)\u003c/strong\u003e for the originally proposed categories - controls (single leaf, multiple leaves) ancestral (disgusting animals, spoiled food) and modern (polluted environment, airborne diseases); plot \u003cstrong\u003eb)\u003c/strong\u003e taking into account the results of the factor analysis, the airborne diseases category divided into three subcategories (shots from hospitals, people wearing masks and sneezing people).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5960947/v1/8c19d42e3ac8c86ef117d7e7.png"},{"id":77037291,"identity":"4b8343e4-ef3e-45f1-86ef-1a20ca9e0557","added_by":"auto","created_at":"2025-02-24 13:35:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":563733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical output of the redundancy analysis (RDA). \u003c/strong\u003eResults for the first two axes of the final model that explain the most variability, RDA 1 explains 6.1% of the variability, RDA 2.9% of the variability, the full model explains 12.4% of the variability. Only the significant explanatory variables of the final model, socio-demographic characteristics (age, field of education) and the final scores from the psychological questionnaires (in italics) are shown. The stimuli rated by perceived disgust are divided by category (see graph legend), with individual respondents depicted by grey circles.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5960947/v1/b8524fed97b2f61eeafe0229.png"},{"id":90827955,"identity":"e3335af4-ae09-4edf-89f5-4237ec802f42","added_by":"auto","created_at":"2025-09-08 16:04:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5588136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5960947/v1/0e9ef85c-916b-4db8-8bc6-944eb2ddbe96.pdf"},{"id":77037286,"identity":"cf9fa7bd-1b0d-4742-b9a2-80f13e1b10c1","added_by":"auto","created_at":"2025-02-24 13:35:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14374,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialslegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-5960947/v1/1e42cc88083dd939ac210858.docx"},{"id":77039142,"identity":"b79c1e73-6de2-43d1-8cb5-2a54f18f5139","added_by":"auto","created_at":"2025-02-24 13:43:31","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":166400,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.xls","url":"https://assets-eu.researchsquare.com/files/rs-5960947/v1/cac64400903e93efd96d9926.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"From bugs to sickness: disgust evaluation of ancestral, modern, and pandemic threats","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eProximate and ultimate mechanisms of disgust\u003c/h2\u003e \u003cp\u003eEarly humans faced significant threats from pathogens, contaminated food, and waste products leading to disgust as an adaptive response to such stimuli (Hlay et al., 2021). The emotion of disgust is therefore considered one of the most ancient emotional systems developed to promote the survival of organisms by avoiding intoxication or disease (Curtis et al., 2004; Schaller \u0026amp; Park, 2011). Emerging from evolutionary adaptations, disgust has developed into the behavioural immune system, a complex psychological and physiological construct affecting various human behaviour domains (Thiebaut et al., 2021). For instance, the universal facial expression associated with disgust includes a wrinkling of the nose and a down-turning of the mouth corners, which signals revulsion and a desire to avoid the source of disgust. Additionally, feelings of nausea and fear of contagion are common responses that further reinforce avoidance behaviour towards perceived threats (Davey, 2011). Recent findings further corroborate this view, showing that disgust sensitivity correlates with behaviours aimed at reducing pathogen exposure, such as enhanced hygiene practices (Stevenson et al., 2021).\u003c/p\u003e \u003cp\u003eEvidence of its evolutionary roots can be traced before primates, back to early vertebrates, with some scholars proposing that even basic forms of aversion to harmful substances existed in ancestral species over 500\u0026nbsp;million years ago. Itoigawa and colleagues (2024) have reported that bitter taste receptors, which help organisms detect potentially toxic compounds, are present in a wide range of vertebrates, including sharks and rays. Among primates, disgust-like behaviours are well-documented, including avoidance of faecal matter, spoiled food, and contaminated objects, suggesting that this emotion emerged long before the divergence of humans and other primates during the Miocene epoch, approximately 23\u0026ndash;5\u0026nbsp;million years ago (Sarabian et al., 2018). Studies in non-human primates, such as chimpanzees and bonobos, show advanced contamination sensitivity and nuanced avoidance behaviours that align with the parasite avoidance theory, highlighting a sophisticated evolutionary mechanism to minimise exposure to infectious agents (Sarabian et al., 2017, 2023; reviewed in Schwambergov\u0026aacute; et al., 2023). Additionally, the communicative role of disgust, such as through facial expressions that signal contamination risks to conspecifics, appears to be a shared trait among primates and underscores its adaptive value in social species (Preuschoft \u0026amp; van Hooff, 1995).\u003c/p\u003e \u003cp\u003eDisgust is associated with distinct physiological responses, including nausea, heart rate deceleration, and changes in skin conductance (Stark et al., 2005), all commonly linked to parasympathetic nervous system activation (Cisler et al., 2009). However, findings from physiological studies vary. In her review, Kreibig (2010) identifies an alternative pattern involving partial sympathetic-parasympathetic co-activation, characterised by heart rate acceleration, faster breathing, and reduced inspiration - especially in response to contamination stimuli, as opposed to blood and injury. De Jong and colleagues (2011) investigated the autonomic nervous system responses to a disgust-inducing video and their relationship with individual differences in disgust propensity and sensitivity. Their study found that disgust elicited increased parasympathetic activity in both cardiac and digestive systems, alongside heightened sympathetic activation in the cardiac system. Interestingly, these physiological responses were not correlated with participants' self-reported disgust levels or their habitual disgust propensity or sensitivity, indicating that subjective experiences and physiological responses to disgust may operate independently. While debate continues about the role of parasympathetic activation in disgust responses, it is evident that disgust is a weaker sympathetic activator than fear (Simon et al., 2017).\u003c/p\u003e \u003cp\u003eUnlike fear, disgust engages a distinct neural network that includes the anterior insular cortex, basal ganglia, ventrolateral and medial prefrontal cortex, anterior temporal cortex, and visual cortex (Wicker et al., 2003; Calder et al., 2007; Klucken et al., 2012; Koenigs, 2013; Gan et al., 2024). Interestingly, distinct spatiotemporal patterns of neural activity have been found for core and moral disgust (Yang et al., 2013). Neuroimaging studies consistently implicate the insula as a central hub in disgust processing, reflecting its role in integrating visceral and emotional signals (Wicker et al., 2003; Wright et al., 2004; cf. Schienle et al., 2002) and the signal of neural activity appears 300 ms after stimulus onset supporting the notion of disgust as a rapid, automatic response (Krolak-Salmon et al., 2003).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTheories of disgust\u003c/h2\u003e \u003cp\u003eAccording to early research, disgust has oral origins and evolved primarily to prevent ingestion of harmful substances, such as spoiled food, which could lead to illness or even death (Rozin et al., 2009; for review see Curtis, 2011). Rozin and Fallon (1987) first proposed the concept of \"core disgust\" as a mechanism to guard the organism against contamination and maintain physical and symbolic boundaries. The three-domain model proposed by Tybur et al. (2009) has further refined this understanding, providing a structural framework for exploring the domains of disgust in relation to evolutionary pressures. In their model, disgust is understood to operate across three primary domains: pathogen disgust, which targets contamination risks; sexual disgust, which serves to avoid fitness-compromising reproductive choices; and moral disgust, which regulates social behaviours to maintain group harmony (reviewed in Chapman \u0026amp; Anderson, 2013). Each domain demonstrates unique triggers and consequences. For example, sexual disgust is particularly sensitive to cues indicating potential genetic incompatibility or disease, whereas moral disgust often responds to violations of fairness or harm (Tybur et al., 2013).\u003c/p\u003e \u003cp\u003eWhile these domains are universal, cultural and environmental variabilities influence their expression (Haidt et al., 1997). For instance, pathogen disgust may manifest more strongly in societies with a high prevalence of infectious diseases. These societies also tend to be put a greater emphasis on social conformity, strong family ties, religiosity and traditional values (Fincher \u0026amp; Thornhill, 2012), which can be seen as a collective strategy to mitigate disease risk (Jędryczka, 2022). This may consequently shift the cultural orientation to collectivism, a phenomenon explained by the parasite-stress theory of values and sociality, suggesting that recurrent infectious diseases drive collectivist behaviours to mitigate risks (Cepon-Robins et al., 2021; Hlay et al., 2021; reviewed in Shapouri, 2023). However, recent findings by Shapouri and Rafiee (2024) challenge this hypothesis, reporting that ecological threats such as parasite stress (operationalized as the frequency of epidemics) and natural disasters could not significantly predict collectivism scores in a cross-national analysis of 188 countries. The results suggest that previous findings linking ecological threats to collectivism may be due to small, non-representative samples.\u003c/p\u003e \u003cp\u003eAs already mentioned, disgust functions not only as a disease-avoidance mechanism triggered in the presence of pathogens but also extends to social and moral domains, reflecting deeper cognitive processes that underpin judgments about fairness, harm, and purity (Haidt, 2001) and govern our interactions with others (Giner-Sorolla et al., 2018). For example, moral disgust has been shown to intensify punitive attitudes toward individuals who violate societal norms, reflecting its role in enforcing social order (Konishi et al., 2017; Oaten et al., 2018), which can be interpreted as an adaptive response to potential disease threats (Nussinson et al., 2018). This aversion can manifest in various social behaviours, including exclusionary attitudes towards outgroups \u0026ndash; such as immigrants (Aar\u0026oslash;e et al., 2017) or gays (Inbar et al., 2009b) \u0026ndash; and increased conformity to social norms, particularly in environments perceived as high-risk for disease transmission (Kusche \u0026amp; Barker, 2019). Disgust is, therefore, critical in intergroup relations, contributing to prejudice and dehumanisation in historical and contemporary contexts (Kteily et al., 2015). Furthermore, studies have shown that individuals with heightened disgust sensitivity are more likely to hold conservative political views, as these often correlate with a preference for traditional norms and a wariness of change (Inbar et al., 2009a; Tybur et al., 2016; Rosenfeld \u0026amp; Tomiyama, 2021; O\u0026rsquo;Shea et al., 2022). This relationship suggests that the behavioural immune system may influence political ideologies by shaping perceptions of social threats and guiding responses to those threats.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAncestral, modern, and pandemic-related disgust elicitors\u003c/h3\u003e\n\u003cp\u003eAncestral disgust elicitors, such as body waste, decaying organic matter, and infected wounds, evoke strong disgust reactions because they are directly tied to disease transmission risks (Curtis et al., 2004, 2011; Oaten et al., 2009). These elicitors engage sensory cues like foul odours or grotesque visuals that signal immediate contamination threats. In contrast, modern disgust elicitors, including chemical pollutants, industrial waste, and radioactivity, often trigger weaker disgust responses but are more likely to elicit fear and anger (Pel\u0026eacute;škov\u0026aacute; et al., 2024). These modern threats lack the visceral sensory characteristics of ancestral hazards and instead rely on abstract knowledge and higher cognitive processes to recognise their danger (Hacquin et al., 2022; Lanondov\u0026aacute; et al., 2025). Shapouri and colleagues (2023) examined affective responses to natural and technological disasters, showing that natural threats evoke primal fear, while technological disasters elicit complex emotions such as anxiety. In contrast, Landov\u0026aacute; et al. (2025) claim that the role of anxiety in response to various types of threats, including the modern ones, is relatively weak.\u003c/p\u003e \u003cp\u003eDisgust sensitivity has been shown to increase during pandemics, likely as part of the behavioural immune system\u0026rsquo;s adaptive response to heightened disease threats, highlighting its role in motivating pathogen-avoidance behaviours (Makhanova \u0026amp; Shepherd, 2020; Kaňkov\u0026aacute; et al., 2023). For example, individuals with higher disgust sensitivity reported greater adherence to hygiene measures such as frequent handwashing, mask-wearing, and social distancing during the COVID-19 pandemic (Stevenson et al., 2021). Beyond COVID-19, research on other diseases, such as Ebola, highlights the role of disgust in shaping social behaviour, including the avoidance of infected individuals and stigmatisation of outgroups perceived as disease carriers (Jalloh et al., 2017). Similar patterns were observed during the 2009 H1N1 influenza pandemic, where disgust responses correlated with protective behaviours and increased prejudice toward groups associated with the disease's origin (see the review by van Leeuwen et al., 2023).\u003c/p\u003e \u003cp\u003eRecently, the COVID-19 pandemic has presented a unique challenge for human perception and emotional response, as the threat was invisible to the naked eye and primarily inferred from indirect cues. These modern signals, such as masks, sanitisers, news of viral spread, or overcrowded spaces, often evoke responses linked to ancestral disgust mechanisms. In this study, we aim to explore how ancestral, modern, and pandemic-related repulsive stimuli, presented as visual images, are subjectively evaluated on perceived disgust. Previously, it has been shown that pandemic-related threats presented as written scenarios align more closely with ancestral dangers (Pel\u0026eacute;škov\u0026aacute; et al., 2024; Landov\u0026aacute; et al., 2025). However, whether this pattern holds for visual stimuli, which engage different cognitive and emotional processes, remains uncertain. Understanding how disgust operates in the modern world and during crises like the COVID-19 pandemic is critical for uncovering its broader psychological and behavioural implications.\u003c/p\u003e\n\u003ch3\u003eAims of study\u003c/h3\u003e\n\u003cp\u003eThe present study seeks to advance our understanding of disgust by addressing three key objectives:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e1. Categorization of Disgust Stimuli\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003cp\u003eThis study aims to validate and refine the categorisation of disgust-eliciting stimuli into predefined domains - ancestral (e.g., spoiled food, parasitic invertebrates, and visible parasites), modern (e.g., toxins, radioactivity, polluted environments, and pandemic-related cues such as fear of hospitalisation, death, masks, or sneezing). By examining the distinctiveness and coherence of these categories, we aim to establish whether they represent meaningful and separate dimensions of disgust. A central question guiding this investigation is whether pandemic-related threats, including invisible pathogens and fear-inducing modern scenarios, align more closely with modern disgust elicitors or exhibit similarities to ancestral categories.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e2. Intensity of Emotional Responses to Different Stimuli\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003cp\u003eThis study will evaluate the relative intensity of emotional responses elicited by ancestral versus modern disgust stimuli. Building on theoretical predictions suggesting the heightened salience of ancestral threats due to their evolutionary significance, we will examine whether ancestral stimuli consistently evoke stronger emotional reactions than modern threats. Furthermore, we will address the role of pandemic-related threats by investigating how their emotional intensity compares to those of ancestral and modern categories.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e3. Contribution of Individual Sensitivities to Emotional Responses\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003cp\u003eThis study will assess the role of individual differences and threat-specific sensitivities in shaping the perception and evaluation of visual representations of ancestral, modern, and pandemic-related threats. We aim to determine how variability in individual sensitivity modulates emotional reactions across these domains, shedding light on the interplay between personal characteristics and threat-specific disgust responses.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe testing procedure was completed by 262 respondents (181 women, 81 men). Before starting the experiment, we calculated the appropriate sample size using \u003cem\u003ea priori\u003c/em\u003e power analysis. For this experiment, 105 respondents would be needed for large effects (effect size p\u0026thinsp;=\u0026thinsp;0.40) and 233 respondents for medium effects (p\u0026thinsp;=\u0026thinsp;0.25). Thus, our sample size is sufficient. All respondents were from Central Europe and completed the experiment in Czech or English. They were recruited from students at several universities, including a university of the third age, and their relatives. We also reached out to former participants from our previous projects. We could obtain participants of different age groups (age 18\u0026ndash;79, mean 31.6, SD\u0026thinsp;=\u0026thinsp;15.8) and educational backgrounds: biological (112 respondents), social science (60), general (29), technical (26), economic (24) and medical (11). Most of them have obtained a university degree (196 respondents), and 66 participants have completed secondary education. A complete list of respondents with their detailed socio-demographic characteristics is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStimuli\u003c/h2\u003e \u003cp\u003ePhotographs of disgusting objects/situations were used as stimuli in this project. The testing set contained a total of 60 pictures: 40 pictures representing four different categories of disgusting objects and 20 control pictures of leaves (10 images of a single leaf on a grey background and 10 images of multiple leaves in a natural context). These controls were used successfully in previous projects (Landov\u0026aacute; et al., 2020, 2023). The four categories of disgusting objects/situations containing 10 pictures each were: 1) spoiled food, 2) invertebrates and parasites, 3) polluted environments and modern toxic substances, and 4) images related to an airborne disease pandemic (hospital shots, people sneezing, people wearing masks). The images for the testing set were selected from published databases (DIRTI: Haberkamp et al., 2017; IAPS: Lang \u0026amp; Bradley, 2007; SMID: Crone et al., 2008; EmoMadrid: Carreti\u0026eacute; et al., 2019), internet sources (Pixabay, Wikimedia Commons, etc.), or taken by the authors and collaborators. A complete list of authors and photo sources is available in Table S2. The original images have been modified to print format, size 10x15 cm, 300 DPI; no other modifications have been made. The printed set of photographs was subsequently used for the testing procedure (for a sample of stimuli see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment\u003c/h3\u003e\n\u003cp\u003eIn addition to socio-demographic data (age, gender, level and field of education), an assessment battery was used to determine the respondents\u0026rsquo; emotional sensitivity. Two commonly used questionnaires, the Three Domains of Disgust Scale (TDDS; Tybur et al., 2009; only the pathogen and more disgust subscale were used, the sexual disgust subscale was omitted as it was not relevant to our study) and the Disgust Scale - Revised, (DS-R: Haidt et al., 1994, modified by Olatunji et al., 2007; Czech translation: Pol\u0026aacute;k et al., 2019) were selected to measure disgust propensity. Furthermore, a short version of the Spider Questionnaire (SPQ-12; original scale by Klorman et al., 1974; short version Pol\u0026aacute;k et al., 2020) was used. Although the SPQ-12 focuses only on spiders, the total score is a good predictor of people's attitude towards small invertebrates, which are often considered disgusting (Landov\u0026aacute; et al., 2021).\u003c/p\u003e \u003cp\u003eRegarding airborne disease pandemics, several questionnaires were selected to cover the period of the COVID-19 pandemic: the Coronavirus Safety Behaviours Scale (CSBS; Knowles \u0026amp; Olatunji, 2021; originally developed in the context of the Ebola and H1N1 epidemic by Wheaton et al., 2012 and Blakey et al., 2015), assessing behaviours that people performed during the pandemic, and the COVID Stress Scales (C-19SS; Taylor et al., 2020), which assesses fears, problems, and control behaviours during the pandemic. All questionnaires were administered in Czech or English.\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThe research was conducted between January and October 2024, testing was conducted in the personal presence of the experimenters. First, each respondent signed an informed consent form and completed the socio-demographic information. The testing picture set was always laid on a well-lighted table for each respondent so he/she could see all the pictures clearly. The order of the pictures was randomised, and only the categories were considered, i.e. all the pictures from the same category would not be directly next to each other. The respondent was then asked to take a good look at the whole set and then to sort all the pictures according to perceived disgust into one packet so that the picture evoking the most disgust was on the top and the picture evoking the least disgust was on the bottom. No time limit was given to the respondents for the rating; on average, it took about 15 minutes to sort the pictures. Finally, each respondent completed the assessment battery. The complete data are available in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eTo determine the appropriate sample size, we used an \u003cem\u003ea priori\u003c/em\u003e power analysis computed in G Power (Erdfelder et al., 1996). Kendall's Coefficient of Concordance (W) calculated using the R program, package irr (R Core Team, 2019; Gamer et al., 2019) was used to determine agreement among respondents on the ranking of stimuli by perceived disgust. Before further analyses, image-ordering data were square-root arcsine transformed. The mean for each stimulus was then calculated, and the final rank order of the ranked images was generated according to the mean rank. The average rating of the stimuli was used to compute linear models (LMs), the explanatory variable being the stimulus category. In the first analysis, the original categorisation (spoiled food, disgusting animals, polluted and toxic environment, airborne disease pandemic and its consequences, and two categories of controls) was used; in the second analysis, in addition, the airborne disease pandemic category was divided into three separate groups (images from hospitals, sneezing, people wearing masks). For a more detailed analysis of the relationships between groups, a \u003cem\u003epost hoc\u003c/em\u003e Tukey test was performed (program R, package lsmeans; Lenth, 2016). Raw image sorting data were used in further analyses. To visualise the data structure and confirm the meaningfulness of the created categories, a cluster analysis was used, the distance matrix was calculated using Pearson correlations among ratings, and tree diagrams were built using Ward's method. Furthermore, a factor analysis was performed to show the detailed structure of the created image groups, the principal component extraction and varimax normalised rotation methods were used, and a parallel analysis was used to determine the number of factors. The cluster, factor, and parallel analysis were calculated using Statistica 10 (Stat Soft, 2011). A multivariate redundancy analysis (RDA) was used to analyse the effect of individual sensitivity on the ratings of disgusting images. The raw data of the ranking of images by individual respondents was the response variable, and the explanatory variables were the final scores from the assessments and the respondents\u0026rsquo; characteristics (age, gender, education). The respondents' education was categorised by level (high school, university) and field of study (general, biological, medical, economic, social science, and technical). The statistical significance of the gradients was confirmed by permutation tests (number of permutations\u0026thinsp;=\u0026thinsp;20000). Calculations were performed with R, MASS (Venables \u0026amp; Ripley, 2002) and vegan (Oksanen et al., 2020) packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAgreement among respondents\u003c/h2\u003e \u003cp\u003eIn total, 262 respondents rated 60 images according to perceived disgust using the stimulus ordering method. Agreement among respondents in ranking the whole set was very high, Kendall's W\u0026thinsp;=\u0026thinsp;0.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. The complete picture set contained 20 control stimuli (leaves) that elicited the least disgust. When the control stimuli were removed in a subsequent analysis, the resulting inter-respondent agreement for the test images remained sufficiently high, W\u0026thinsp;=\u0026thinsp;0.345, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation and categorisation of disgust stimuli\u003c/h2\u003e \u003cp\u003eWe first visualised the data using cluster analysis to test the meaningfulness and consistency of the proposed groups. The tree diagram showed that the selected image categories formed separated consistent groups, i.e. the images appropriately represented the selected stimulus categories (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The exploratory factor analysis, preceded by the parallel analysis, determined five factors which explained 63.6% of the total variability. Factor 1 (24.7% of the explained variability) corresponded best with images depicting spoiled food. In comparison, factor 2 (17% of the explained variability) included disgusting animals on the one hand and polluted environment and toxicity on the other. The group of stimuli depicting a pandemic consisted of three separate factors: factor 3 (11.1% of the explained variability) corresponded to images depicting the consequences of illness (photos of a hospital environment and death), factor 4 (6.2%) was associated with a potential risk of airborne disease pandemic (people wearing masks), and factor 5 (4. 6%) pertained to the risk of direct infection (people sneezing) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The factor analysis showed that although the pandemic-related images were well distinguishable as a category from the other categories (animals, spoiled food, and polluted environment) in terms of disgust, the internal structure of this category was not consistent and was composed of three main subgroups.\u003c/p\u003e \u003cp\u003e \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\u003e\u003cb\u003eFactor analysis results.\u003c/b\u003e Results for five factors are shown; the number of factors was determined by parallel analysis. The most significant values for each stimulus are highlighted in bold. Stimulus codes correspond to the original categories, Animal (disgusting animals), Food (spoiled food), Toxic (polluted environment and toxicity), Disease (airborne disease pandemics; three subcategories of stimuli considered here).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStimulus code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFactor 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.750\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.644\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.719\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.621\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.681\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.660\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.625\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.709\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.548\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal_10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.713\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.800\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.843\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.540\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.878\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.832\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.806\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.798\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.823\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.754\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood_10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.840\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.715\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.468\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.681\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.368\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.566\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.516\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.656\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.428\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.559\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic_10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.542\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_01(hospital)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.856\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_02(hospital)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.895\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_03(sneezing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.885\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_04(masks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.901\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_05(sneezing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.897\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_06(masks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-\u003cb\u003e0.906\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_07(hospital)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.853\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_08(masks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.907\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_09(hospital)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.873\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease_10(hospital)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.780\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.128\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIntensity of emotional responses to different stimuli\u003c/h2\u003e \u003cp\u003eNext, we focused on the intensity of perceived disgust for each category. We used a linear model (LM) for the original stimulus categories (spoiled food, animals, toxicity and pollution, disease, and control stimuli). The results showed that the stimulus category affected the rating of perceived disgust (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and confirmed that disgust images differed conclusively from the control stimuli (leaves). Pictures of spoiled food, one of the categories of ancestral disgust elicitors, evoked the greatest disgust in the respondents. Contrarily, as the other ancestral category, disgusting animals resembled in their mean ratings the pictures of polluted environments and toxicity, i.e., the modern stimuli. The weakest average response (except for the control) was found for the pictures depicting a pandemic situation and its consequences (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the factor analysis outcomes, we created a second linear model in which the pandemic-related category was divided into three subgroups. The results again confirmed the effect of these groups on ratings of perceived disgust (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the distinctiveness of all the categories from controls (see Table S3 for complete results of both models). Of the three categories of pandemic threat, images depicting people sneezing, i.e. the visible sign of infection, were rated as the most disgusting. The average rating corresponded to the disgust level for polluted environments (modern disgust) and small animals (ancestral disgust). Pictures of hospital environments, particularly the risk of an airborne disease pandemic represented by people wearing masks, elicited the weakest disgust response (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFor a detailed analysis of the relationships between categories, we used a post hoc Tukey test. The analysis confirmed that all categories differed from the controls. The two groups of control stimuli (a leaf on a grey background vs leaves on a natural background) did not differ from each other (p\u0026thinsp;=\u0026thinsp;0.1172). Within the ancestral threats, there was no conclusive difference between the disgusting animals and spoiled food (p\u0026thinsp;=\u0026thinsp;0.2517). Modern threats represented by polluted and toxic environments differed from the ancestral spoiled food group (p\u0026thinsp;=\u0026thinsp;0.0142) but were not conclusively different from animals (p\u0026thinsp;=\u0026thinsp;0.5554). Images depicting the threat of a pandemic were significantly different from the spoiled food (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), animals (p\u0026thinsp;=\u0026thinsp;0.0001), and polluted environments (p\u0026thinsp;=\u0026thinsp;0.0026), thus forming a separate group (See Table S4 for detailed results).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eContribution of individual sensitivities to emotional responses\u003c/h2\u003e \u003cp\u003eMultivariate redundancy analysis (RDA) was used to analyse the effect of respondents' characteristics (age, gender, level, and field of education) and their sensitivity (assessment battery) on ratings of perceived disgust. The final model explained 12.4% of the total variability. The SPQ-12 (F\u0026thinsp;=\u0026thinsp;10.512, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and moral disgust subscale of the TDDS (F\u0026thinsp;=\u0026thinsp;4.227, p\u0026thinsp;=\u0026thinsp;0.0004) had the most significant effect on the evaluation of disgust. Furthermore, the respondents' field of education (F\u0026thinsp;=\u0026thinsp;2.295, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), pathogen disgust subscale of the TDDS (F\u0026thinsp;=\u0026thinsp;2.297, p\u0026thinsp;=\u0026thinsp;0.0159), core disgust subscale of the DS-R (F\u0026thinsp;=\u0026thinsp;2.238, p\u0026thinsp;=\u0026thinsp;0.0182), level of stress evoked by the COVID-19 pandemic measured by the C-19SS (F\u0026thinsp;=\u0026thinsp;2.644, p\u0026thinsp;=\u0026thinsp;0.0066), and the respondent\u0026rsquo;s age (F\u0026thinsp;=\u0026thinsp;2.046, p\u0026thinsp;=\u0026thinsp;0.0282) also significantly affected the disgust evaluation. The first axis (RDA1) explained 6.1% of the total variability. It was best predicted by the relationship to invertebrates (SPQ-12), sensitivity to disgust at the ancestral level (TDDS pathogen disgust DS-R core disgust), and biology or humanities fields of education. The second axis (RDA2) explained 2.9% of the total variability. It was best predicted by the respondents' moral attitude (TDDS moral disgust), age, the stress of COVID-19 (C-19SS), and non-biological fields of education (technical, economic). A visualisation of the results is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and detailed results of the RDA analysis are shown in Table\u0026nbsp;5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study examined the subjective evaluation of disgust in response to ancestral, modern, and pandemic threats. The validation of stimulus categories through cluster and factor analyses revealed meaningful distinctions between the ancestral and contemporary threat stimuli. The clear grouping of ancestral (spoiled food and disgusting animals) and modern threats (polluted environments and pandemic-related stimuli) aligns with theoretical expectations. Disgust evolved primarily as a pathogen avoidance mechanism (Curtis et al., 2011), with ancestral elicitors such as spoiled food and small animals posing direct and immediate survival risks (Davey, 2004, 2011).\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic offered an unprecedented context for evaluating disgust responses to disease-related stimuli. The categorisation and evaluation of pandemic-related stimuli represent a key contribution of this study as it extends the findings of Pel\u0026eacute;škov\u0026aacute; et al. (2024) by highlighting the internal heterogeneity of pandemic-related stimuli. While the previous research focused on overall emotional responses, our factor analysis revealed three distinct subcategories within pandemic-related disgust: 1) visible signs of infection (e.g., sneezing), 2) potential airborne disease risks (e.g., people wearing masks), and 3) consequences of illness (e.g., hospital environments). Such a distinction offers novel insights emphasising the need for a more granular approach to understanding pandemic-related responses.\u003c/p\u003e \u003cp\u003eFrom an evolutionary perspective, disgust serves as a protective mechanism to minimise contact with harmful pathogens and toxins. The study confirms that certain ancestral stimuli, particularly spoiled food, elicit the strongest disgust reactions, consistent with previous research highlighting the role of disgust in avoiding pathogens linked to foodborne illnesses (Curtis et al., 2004). This also aligns with prior research (Pel\u0026eacute;škov\u0026aacute; et al., 2024; Landov\u0026aacute; et al., 2025) showing that ancient threats like body waste products and worms trigger significant disgust responses. Thus, the pronounced disgust elicited by spoiled food reaffirms its status as a prototypical ancestral disgust elicitor due to its high pathogen load (Chapman et al., 2009; Tybur et al., 2013). According to Rozin and Fallon (1987), disgust originated as a food-related emotion, an oral defence mechanism primarily aimed at rejecting toxic or contaminated food and thus preventing foodborne illnesses. This adaptive response retains contemporary relevance, as diseases like botulism, linked to food contamination, are often lethal (Heilmann et al., 2024).\u003c/p\u003e \u003cp\u003eInterestingly, ancestral disgust stimuli do not appear to form a uniform category but rather encompass distinct subcategories with varying emotional impacts. Stimuli representing disgusting animals, classified as an ancestral category and usually considered triggers of strong disgust (Davey \u0026amp; Marzillier, 2009, Pol\u0026aacute;k et al, 2020), elicited responses comparable to those triggered by modern threats, such as polluted environments and toxic substances. The similar ratings for disgusting animals and polluted environments suggest that the sensory salience of certain modern threats may engage psychological mechanisms akin to those activated by ancestral disgust elicitors, albeit with reduced intensity.\u003c/p\u003e \u003cp\u003eModern threats like pollution and toxicity elicited comparatively weaker disgust responses than ancestral threats like spoiled food, potentially undermining protective behaviours against these hazards. This result is consistent with prior research suggesting that disgust as an evolved mechanism is less effective in addressing abstract or invisible dangers, such as chemical pollutants or radiation (Oaten et al., 2009; Pel\u0026eacute;škov\u0026aacute; et al., 2024). Modern threats often lack immediate sensory cues (e.g., smell or visual contamination) that traditionally trigger disgust, which may explain the weaker responses. A recent study showed that these categories of modern threats evoke more fear and anger rather than disgust (Landov\u0026aacute; et al., 2025).\u003c/p\u003e \u003cp\u003eThe limitations of disgust in addressing modern environmental challenges have significant implications for public health and environmental behaviour. The relatively low ratings for pandemic-related stimuli in our study may reflect the novel and less immediate nature of these threats compared to other categories. Mermin-Bunnell and Ahn (2022) posited that disgust is more attuned to organic and visible contamination than abstract or systemic risks. They found that presenting disgusting images related to COVID-19 increased public health compliance, especially among conservatives. Among unvaccinated conservative participants, these images significantly increased their willingness to be vaccinated compared to less disgusting images or perks offered for COVID-19 vaccines. The authors suggested that interventions emphasising sensory salience as such disgusting images in public health campaigns could improve compliance and help accelerate the end of the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eAmong the pandemic-related visual stimuli, the strongest disgust was elicited by visible infection cues (sneezing), demonstrating a perceptual link between observable pathogen presence (overt contagion cue) and disgust (Schaller \u0026amp; Park, 2011; Stevenson et al., 2021). However, weaker responses to hospital environments and individuals wearing masks suggest that these stimuli may evoke complex emotional reactions, blending disgust with fear or even social considerations, such as empathy or norms of care (Li, 2021). While associated with disease, masks may also convey protection and social responsibility, dampening their disgust-eliciting potential. Perceptions of mask-wearing during the COVID-19 pandemic evolved as they became normalised and valorised in many cultures (Capraro \u0026amp; Barcelo, 2021). Pel\u0026eacute;škov\u0026aacute; et al. (2024) reported that pandemic-related stimuli primarily elicited fear and anger rather than disgust, highlighting the multi-dimensional emotional responses to such threats. Our findings suggest that while pandemic-related stimuli can activate disgust, their internal heterogeneity reflects the complex interplay between sensory cues, cultural norms, and individual risk perceptions.\u003c/p\u003e \u003cp\u003eRecent studies examined the impact of the COVID-19 pandemic on disgust sensitivity. For instance, Stevenson et al. (2021) found that individuals reported higher levels of disgust sensitivity during Australia's lockdown period. Furthermore, self-reported compliance with official recommendations during the COVID-19 pandemic was partly driven by individual differences in moral values, disgust sensitivity, and psychological reactance (D\u0026iacute;az and Cova, 2022). In contrast, Schwambergov\u0026aacute; et al. (2023) observed that the pandemic elevated sensitivity to moral disgust but not pathogen disgust. The division into subcategories in our study complements these findings by demonstrating that pandemic-related stimuli do not uniformly activate disgust mechanisms. The nuanced responses to pandemic-related stimuli highlight discrepancies with studies emphasising a more uniform disgust response to disease cues (Schaller \u0026amp; Park, 2011). These differences may stem from the unprecedented social dynamics of the COVID-19 pandemic, which introduced novel stimuli (e.g., masks) and altered contextual interpretations of disease-related behaviours.\u003c/p\u003e \u003cp\u003eThe multivariate redundancy analysis (RDA) revealed significant contributions of individual sensitivities and demographic characteristics to disgust evaluation. Fear of spiders, or more generally repulsion linked to invertebrates, as measured by the SPQ-12, emerged as the strongest predictor, underscoring the role of specific phobias in shaping disgust responses. The influence of the pathogen and core disgust subscales of the TDDS and DS-R align with the evolutionary framework, where heightened sensitivity to disease cues enhances survival. Respondents with higher pathogen disgust scores showed stronger reactions to ancestral stimuli, supporting that pathogen sensitivity is an adaptive trait linked to disease avoidance (Tybur et al. 2009; Oaten et al., 2009).\u003c/p\u003e \u003cp\u003eMoral disgust also emerged as a significant predictor, particularly in responses to modern and pandemic-related threats. The effect of moral disgust, particularly in respondents with non-biological educational backgrounds, suggests an interaction between cognitive schemas and emotional responses. This aligns with Tybur et al. (2013), who proposed that moral disgust extends the scope of the emotion beyond pathogen avoidance to include social and ethical violations. There is evidence that moral disgust, while rooted in the same neural circuitry as pathogen disgust, can be modulated by cultural and educational factors (Chapman \u0026amp; Anderson, 2013).\u003c/p\u003e \u003cp\u003e Age-related differences, though relatively minor, highlight the potential for life-stage-specific modulation of disgust sensitivity. While less reactive to immediate pathogen cues, older adults may exhibit stronger moral disgust, reflecting the growing importance of social and ethical considerations in later life, as Rozin et al. (2009) suggested. This trend is evident in the RDA\u0026rsquo;s second axis, which links moral disgust with age and non-biological education fields.\u003c/p\u003e \u003cp\u003eAdditionally, the influence of education and the field of study is noteworthy and highlights the role of cognitive and cultural factors in shaping emotional responses. In our study, variability in disgust responses among biologists was more influenced by the stress of COVID-19. Subsequently, they ranked as more repulsive the pandemic-related disgust images compared to disgusting animals, possibly due to greater exposure to or awareness of pathogen-related risks. On the other hand, economically and technically educated respondents were more sensitive to moral disgust and ranked modern threats such as toxic or radioactive pollution as more disgusting. Respondents with a background in social sciences were more sensitive to core and pathogen disgust, in which case they ranked higher disgusting animals.\u003c/p\u003e \u003cp\u003eThe significant effect of stress of the COVID-19 pandemic, as measured by the C-19SS, further underscores the dynamic nature of disgust. Elevated stress levels heightened sensitivity to pathogen cues, reflecting the adaptive recalibration of emotional responses during heightened disease threats. This finding is consistent with research on the behavioural immune system, which posits that environmental pathogen prevalence modulates disgust sensitivity (Ackerman et al., 2018; Cepon-Robins et al., 2021).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides valuable insights into the complexities of disgust responses to various stimuli, including ancestral, modern, and pandemic-related threats. The results highlight distinct emotional reactions to these categories, with ancestral disgust stimuli such as spoiled food eliciting the strongest responses, whereas modern threats like pollution, radioactivity, and pandemic-related cues provoked weaker, more heterogeneous reactions. The factor analysis further revealed the internal variability within pandemic-related stimuli, with visible signs of infection (e.g., sneezing) generating stronger disgust compared to hospital environments or people wearing masks. Additionally, individual factors such as fear of spiders, moral disgust sensitivity, age, and educational background played significant roles in shaping disgust responses.\u003c/p\u003e \u003cp\u003eOur findings highlight the complexity of disgust as an emotion shaped by evolutionary history, cultural influences, and individual differences. The differential responses to ancestral, modern, and pandemic-related threats underscore the adaptability of disgust mechanisms in responding to novel threats, such as pandemics, while also revealing their limitations in addressing modern environmental challenges. By integrating evolutionary theory with contemporary data, we can better understand the interplay between ancestral mechanisms and modern challenges in shaping human emotional responses. By bridging theoretical perspectives with empirical evidence, this research contributes to a more comprehensive understanding of the adaptive and maladaptive aspects of disgust in the modern world.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFuture directions\u003c/h2\u003e \u003cp\u003eThe differentiation of pandemic-related disgust into subcategories offers a valuable framework for understanding the complexity of modern disease-related emotions including disgust, fear, and anxiety. For example, a study has shown that induced disgust increases self-reported anxiety across different types of stimuli, regardless of their relevance to disgust or fear (Davey et al., 2008). This interplay highlights the importance of understanding disgust not only as a standalone emotion but also as a component of broader emotional responses, particularly in the context of anxiety disorders. Thus, future research should explore the interplay between disgust and other emotions, such as fear and anxiety, in shaping responses to complex contemporary threats such as new pandemics. Future studies should also explore how cultural, temporal, and contextual factors shape responses to these stimuli. Additionally, longitudinal studies examining the impact of prolonged exposure to pandemic-related stimuli on disgust sensitivity could provide valuable insights into the dynamic nature of emotional responses and elucidate whether the observed patterns persist as societies adapt to pandemics and associated public health measures. Finally, the interaction between individual characteristics and disgust underscores the need for personalised health communication and intervention approaches. For instance, understanding the role of moral disgust in shaping attitudes toward public health measures could inform strategies to address vaccine hesitancy or compliance with hygiene practices.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out following the approval of the respective ethical committee (hidden for the review) and following the Declaration of Helsinki. All subjects provided informed consent with participation in the study and personal data processing. If a photograph taken for the test set contained a recognisable face, we obtained a signed written consent of the person in the picture (in his/her native language) to use it in the experiment and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Czech Science Foundation (GAČR) no. 22-13381S.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available in the supplementary material of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAar\u0026oslash;e, L., Petersen, M. B., \u0026amp; Arceneaux, K. (2017). 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Disgust and the insula: fMRI responses to pictures of mutilation and contamination. \u003cem\u003eNeuroreport\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(15), 2347-2351. https://doi.org/10.1097/00001756-200410250-00009\u003c/li\u003e\n\u003cli\u003eYang, Q., Yan, L., Luo, J., Li, A., Zhang, Y., Tian, X., \u0026amp; Zhang, D. (2013). Temporal dynamics of disgust and morality: an event-related potential study. \u003cem\u003ePLoS One\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(5), e65094. https://doi.org/10.1371/journal.pone.0065094\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":"evolutionary-psychological-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evop","sideBox":"Learn more about [Evolutionary Psychological Science](http://link.springer.com/journal/40806)","snPcode":"40806","submissionUrl":"https://submission.springernature.com/new-submission/40806/3","title":"Evolutionary Psychological Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5960947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5960947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDisgust is a fundamental emotion that evolved to protect organisms from pathogens and toxins, shaping behaviours critical for survival. This study explores how ancestral, modern, and pandemic-related visual stimuli elicit disgust. Specifically, our goal was to validate the categorisation of disgusting stimuli, evaluate the intensity of emotional responses, and determine the contribution of individual differences. A sample of 262 participants from diverse educational and professional backgrounds ranked 60 visual stimuli, including ancestral (spoiled food, bugs), modern (toxic substances, radioactivity), and pandemic-related (sneezing, masks, hospitals) categories, on perceived disgust (pictures of leaves were used as controls). They also completed assessments of pathogen, core, and moral disgust, along with pandemic-related behaviours and stress. Results revealed distinct clusters of disgust stimuli, with spoiled food evoking the strongest repulsion, while modern threats, such as pollution and radioactivity, elicited weaker disgust. Pandemic-related stimuli formed a unique category, with visible infection cues (e.g., sneezing) triggering stronger disgust than abstract cues like masks or hospital environments. The findings highlight the evolutionary roots of disgust and its adaptation to modern contexts. They underscore the need to consider cultural, individual, and situational factors in public health strategies and interventions targeting hygiene and disease prevention behaviours.\u003c/p\u003e","manuscriptTitle":"From bugs to sickness: disgust evaluation of ancestral, modern, and pandemic threats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-24 13:35:26","doi":"10.21203/rs.3.rs-5960947/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-28T01:15:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T00:02:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71399041330079302519231615524463337347","date":"2025-03-30T17:17:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-18T13:05:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277960931050197048668737846871165257664","date":"2025-02-12T16:24:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-12T14:45:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-12T13:43:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-12T13:42:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Evolutionary Psychological Science","date":"2025-02-04T21:33:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"evolutionary-psychological-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evop","sideBox":"Learn more about [Evolutionary Psychological Science](http://link.springer.com/journal/40806)","snPcode":"40806","submissionUrl":"https://submission.springernature.com/new-submission/40806/3","title":"Evolutionary Psychological Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"58d923c4-2d1e-477c-acc5-523471176b69","owner":[],"postedDate":"February 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T16:00:04+00:00","versionOfRecord":{"articleIdentity":"rs-5960947","link":"https://doi.org/10.1007/s40806-025-00442-6","journal":{"identity":"evolutionary-psychological-science","isVorOnly":false,"title":"Evolutionary Psychological Science"},"publishedOn":"2025-09-03 15:57:27","publishedOnDateReadable":"September 3rd, 2025"},"versionCreatedAt":"2025-02-24 13:35:26","video":"","vorDoi":"10.1007/s40806-025-00442-6","vorDoiUrl":"https://doi.org/10.1007/s40806-025-00442-6","workflowStages":[]},"version":"v1","identity":"rs-5960947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5960947","identity":"rs-5960947","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}