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HYPOTHESIS AND THEORY article

Front. Polit. Sci., 01 October 2025

Sec. Political Participation

Volume 7 - 2025 | https://doi.org/10.3389/fpos.2025.1570686

This article is part of the Research TopicA Reflection on Conceptions and Theories of Emotion across Three Disciplines: Psychology, Political Psychology and Political ScienceView all 3 articles

An Emotion System Theory to address gaps in Affective Intelligence Theory and conceptualization of emotional phenomena

  • Department of Psychology, Rutgers University, Camden, NJ, United States

How can emotions, and their influence on individuals and groups, be best conceptualized and studied by political scientists as well as by psychologists? Empirical work indicates that psychological theories, and research conducted by psychologists about emotions, are not only relevant but indeed important for understanding and predicting political phenomena. Building on a recent article by George Marcus, gaps are identified in Affective Intelligence Theory (AIT) and other theories of emotion that Marcus reviews, and an alternative, more comprehensive theory is delineated. The Emotion System Theory, here referred to as EST, encompasses a wider range of emotions and emotion-eliciting appraisals, and makes predictions differing from those in AIT about the determinants of political processes and specific political behaviors. For example, as distinct from AIT's focus primarily on enthusiasm, fear, and anger EST (a) defines and distinguishes among 16 positively- or negatively-valenced emotions plus the neutral-valenced emotion of surprise; (b) proposes five ways in which they can be measured; (c) specifies distinctive causes and components for each emotion; and (d) discusses their wide-ranging and often powerful impact e.g., on political information processing, communication (e.g., in campaigns and ads), candidate evaluation, voting, and various types of political participation. Together, these emotions constitute a coherent set of general-purpose response strategies for coping with crises and opportunities, within and outside of the political domain.

1 Introduction

Recent reviews (e.g., Gadarian and Brader, 2023; Redlawsk and Mattes, 2022; Redlawsk and Pierce, 2017) and empirical articles (e.g., Clifford et al., 2023; Cohen-Chen and Van Zomeren, 2018; Huddy et al., 2021; Roseman et al., 2020; Vasilopoulos et al., 2019) make clear that emotions are often manifest in political events and are important influences on political actors and processes. To cite just a few salient examples, consider the anger seen in the January 6, 2021 attack on the U.S. Capitol, with rioters beating police officers and threatening to kill government officials (Riley, 2022); the fear of becoming ill from COVID-19 that increased support for mask mandates among Republicans and decreased voting for Donald Trump among independents in 2020 (Mehlhaff et al., 2024); and the hope that fueled voting for Barack Obama in 2008 (Finn and Glaser, 2010).

In his ambitious target article, Marcus (2023) makes multiple contributions to advancing understanding of the nature of emotions in political science and psychology. These include affirming the importance of a comprehensive theory of emotions for understanding and predicting outcomes in political science; calling attention to the influence of multiple emotions, multiple intensities of emotions, and the multiple concurrent appraisals—each assessing a “strategically vital aspect” of situations—that elicit emotional responses; and proposing criteria for a comprehensive emotion theory. Moreover, in reviewing four theories of emotion, including Affective Intelligence Theory (AIT) which he has pioneered (e.g., Marcus et al., 2000), Marcus (2023), p. 10) is admirably modest, acknowledging that AIT is incomplete, and that more can and should be done to identify additional appraisals and emotions (see also Sirin and Villalobos, 2021) which might play a role in politics (following his counsel that a theory should be interested in “expanding its reach rather than defending its current formulation”).

In the sections that follow, I employ Marcus' (2023) criteria, phrased here as questions that can be asked of theories of emotions; identify gaps in the answers provided by the theories he reviews (especially AIT, in view of its extensive influence in political science); and compare the answers given by the Emotion System Theory (e.g., Roseman, 2011, 2013, 2018) which may fill some of these gaps. I conclude with comments on unanswered questions to be addressed by future research on emotions in political science.

According to Marcus' (2023) criteria, a theory of emotion should:

• Offer an explicit definition of emotion (Question, as per James, 1890: What is an emotion?)

• Offer a taxonomy of emotions (Question: What are the emotions?)

• Specify a measurement component (Question: How can emotions be measured?)

• Advance testable claims (Question: What are the causes and effects of emotions?)

• Be empirically falsifiable, in comparison to competing theories (Question: Has the theory been tested against alternatives?)

• Incorporate parallel processing (Question: Are multiple emotions and their elicitors processed simultaneously?)

• Integrate preconscious processing (Question: Can these emotions be elicited without reflective consciousness?)

Table 1 addresses each of these questions, briefly summarizing answers provided by EST and AIT, previewing what can be found in the body of this article.

Table 1
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Table 1. Some important differences between Affective Intelligence Theory and Emotion System Theory.

2 Definition: what is an emotion?

Although many, including Marcus (2023), lament a lack of consensus among specific authors, there is in fact some agreement among many contemporary emotion theorists and researchers (see e.g., Ellsworth, 2024b; Fontaine et al., 2013; Moors, 2022, Axis 1; Roseman, 2011; Scarantino, 2024) on the properties of emotions (despite considerable disagreement about causal mechanisms and emotion taxonomies; see, e.g., Moors, 2022, Axes 2–4). These show that the different definitions cited by Marcus (2023) are focusing on different aspects of emotions (components and causes vs. functions), rather than incompatibilities between the theories.

Figure 1
Chart showing hypothesized emotion-specific phenomenology, expressions, behaviors, and goals for 17 generally adaptive, other-directed, or self-directed emotions that are either hedonically positive (joy, relief, hope, love, pride), negative (fear, sadness, distress, frustration, disgust, interpersonal dislike, anger, contempt, regret, guilt, shame), or neutral (surprise). Also shown are the combinations of appraisals hypothesized to elicit each emotion; the coping strategy each emotion enacts; and the family of strategies to which each emotion belongs: contacting (moving toward something), distancing (moving away from something), rejection (moving something else away), attack (moving against something), or information-seeking (suspending movement to facilitate information-seeking).

Figure 1. Hypothesized structure of the emotion system, showing appraisals and some resulting emotional responses. Emotion components: PHE, Phenomenological; EXP, expressive; BEH, behavioral; EMV, emotivational goal. Strategies integrating the response components for each emotion are given in angle brackets. Appraisal combinations eliciting each emotion are found by proceeding outward from an emotion box to its borders around the chart. Adapted and revised from Roseman, I. J. Appraisal in the emotion system: Coherence in strategies for coping. Emotion Review, 5, 141–149. Copyright ©391242013 Sage Publications. doi: 10.1177/1754073912469591

In addition, contrary to a claim by Marcus (2023), p. 7), neither the Emotion System Theory nor the appraisal theories of Frijda (1986), Lazarus (1991), or Scherer (2005), maintain that emotions are only “subjective feelings expressed in consciousness.” Instead, as in these other models that view emotions as typically elicited by appraisals, the Emotion System Theory defines an emotion as a multicomponent construct including: (a) a subjective “phenomenological” component, encompassing emotion-specific thoughts and feelings; (b) a physiological component, comprising neural, chemical, and muscular responses; (c) an expressive component, identifying facial, vocal, postural, and verbal responses that communicate a person's emotional state; (d) a behavioral component, consisting of tendencies or readinesses to act (or refrain from acting) in particular ways when in particular emotion states in particular situations; and (e) an “emotivational” (Roseman, 2011) component which distinguishes goals that individuals or groups tend to pursue when in particular emotion states. Each of these components was also specified in what Kleinginna and Kleinginna (1981), after considering 92 possible emotion definitions, viewed as a “model definition” that includes “all traditionally significant aspects of emotion” (p. 345).

For example, on the level of the individual, fear may be characterized (in part, because full specification would exceed the space available here) by thoughts about some danger (Rosen and Schulkin, 1998); feeling aroused and cold (Scherer and Wallbott, 1994); patterns of neural activity in brain networks that include the cortical and medial amygdala, hypothalamus, bed nucleus of the stria terminalis, and brainstem (Orederu et al., 2024); a facial expression with brows raised and drawn together, widened eyes, and lips drawn back (Ekman, 2003); behaviors such as inhibition or flight (Blanchard et al., 2001); and the goal of getting to safety (Roseman et al., 1994b; cf. Marcus, 2023, “seek[ing] security”).

In contrast, anger may be characterized by thoughts about injustice and retaliation (Averill, 1982; Litvak et al., 2010); feeling aroused (Davitz, 1969), hot (Scherer and Wallbott, 1994), energized (Hackert et al., 2021), and ready to explode (Kövecses, 2010); neural activity in the brain's medial amygdala and periacqueductal gray (Potegal and Stemmler, 2010); a face with lowered, drawn-together, furrowed brows, and tightened, pressed-together or funneled lips (Matsumoto et al., 2008); a readiness to attack others verbally (Averill, 1982) or physically (Harmon-Jones, 2003); and the goal of compelling another person's action, by harming them if need be (Fessler, 2010; Roseman, 2024).

In political contexts, fear responses have been observed, for example, in connection with concerns about terrorist attacks (Huddy et al., 2003), support for policies such as mask mandates and stay-at-home orders in response to the possibility of becoming seriously ill from Covid-19 (Mehlhaff et al., 2024), and the goal of preventing some danger or removing oneself from danger (Gadarian and Brader, 2023). Anger responses have been observed in opposition to health care reform (Banks, 2014), support for military action (Huddy et al., 2007), and voting for anti-immigrant parties (Erisen and Vasilopoulou, 2022).

Note that EST maintains that there may be variation in an emotion's responses depending upon: (a) its specific elicitors on a given occasion; (b) its intensity; (c) situational affordances and constraints on action; and (d) the occurrence of other emotions, emotion regulation, and other non-emotional cognitive, motivational, or behavioral processes at the same time as the emotion (Roseman, 2011). Thus, in people (Blanchard et al., 2001) as in other animals, fear can prompt a variety of behaviors including remaining motionless, assessing risks, fleeing, hiding, or attacking-in-order-to-escape (depending on features of the situation, such as the imminence of the threat and the availability of an escape route or a place to hide).

For example, (a) the fear elicited by planes crashing into the World Trade Center will be processed by different sensory and brain mechanisms, and is likely to elicit different behavioral responses (e.g., running away; Strozier, 2011), than the fear of contracting a deadly virus such as COVID-19 (e.g., limiting social interactions; Luo et al., 2021); (b) The annoyance produced by traffic noise (Riedel et al., 2019) will likely differ in subjective intensity and behavior from the outrage elicited by deadly attacks on one's country, as in the bombing of Pearl Harbor (Schencking, 2022), or the murder of an ingroup member like George Floyd (Rothbart, 2021); (c) The extent of both Israelis' and Palestinians' hope for peace in 2017 was found to be dependent on each group's perception of the other's hope for peace in the situation (Leshem and Halperin, 2020); (d) Anger or its effects can be lessened by co-occurring hope (e.g., for fixing the immigration system; Walter et al., 2021) or thinking about non-emotional topics (Denson et al., 2012), and increased (Zillmann, 1988) or decreased (Pels and Kleinert, 2016) by physical exercise. Disgust responses can be reduced by hunger (Hoefling et al., 2009).

I have reviewed these examples to reply to claims that variability across instances undermines the scientific status of distinct emotions (e.g., Barrett, 2009). These claims ignore the functional comparability of different emotional responses in different situations. They may also reflect the view that an emotion is a “whole body” response (e.g., Wiens, 2005) which is identical each time it occurs.

However, that is manifestly false. People can experience anger, fear, and other emotions while watching campaign ads on television, voting, attending a rally, posting online, etc. (e.g., Redlawsk and Pierce, 2017; Reicher and Haslam, 2017; Van Troost et al., 2013), and the various activities can alter thoughts, feelings, expressions, behaviors, and goals at those times. Emotions are not fixed action patterns or “natural kinds” (Barrett, 2006) that have invariant properties across all occurrences. Rather, like most phenomena in science, there are probabilistic (beyond-chance) relationships between particular emotions and particular responses (Lazarus, 1991; Roseman, 2011). And unless, like Descartes, we adopt a dualistic view, then every variation in thought, feeling, and motivation—as well as behavior—will have a corresponding physical substrate. That makes finding a “signature” for a given emotion across all instances (Barrett, 2009) exceedingly difficult. But as one's focus widens from particular brain circuits or muscle movements to adaptive behavior patterns (Plutchik, 1984) or “emotivational goals” (Roseman, 2011)—such as getting out of danger in fear (Gadarian and Brader, 2023) or compelling others' actions in anger (Sell et al., 2009)—consistencies across instances are more apparent.

3 Taxonomy: what are the emotions?

Marcus (2023) rightly criticizes valence theories as inadequate insofar as distinguishing only positive vs. negative emotions “leaves neither theoretical nor empirical space for any other emotions” (p. 6). However, similar to EST, prominent emotion theories incorporating valence generally also include other dimensions (such as certainty, agency, and control) and more than two emotions (see Ellsworth, 2024a). Indeed, most contemporary theories encompass at least four emotions (happiness or joy; sadness; fear; and anger) and many theories add other EST emotions such as surprise, disgust, contempt, pride, love, and hope (e.g., Ekman, 2003; Fontaine et al., 2013; Keltner et al., 2022; Lazarus, 1991; Smith and Kirby, 2011). None of the aforementioned theories includes enthusiasm, though Redlawsk and Mattes (2022) note that the political science literature typically does not distinguish enthusiasm from hope.

Thus, an important question is: are there really only three emotions that are relevant to politics—enthusiasm, fear, and anger? The current formulation of AIT, which focuses on those states, is vulnerable to the same limitation that Marcus (2023) attributes to valence theories. Below I will discuss the elicitation and impact of each of the 10 emotions mentioned in the previous paragraph. The Emotion System Theory encompasses a total of 16 positive or negative emotions plus the neutral-valenced emotion of surprise. These can be found in Figure 1, which details some of their phenomenological, expressive, behavioral, and goal components, as well as hypothesized appraisal determinants, for each emotion.

Systematicity I: Why these emotions? Marcus (2023) reasonably asks for a comprehensive taxonomy, preferably one not based solely on emotion words (many of which have overlapping meanings and vary from one language to another)—with a rationale for inclusion or exclusion of candidate emotions. Two rationales can be given for the emotions shown in Figure 1.

The first is empirical: although various studies have focused on different sets of emotions, the aggregated body of research has shown differences between each of the Figure 1 emotions in multiple response components (e.g., Fontaine et al., 2013; Keltner et al., 2022; Roseman et al., 1994a,b; Saarimäki et al., 2016; Tracy et al., 2007; Volynets et al., 2020) and appraisal elicitors (e.g., Ellsworth and Smith, 1988; Fontaine et al., 2013; Roseman, 1991; Smith and Ellsworth, 1985; Tong, 2010, 2015).

The second rationale is functional. Though Marcus (2023), p. 7) criticizes the taxonomies of appraisal theories as “largely ad hoc”, the emotions in Figure 1 comprise a coherent repertoire of alternative strategies for coping with the general types of opportunities and crises that people encounter (Roseman, 2013). As shown within the angle brackets at the bottom of each cell in Figure 1, these emotions involve either some form of movement, preparing movement, suspending movement, or ceasing movement; moving toward, moving away, moving something else away, or moving against something; and moving with reference to objects and events, other persons, or the self. Respectively, these function to cope with crises and opportunities that emotion-eliciting appraisals indicate are at hand, imminent, evolving, or ending; in which a typically adaptive response in the situation would be to maximize, minimize, eliminate, or change something; about the world, other persons, or the self (Roseman, 2011).

For example, surprise (an emotion not differentiated in AIT but whose elicitors and responses have been detailed by Ekman et al., 1972; Fontaine et al., 2013; Horstmann and Schützwohl, 2024; and others) involves suspending movement and processing information in order to understand unexpected events (Reisenzein et al., 2019).

The hedonically positive emotions involve different ways of getting more of something (Tolman, 1923). Joy gets more of rewarding outcomes by moving toward them (e.g., increasing contact and engagement with them; see Fredrickson, 2013; Frijda, 1986). Relief enables getting more of something by ceasing to move away from situations which might have been more aversive but in fact are not (e.g., by resting or relaxing; Lazarus, 1991; Roseman et al., 2013). Hope increases the likely experience of positive outcomes by preparing to move toward (approach) or to stop moving away from them (Roseman, 2013; Roseman et al., 2013). Love gets more by moving toward, being with, or forming a relationship with other people who cause positive outcomes (Shaver et al., 1987). Pride gets more by moving toward the self e.g., by exhibiting and asserting oneself or one's group (Weisfeld and Dillon, 2012).

The negative emotions involve different ways of getting less of something (Tolman, 1923), which, in answer to a question raised by Marcus (2023), is the unifying attribute underlying the class of negative emotions. The emotion of distress gets less of something aversive by moving away from it (e.g., Batson et al., 1987). Sadness gets less of losses, or failures to attain rewarding outcomes, by ceasing pursuit of unattainable rewards (reducing movement toward them; see Gadassi-Polack et al., 2024). Fear gets less of negative outcomes (whether aversive events or losses) by preparing people to move away from or to stop moving toward them (Lerner and Keltner, 2001; Mobbs et al., 2015). Frustration gets less by increasing effort to force change in negative outcomes (Amsel, 1992). Disgust rejects things that are intrinsically negative (Rozin et al., 2016). Interpersonal dislike, anger, and contempt (respectively) get less of negative outcomes caused by other people by moving away from (e.g., minimizing time spent with) those people (Roseman, 2024; Steele, 2020), attacking them (Frijda et al., 1989), or moving them away (e.g., breaking relationships with them, excluding them from interactions; Fischer and Roseman, 2007). Regret, guilt, and shame get less of negative outcomes appraised as caused by the self, by moving away from those outcomes (e.g., resolving not to repeat them; Zeelenberg and Pieters, 2006), attacking (e.g., reproaching) the self (Karlsson and Sjöberg, 2009), or moving the self away (e.g., concealing the self; Tangney et al., 1996).

Let us consider some examples of these emotions, functioning as described, in political contexts.

Cohen-Chen and Van Zomeren (2018) manipulated hope that gun control measures could be implemented, which increased willingness to sign a petition, participate in a demonstration, and contribute money; lowering hope decreased willingness (see also Cohen-Chen et al., 2014).

• Analyses by Sabti and Ramalu (2024) showed that psychological distress mediated the impact of such factors as financial difficulties and political instability on Iraqi doctors' intentions to leave their home country (see also examples of distress migration in other countries in Avis, 2017).

Sadness was identified in posts from some Parler users when the events of January 6, 2021 did not result in Trump remaining in office (Norgaard and Walbert, 2023; anger, particularly at Mike Pence, was also observed). Using survey data from 23 European countries and Israel, Landwehr and Ojeda (2021) found that symptoms of depression were associated with decreased voting and physical political participation, such as working for a political group and demonstrating in public (but see de León and Trilling, 2021; Weber, 2013).

Disgust sensitivity is associated with opposition to immigration (Clifford et al., 2023; Karinen et al., 2019), and inducing this emotion increases rejection of gay marriage (Adams et al., 2014).

Mattes et al. (2018) found that contempt was the emotion viewers perceived most in two broadcast ads attacking either Hillary Clinton or Donald Trump in the 2016 U.S. presidential campaign. Redlawsk et al. (2018) reported that contempt felt by voters toward either Ted Cruz, Marco Rubio, or Donald Trump predicted decreased probability of voting for that candidate in the 2020 Iowa GOP caucus. Roseman et al. (2020) found that contempt was the emotion most strongly predicting voting against three of the four candidates in two U.S. Senate races (looking down on and breaking relations with them).

These studies provide examples of how adding hope, distress, sadness, disgust, and contempt to the emotions in AIT can help account for politically-relevant outcomes. Examples demonstrating the politically-relevant impact of pride, love, guilt, and surprise are discussed below.

The systematic nature of the taxonomy of emotions in Figure 1 can also be seen from the identification of families of emotions (Roseman, 2011), each having members that are specialized to cope with crises or opportunities caused by other persons (shown in the middle third of the chart) or the self (shown in the bottom third), in addition to those whose responses tend to be adaptive regardless of who or what was the cause (shown in the top third of the chart).

Emotions in the contacting family (shown in green in Figure 1) move people toward the events or persons that elicited them, e.g., increasing contact and interaction. Emotions in the distancing family (shown in blue) move people away from their elicitors, decreasing contact and interaction. Attack family emotions (shown in red) move against their elicitors, “contending” with them (Arnold, 1960), i.e., trying to force them to change. Rejection family emotions (shown in brown) move their elicitors away from the self.

The other-person-directed and self- (or own group-) directed emotions in the bottom two thirds of the chart can be understood as specialized members of those families that have evolved because different responses are likely to be adaptive in dealing with animate emotion elicitors. One can cope with animate sources of crises or opportunities through their motives (goals and preferences), which can be fulfilled or thwarted, and their hedonic feelings (i.e., they can be made to feel good or bad).

For example, the emotion of love (affection) moves toward other persons in social space, enhancing interpersonal closeness and connectedness, and forming relationships which increase the likelihood that individuals and groups with whom affection is shared will continue to be sources of positive outcomes. In politics this can be manifest in expressions of support and devotion of followers for a leader, as seen in the affection felt by some supporters of Donald Trump (Warner, 2024) who report completely unqualified warmth/favorability on feeling thermometer scales (Crowell et al., 2018), or wait on line for hours to attend his rallies (Justice, 2020), gratifying his desire for approval (Kohn, 2021). Using ANES data, McDonald (2023) shows that presidential candidates perceived as more compassionate (caring about people like you) got more votes in elections from 1992 to 2020.

Similarly, anger typically moves against other people in ways suited for dealing with animate sources of negative outcomes. These include inflicting physical pain, taking away benefits, and blocking the target's objectives. For example, anger felt toward candidates may be manifest in voting against them (Finn and Glaser, 2010) or working against them in campaigns (Valentino et al., 2011); and populist anger felt toward immigrants predicts support for anti-immigrant candidates and policies (e.g., Rico et al., 2017; Vasilopoulos et al., 2019). Anger felt toward people who have committed crimes motivates passage of laws mandating longer prison sentences and capital punishment (Johnson, 2009); and anger felt toward suspected terrorists underlies harsh interrogations (Carlsmith and Sood, 2009).

Outcomes seen as self-caused elicit intrapersonal ways of moving toward, away, or against. In pride, the subject self (the “I” or “we”) is moved closer to the object self (the “me” or “us”) e.g., by thinking about or displaying one's own individual or group opinions, characteristics, and accomplishments, or asserting dominance (Dickens and Robins, 2022). These are adaptive in that feeling pride enhances perseverance (Williams and DeSteno, 2008), effort, and perceived self-efficacy (Verbeke et al., 2004), and displaying pride (e.g., in parades, or by wearing political buttons or clothing declaring one's group membership or views; Corrigan and Brader, 2011; Graham et al., 2021; Peterson et al., 2018; Weisfeld and Dillon, 2012) is relatively likely to elicit positive responses from in-group members (Mercadante et al., 2021, “authentic” rather than “hubristic” pride). (Panagopoulos 2010) found that a manipulation which increases pride (informing high-propensity voters that their names would be published in a local newspaper) increased turnout in two midwestern cities. Asserting individual or in-group preferences is more likely to extend success when positive outcomes are self-caused.

In guilt, one attacks the self or one's own group in ways suited to negative outcomes which I or we have caused, e.g., via motivating self-criticism or reparative behavior that imposes costs or restrictions. For example, Dutch research participants induced to feel collective guilt about mistreatment of Indonesians (during the colonial period) endorsed giving monetary compensation, and the amount was mediated by collective guilt (Doosje et al., 1998; cf. Barkan, 2001). Guilt among priests who have sexually abused minors can prompt self-punitive behavior (Plante, 1999); and in response to clergy sexual abuse, the Catholic church undertook some reforms to prevent recurrences (Dokecki, 2004).

To account for these phenomena, emotions whose responses are specialized for dealing with other persons or the self, such as love, anger, contempt, pride, and guilt—together with their characteristic responses and effects—should be included in comprehensive models of the impact of emotions in politics. All of these emotions are included in the theories of Scherer (Fontaine et al., 2013) and (with the exception of contempt) Lazarus (1991), though some appraisal antecedents and response components differ from theory to theory (see, e.g., Roseman et al., 1990; Roseman, 2024 for details).

The emotion of surprise should also be accorded a prominent place in theories within political science and psychology, given its key role in information processing and the centrality of information processing in our understanding of important political phenomena including opinion formation, candidate evaluation, and voting (e.g., Jerit and Kam, 2023; Lau and Redlawsk, 2006; Taber and Young, 2013).

Surprise has: distinctive phenomenology—thoughts about discrepancies, and a feeling of mental interruption or interference (Reisenzein, 2000); typical physiology involving coordinated activity in eight brain networks (Zhang and Rosenberg, 2024) along with skin conductance increase, heart rate deceleration, and pupil dilation (Reisenzein et al., 2019); a prototypical facial expression with raised, arched brows, widened eyes, and open, slack-jawed mouth (Ekman, 2003); and behavioral manifestations including pausing action and attending to the eliciting event, with a goal of understanding its nature and causes (Reisenzein et al., 2019).

I noted above that the Emotion System Theory conceptualizes each emotion as a response “strategy.” These are typically not conscious plans undertaken to achieve objectives, but rather syndromes of response that have been shaped over the course of evolution to cope adaptively with particular types of situations when they are encountered. The various responses of surprise have coevolved to cope with the occurrence of something unexpected, by interrupting behavior and processing information in order to enable the modification of knowledge, opinions, actions, plans, and goals.

Each of the responses in an emotion syndrome contributes to implementing that emotion's strategy. For example, in surprise, thoughts about discrepancies from existing schemas and the feeling of mental interference contribute to revising thoughts and actions that may be outdated in light of the unexpected eliciting information. The widened eyes and opened mouth of the surprise expression enhance information intake, and pausing behavior enables reallocation of attention to this information. Information processing aimed at understanding the surprising event helps people change their models of the specific situation (and at times the larger world) and move on from prior beliefs and actions.

Thus, it seems that the emotion of surprise has many of the characteristics, effects, and functions which AIT ascribes to fear. Contrary to AIT's emphasis on fear, it is surprise that more consistently results from unexpected events (Reisenzein et al., 2019; Roseman et al., 1996), reduces reliance on existing beliefs and action patterns, prompts a search for new understanding, and facilitates revision of plans and behaviors (e.g., Loewenstein, 2019). It is surprise which, from the moment of its onset, opens individuals and groups to new information (e.g., Reisenzein et al., 2019), reflective deliberation (Johnston et al., 2015), learning (e.g., Brod et al., 2018), and persuasion (e.g., Petty et al., 2001).

Although the emotion of surprise has received little attention from political psychologists (Gadarian and Brader, 2023)—likely because its place has been occupied by fear—a careful reading of the literature shows its influence and importance. Surprising political events, such as the September 11, 2001 attacks, or the 2016 electoral defeat of Hillary Clinton by Donald Trump, can lead to dramatic disruptions of extant beliefs, actions, and plans, initiating immediate and sometimes prolonged information search, analysis, and consideration of changes in traditional practices and standard operating procedures (see e.g., Kennedy et al., 2018; Wirtz, 2006).

How then should we understand fear? Differentially characteristic responses of fear are shown in Figure 1 and some were discussed above. Unlike surprise, fear focuses especially on thoughts about threat or danger. The widened eyes and flared nostrils of the characteristic fear expression increase intake of both visual and olfactory information about threats that can help locate their sources (Lee and Anderson, 2018; Susskind et al., 2008). That is, the increased vigilance that is part of the fear syndrome particularly involves a search for threats. Unlike surprise, the primary emotivational goal in fear is to get to safety, prevent a potential danger from actually occurring. Thus, inducing fear leads individuals and groups to take actions to remove a danger or remove the self from danger (Gadarian and Brader, 2023), whether the danger involves contracting a deadly virus (Harper et al., 2021), falling victim to a terrorist attack (Gadarian, 2010), or incurring an electoral defeat (Valentino et al., 2018).

Note that according to the Emotion System Theory, the influences of the behavioral and emotivational components of an emotion are distinct and complementary. The behavioral component consists of situation-keyed action readinesses and tendencies (Frijda, 1986), such as stilling behavior in surprise (Camras et al., 2002), and freezing, running, or defensive fighting in fear (Blanchard, 2023). Recognizing the role of the behavioral component incorporates the traditional view of emotions as involving relatively short-term impulsive action (see e.g., Cannon, 1939; Eben et al., 2020) in which felt urgency is the hallmark of emotion (Frijda, 1986, 2010).

The emotivational component reflects a newer view of emotions as including flexible instrumental behavior aimed at attaining emotion-specific goals. The term “emotivational” (Roseman, 1984, 2011; Fischer and Roseman, 2007) was introduced to distinguish goals that are part of an emotion from other (“motivational”) goals (such as hunger, sexual drive, or need for achievement), that are not emotion components, but can, when events are consistent or inconsistent with them, contribute to eliciting emotions (e.g., Lazarus, 1991; Scherer, 2001). As with other goals, there may be an infinite number of ways that an emotivational goal can be instrumentally pursued, and potentially over a long time frame. For example, to understand a surprising electoral defeat, one could interview campaign strategists, examine survey findings, establish commissions of inquiry, etc. (e.g., Masket, 2020; Sides et al., 2017). To seek safety when afraid of contracting a deadly virus, one could wear masks, keep from close contact with others, or vote for a candidate—from one's own party or the opposing party—who seems most likely to take needed measures to protect the public (Lebow, 2024; Shino and Smith, 2021).

The emotivational component is proposed to have a greater influence on emotional behavior when there is time to weigh the consequences of alternative actions. The faster that motive-relevant events are changing, and the larger and more important those changes are, the more intense a resulting emotion is likely to be, and the more that control over behavior is likely to shift from the flexibility of emotivational goal pursuit to the more established action readinesses and tendencies of the behavioral component (Roseman, 2008, 2011). Action tendencies in an emotion's behavioral component tend to be responses that evolution or experience have indicated are likely to attain an emotion's emotivational goal across situations. For example, pausing behavior and looking around and listening, are evolution-shaped ways of seeking understanding of surprising events (Reisenzein et al., 2019). The behaviors of freezing, flight, and physical defensive fighting are evolved ways of reducing danger (Mobbs et al., 2015).

Thus, as long as there is sufficient time to reflect (e.g., when danger is not too imminent and fear not too intense), reflective deliberation can indeed be engendered by fear, as AIT proposes—with a focus on increasing safety (e.g., Lerner and Keltner, 2001). Reflective deliberation can also be engendered by hope—with a focus on making a desirable event happen (as when hope motivates a consideration of actions that can be taken to address climate change; Feldman and Hart, 2018). Or by anger—with a focus on compelling a change in the target's behavior (e.g., when an offended political leader considers how best to get revenge, so that offenses will not be repeated; Arnsdorf et al., 2023; Scheff, 2019).

Behaviors when one feels intensely afraid are not usually calmly considered and deliberatively chosen. Mobbs et al. (2015) describe in detail how, as a threat becomes more imminent, attention is narrowed and behavior options are increasingly constrained. If the danger moves from merely potential to actually occurring and a “danger threshold” is breached, then species-typical defensive responses of freezing, fleeing, or fighting may be elicited (p. 7). Blanchard et al. (2001) find human analogs to these behaviors that were initially observed in rats. LeDoux and Daw (2018), who explicitly allow for “choices” and instrumental defensive behaviors in what corresponds to fear, list nonconscious and conscious deliberation as only two of six processes that influence action in their taxonomy of defensive behavior. Indeed, “it is suggested that slow and computationally laborious deliberation is best suited to situations requiring prevention and avoidance when the agent is fairly safe” (p. 273).

As reviewed by Redlawsk and Mattes (2022): Jepson and Chaiken (1990) found that fear reduced participants' systematic and elaborative processing of persuasive messages; Keinan (1987) found that threats reduced consideration of alternative answers to test questions; and Cheung-Blunden and Ju (2016) found that anxiety impaired recall of information about cyberattacks. Based on this and related work, Redlawsk and Mattes (2022, p. 144) conclude that “Taken together, these studies mostly question the link between anxiety and information search.”

Thus, the unique connection that AIT posits between the emotion of fear and “reflective deliberation” (MacKuen et al., 2010; Marcus, 2023) can be challenged. Open, relatively unbiased information search and information processing is more characteristic of surprise than of fear. Consistent with this view, Johnston et al. (2015) found that, in contrast to AIT predictions, expectancy-disconfirming emotions (including enthusiasm felt toward presidential candidates from the opposing party and anger toward candidates from the party with which voters identified) increased reflective deliberation and reduced the impact of partisanship on voting in presidential elections from 1980 to 2004, while anxiety felt toward outparty candidates increased partisan voting.

4 How can emotions be measured?

Multicomponent theories (e.g., Kleinginna and Kleinginna, 1981; Izard, 2007; Scherer, 2005), including the Emotion System Theory, offer multiple ways to measure emotions. Insofar as the taxonomy in Figure 1 differentiates emotions that are distinct from each other in typical phenomenology, expressions, behaviors, and goals, each of those response types can be employed in emotion measurement. For example, Table 2 provides for researchers three ways in which each of the EST emotions could be measured (i.e., by emotion phenomenology, behaviors, or goals).

Table 2
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Table 2. Three ways to measure EST emotions, with political examples (examples not to be included in the measures).

To date, most research in political science as well as psychology depends on self-reports (phenomenology) to measure emotions. Examples include the ANES feeling thermometers and affect batteries (American National Election Studies, 2021), the Differential Emotions Scale (Izard et al., 1974), and the revised PANAS-X (Watson and Clark, 1994). Although these measures rely on verbal labels of emotions, reports of body feelings could also be employed, as evidence suggests that these are universal (Volynets et al., 2020).

The measurement of emotivational goals (what individuals or groups want when experiencing an emotion) are similarly most readily accessible through self-report, though goals could also be inferred from patterns of action over time (see Frijda et al., 1989). For example, anger might be inferred from a political leader's repeated attempts to get back at those perceived to have treated him unfairly or humiliated him (Breeze, 2020; Zlotnik, 2003).

Another approach is measuring emotions by the words that people use, as in the speeches of political leaders (Matsumoto et al., 2013). Indeed, researchers have developed dictionaries to code emotions from political communications (e.g., Boyd et al., 2022; Kahn et al., 2007). For example, contempt can be inferred from the belittling insults and exclusionary rhetoric in a leader's tweets and speeches (Ali, 2019; Quealy, 2021). Sentiment analysis of social media posts has been employed to assess positive or negative reactions of the public during a political campaign (Ceron et al., 2015).

Emotions can also be measured physiologically. There is now evidence that at least some of the emotions in Figure 1, such as surprise, happiness, sadness, fear, anger, and disgust, consistently differ from others in brain physiology (see, e.g., Engen and Singer, 2013; Friedman and Thayer, 2024; Harris and Fiske, 2006; Saarimäki et al., 2016), and/or peripheral responses such as heart rate and blood pressure variables (e.g., Kreibig, 2010, 2014; Lench et al., 2011; Stemmler, 2010); but see also Behnke et al. (2022), Lang (2014), and Quigley and Barrett (2014).

Expressions, especially facial muscle movements, have been widely used to measure emotions. Facial expressions have been used to assess the emotions of political leaders (Matsumoto et al., 2014) and candidates in debates (Rodríguez-Fuertes et al., 2022) as well as the emotions of debate audiences (Fridkin et al., 2021).

Accuracy in measurement is complicated by the conceptualization of emotions as syndromes of response (Averill, 1980) in which the presence of particular syndrome constituents may not be essential every time the emotion is present. For example, Reisenzein et al. (2019) report that the characteristic facial expression of surprise is present in only 10%−30% of tested situations in which participants report feeling surprise, and there are also low correlations between other felt emotions and their expressions. It seems clear that people don't make a facial expression each time they feel an emotion; and people may also simulate expressions, more or less well, in the absence of actually feeling an emotion, as in smiles shown by people who are not feeling happy (Ekman and Friesen, 1982). However, there is cross-cultural agreement far beyond chance in the emotion-specific facial, vocal, and postural responses people make when they do express surprise, happiness, sadness, fear, anger, and other emotions (e.g., Cordaro et al., 2018; Coulson, 2004; Elfenbein et al., 2007; Sauter et al., 2015; Shiota, 2024; Volynets et al., 2020), and in the specific emotions inferred from those expressions (e.g., Elfenbein and Ambady, 2002); for a cautionary view, see Sauter and Russell, 2024).

When measuring emotions (e.g., employing Table 2) it is important to remember that the same emotion may be manifest in different responses in different situations, as discussed above (cf. Barrett, 2009), and different emotions manifest in similar responses (Tomkins, 1962). In both cases, combining the multiple response components characterizing a given emotion in Figure 1 may allow researchers to triangulate on a person's actual emotion state (the more characteristic components that are present, the more likely it is that the corresponding emotion is occurring). Obtaining evidence on the emotivational component—the main goal(s) being pursued at a particular time—would be especially helpful in identifying the emotion(s) occurring at that time and predicting likely responses. For example, if a demographic group is choosing which party to vote for in order to avert an undesirable outcome (a protection goal, such as avoiding erosion of group members' economic prospects) that they've been warning could occur (a vigilance behavior, as in watching out for signs of inflation), then they are likely being influenced by fear. Feeling fear could lead them to defect from their traditional party, or to stick with it, depending on which they think would better achieve protection. If instead a group is voting against a candidate or party (a political attack behavior) to get back at them for some perceived harm (a revenge goal), then that group is likely being influenced by anger. Feeling anger could also lead to defecting from or sticking with their historically affiliated party, depending on who is blamed for the perceived harm, and whether defection or its opposite is perceived as more effective at forcing a change in the target's actions (cf. Bol and Verthé, 2019). Thus, the Emotion System Theory's multicomponent definition, and its specification of characteristic responses distinguishing one emotion from another, offer multiple ways to operationalize emotions that can satisfy Marcus' (2023) measurement criterion.

5 Testable causal claims: what are the causes and effects of emotions?

Consistent with its identification of only three principal emotions, AIT posits just three fundamental appraisals that elicit these responses: an appraisal of failure…success underlies the emotion of enthusiasm, familiarity…novelty/uncertainty underlies fear, and norm compliance…norm violation underlies anger.1

In the Emotion System Theory, particular emotions are held to be elicited by a combination of appraisals, which interact to determine which emotion or emotions will result. Since their first modern incarnation, all prominent appraisal theories (e.g., Arnold, 1960; Lazarus, 1991; Roseman, 2013; Scherer, 2009; Smith and Kirby, 2011) agree that particular emotions are elicited by combinations of appraisals.

In Figure 1, emotion-eliciting appraisals are found by moving outward from each emotion to its borders around the chart. For example, it is hypothesized that hope is elicited by appraisals that (a) an outcome is consistent with a person's motives (goals or preferences) and (b) uncertain. Fear results from appraisals that (a) an outcome is inconsistent with a person's motives, and (b) uncertain, when (c) the person feeling the emotion perceives that they have relatively low control potential. Anger results from appraisals that (a) an outcome is inconsistent with a person's motives, and (b) caused by another person or persons, when (c) the person feeling the emotion perceives relatively high control potential and (d) the situation is appraised as an instrumental problem (rather than an intrinsic problem). Where Figure 1 shows more than one appraisal value for a particular emotion, this indicates that the emotion can be felt regardless of the value on that appraisal. For example, surprise can be felt in response to unexpected events whether they are consistent or inconsistent with a person's motives.

The Emotion System Theory includes analogs to AIT's appraisals, but with differences in conceptualization, the number of appraisals identified, and the testable causal claims made.

First, EST's appraisal of motive-inconsistency…motive-consistency is similar to AIT's dimension of failure…success. But whereas AIT focuses this appraisal on “reward-seeking actions” (Marcus, 2023), the Emotion System Theory maintains that virtually anything can be appraised as wanted or unwanted by a person or a group (e.g., success or failure in obtaining something rewarding, such as candidate's electoral victory, or in reducing something aversive, such as violent crime). And whereas in AIT, this appraisal is held to influence “levels of enthusiasm” (Marcus, 2023), in the Emotion System Theory, the motive-inconsistent…motive consistent appraisal contributes to distinguishing all negative emotions from all positive emotions.

Indeed, most theories of emotions in psychology (in contrast to AIT) recognize a distinction between groups of hedonically negative and hedonically positive emotions (e.g., Arnold, 1960; Ekman, 2003; Fontaine et al., 2013; Izard, 2007; Lazarus, 1991; Ortony et al., 2022; Russell, 2003; Smith and Kirby, 2011; Tomkins, 1962, 1963; Wundt, 1902). Empirically, this is supported by data from numerous studies which find emotions or their component responses differentiated, in similarity or co-occurrence data, into negative vs. positive emotion groups (e.g., Abelson et al., 1982; Cacioppo et al., 2004; Gillioz et al., 2016), with this distinction, and its underlying appraisal, typically the factor accounting for the largest share of variance.

Second, the Emotion System Theory's appraisal distinguishing certain vs. uncertain outcomes is similar but not identical to the familiar…novel/uncertain dimension which in AIT determines “levels of fear.” Marcus (2023) sometimes also conceptualizes the high end of this dimension as “the novel and the unexpected” (p. 4). However, it is questionable whether novelty, uncertainty, and unexpectedness are synonymous, and the Emotion System Theory distinguishes among these three perceptions, according them different roles in the causation of emotions. Uncertainty (as opposed to certainty) is proposed as contributing to the elicitation of fear (as in AIT), but also of hope, in combination with other appraisals (Figure 1). As discussed above, in contrast to AIT, unexpectedness is proposed to elicit surprise, and this is empirically supported by multiple carefully conducted studies (reviewed by Reisenzein et al., 2019).

Novelty is not shown in Figure 1 because it is not proposed to distinguish one emotion from another. Rather, the Emotion System Theory cites motive-relevant change as affecting all emotions (Roseman, 2008). If there is no perception or change of relevance to motives in a situation, then no emotion would be felt. Frijda (2007) states this clearly and provides evidence for the “Law of Change”: emotions are elicited by changes in favorable or unfavorable conditions, rather than by the mere presence of those conditions; and greater change produces more intense emotions.

Third, the Emotion System Theory proposes control potential—one's perceived ability to do something about a motive-inconsistent outcome—as a crucial contributor to anger, whereas AIT proposes norm violation (cf. Scherer, 2001). Consistent with AIT, research findings have supported the link between anger and appraisals of being unjustly or unfairly treated (e.g., Averill, 1982; Kuppens et al., 2003; Tong, 2010). An earlier version of the Emotion System Theory assigned this role to an appraisal of “legitimacy” (whether a negative or a positive outcome was deserved; Roseman, 1984). But empirical results (reported in Roseman, 1991) found that ratings of deservingness failed to distinguish distancing emotions such as distress and sorrow (sadness) from attack emotions such as anger, as had been predicted. These findings, along with other empirical results and theoretical considerations, led to reconceptualizing this appraisal as low vs. high control potential (Roseman et al., 1996).

Why prefer control potential over norm compliance as a determinant of emotions such as anger? For one thing, appraisals of legitimacy or norm violation seem too cognitively complex to account for the appearance of anger-related responses in animals (e.g., Blanchard and Blanchard, 2003; Panksepp, 2004) or in four-month-old infants (Lewis et al., 1990). In contrast, appraisals of control potential (or “efficacy,” as it is often studied) are comparatively simple, requiring no understanding of social norms or deservingness. For example, Lewis et al. (1990) found that the infants showed anger-related facial expressions after they'd been trained to be able to control a desirable event, and then had that contingency extinguished. Osgood et al. (1957) established that perceptions of “potency” (from weak to strong) are universal in judging any object, person, or group. The Semantic Differential scale measuring the dimensions of evaluation, potency, and activity has been used, for example, in studies of how people perceive political parties (Petrenko et al., 1995), illegal immigrants (Short and Magaña, 2002), and nations (Smith et al., 1990). If one individual or group is strong relative to another, then they have the potential to control or do something about actions or outcomes the latter causes.

Moreover, a number of empirical findings support the connection between control potential and anger. For example, data relevant to the J-curve theory of revolutions (Davies, 1962) indicate that protests and rebellions tend to occur when a sudden downturn in outcomes follows a period of improvement (which may suggest that change, and thus some control, is possible). Two studies by Lemay et al. (2012) found that appraisals of control were significantly correlated with the intensity of felt anger. When viewing photographs of the September 11 attacks, Americans' perceived group efficacy predicted their feeling of anger (Cheung-Blunden and Blunden, 2008). A measure of “internal efficacy” (believing you are able to understand political events) predicted feeling angry at both Bill Clinton and George Bush in the 1992 presidential election (Valentino et al., 2009). Tausch and Becker (2013) found that students' perceived group efficacy in preventing tuition increases predicted the intensity of their felt anger.

Appraisals of norm violation and control potential are often correlated. One way to understand this is to see norm violation as a distal determinant, which often affects anger through the proximal influence of control potential (Roseman, 2018). In a highly cited publication, French and Raven (1959) proposed that having justice on one's side confers what they called “legitimate power.” Having justice on one's side helps to put a person into a position of relative strength: confronting those who have inflicted harm with evidence that they've behaved unjustly (violating norms) may get them to agree to alter their behavior. In addition, other individuals or groups, perceiving injustices, may support an injured party in compelling an offender to change behavior. In contrast, if one deserves a negative event, one has less potential to influence the harm-doer or recruit support from others.

Lerner (2015) describes how people develop the belief that they will, most of the time, get what they deserve, and presents much evidence for the prevalence of such “just world” beliefs. The widespread conviction that wrongs will ultimately be righted shows that encountering injustice can promote a sense of control potential. In the political domain, Huddy (2013) concludes that the perceived strength of one's group “includes a sense of moral strength” (p. 756; cf. Mackie et al., 2000).

Systematicity II: Why are these appraisals crucial causes of the emotions in the Emotion System? In addition to empirical support, the Emotion System Theory offers functional explanations for the role of each appraisal in eliciting emotions.

The perception of unexpectedness indicates that one's current understanding is inadequate in some respect, and therefore behavior based on that understanding may be inappropriate. It is consistent with this view that unexpectedness functions as the elicitor of surprise, which seeks information that enables revision of mental models and behavior (as when Israelis sought to understand the unexpected October 7 Hamas attacks and considered changes in their beliefs and behaviors; Hitman, 2024).

An appraisal of motive-consistency flexibly assesses current adaptive value. If something is consistent with one's current motives (goals and preferences), then eliciting one or more positive emotions, which function to get more of it, can maximize its benefits. If something is motive-inconsistent, then eliciting one or more negative emotions, which function to get less of it, can minimize harm. For example, if the benefits of the Medicare Drug Price Negotiation Program are appraised to exceed its costs, positive emotions such as hope or pride would tend to increase the likelihood of its continued implementation; if the costs are seen as higher, negative emotions such as fear or anger would tend to decrease that likelihood (Tollen, 2024).

According to the Emotion System Theory, having an appraisal of whether a situation involves increasing and decreasing reward-seeking vs. punishment-avoiding motives allows the prioritization of coping with more urgent concerns, consistent with findings that negative events have greater impact than positive events (Baumeister et al., 2001). For example, as shown in Figure 1, prolonged heat waves or market crashes would give rise to distress (when they occur) and relief (when they abate) and more urgent behavioral changes; whereas runs of pleasant weather and market rallies would elicit joy when (when they occur) and sadness (when they subside), and comparatively less urgent behavioral effects.

The Emotion System Theory conceptualizes hope and fear as “proactive” emotions (preparing for action), influenced by appraisals of uncertainty about whether potentially motive-consistent (or motive-inconsistent) outcomes will actually occur. In contrast, joy, relief, sadness, and distress are seen as “reactive” emotions, coping with motive-inconsistent or motive-consistent events appraised as certain to occur (including outcomes that are occurring now or have already taken place). Functionally, if an event is uncertain, it is prudent to prepare but perhaps not yet react as one would ultimately. If the event is certain to occur or already happening, then reacting immediately may be more adaptive. For example, Casas and Williams (2019) reported that images from the Black Lives Matter protests which evoked fear (e.g., images of police violence) were retweeted more than images evoking sadness (e.g., memorials to protesters who died). In this case, images suggesting the possible recurrence of police violence may motivate people to prepare for that eventuality together with retweet recipients, whereas the certainty of a protester's death is comparatively likely to lead to acceptance of the loss (cf. Brader and Marcus, 2013; O'Hara et al., 2023).

When encountering a motive-inconsistent event, it also makes functional sense for appraisals of high vs. low control potential to determine whether to get less of it by reacting with “contending” emotions (Arnold, 1960), such as disgust, anger, and contempt (whose responses aim to change a situation), or “accommodating” emotions, such as distress, fear, and interpersonal dislike (whose responses increase distance between the self and the elicitor). If an individual's or group's control potential is low, attempts to push for change would likely fail. But if control potential is high, there may be a chance of succeeding, and a motive-inconsistent outcome may not have to be accepted. For example, Klandermans et al. (2008) report that immigrants who perceived themselves as victims of discrimination felt angry if they also appraised themselves as efficacious, but fear in the absence of perceived efficacy. Although having justice on one's side typically confers some power, if an individual or group has low control potential, then attacking or trying to compel the behavior of others—even if those others are violating norms—may invite strong opposition and damaging counterattacks. Facing immoral but overwhelmingly powerful authorities tends to elicit emotions such as fear or sadness that sap anger (e.g., Burnette et al., 2019; Garg and Lerner, 2013).

This is an important functional reason why control potential (as EST proposes), rather than norm violation (as in AIT) is a crucial proximal determinant of anger, with its characteristic confrontation and attack responses. Although, as noted above, being the victim of some norm violation often confers a sense of control potential, it does not always do so; and when individuals or groups face norm violations but are irremediably weak, responding with anger and attack behaviors may be counterproductive and even fatal. Moreover, people can and do get angry even when there has been no norm violation (Berkowitz, 2010). People also sometimes do get angry when they are powerless, but these instances may involve the belief that some third party (powerful others, perhaps a deity) or people in the future will be able to ultimately change the situation (which provides a sense of control potential). Wortman and Brehm (1975) found evidence that reactance, an anger-related response to being confronted with threats to freedom (Brehm and Brehm, 1981) occurs in response to negative outcomes as long as they are believed to be controllable; but gives way to learned helplessness (a sadness-like response) when control is seen as impossible.

When events are motive-inconsistent and control potential is high, the Emotion System Theory proposes that the type of motive-inconsistency or problem that an individual or group is facing determines whether attack emotions or rejection emotions are likely to be more adaptive. If something is unwanted merely because it blocks a goal (“instrumental problem type”; see Dollard et al., 1939), then emotions which attack the source of the goal blockage (frustration, anger, and guilt) may be able to force a change. But if something or someone is unwanted in itself (see Janoff-Bulman, 1979; Lewis, 1971; Tangney, 1995), because of some intrinsic problem such as inherent offensiveness (Rozin et al., 2016), immorality (Bell, 2013), or incompetence (Hutcherson and Gross, 2011), then such problems may not be modifiable, even if attacked (Fischer and Giner-Sorolla, 2016). In those cases, emotions that reject the unacceptable object, other person, or part of the self, e.g., by removing it in disgust (Plutchik, 1984), excluding them in contempt (Fischer and Roseman, 2007), or withdrawing in shame (Tangney et al., 1996) may be the most effective way to minimize its impact. For example, (Fischer and Roseman 2007) found that low vs. high appraisals of bad character distinguished anger (and short-term attacks) from contempt (and social exclusion).

I discussed above how emotions in the middle third and bottom third of Figure 1 are specialized for dealing with other people and the self, respectively. To be relevant to their elicitors, the responses of emotions felt toward others or the self should be elicited (respectively) by events caused by other people or the self , which they might therefore influence. An example of other-person causation and associated potential influence is when anger among middle-aged German men over perceived displacement by women was felt toward Chancellor Angela Merkel, whose family-work reconciliation policies were seen as a cause of the problem (Mushaben, 2020). Perceiving harm (against Namibians and Jews) as intentionally caused by Germans themselves (self-agency) led German students to support personal apologies to the victims and group reparations (Imhoff et al., 2013).

Thus, it is proposed that the appraisals identified in the Emotion System Theory process basic dimensions of situations that interact to predict which emotions are most likely to be adaptive in the type of situation that a person or group faces.

With regard to empirical evidence, as Parkinson and Manstead (1992) noted, most studies that establish relations between appraisals and emotions have been correlational (e.g., Frijda et al., 1989; Levine, 1996; Roseman et al., 1996; Smith and Ellsworth, 1985), and such correlational research cannot prove causation. Testing claims about causal influence requires experimental research, in which appraisals are manipulated and the resulting emotions measured. A number of hypotheses from the Emotion System Theory have been tested in this way.

Roseman (1991) manipulated five appraisals from an earlier version of EST (Roseman, 1984) in vignettes, and measured the emotions that research participants said the story characters felt. This study encompassed four of the six appraisals and all of the emotions in Figure 1 except surprise. A priori predictions from the theory were tested, and a predicted 5-way interaction was obtained. Results provided overall significant support for theory's hypotheses regarding how particular combinations of appraisals would lead to particular emotions. But as less than half of the emotion-specific predictions for the legitimacy and agency appraisals were significantly supported, the theory was revised (Roseman et al., 1996) to change from legitimacy to control potential as a determinant of attack emotions, and to propose that the emotions in the top third of the chart could result from events regardless of their cause.

Roseman and Evdokas (2004) manipulated appraisals of outcome probability and motivational state. They found that certainty about getting a pleasant taste led to feeling joy; certainty about not getting an unpleasant taste elicited relief; and uncertainty about getting a pleasant taste led to feeling hope (as predicted by Emotion System Theory). Contrary to predictions for the emotions in this study, but in line with Figure 1, uncertainty about avoiding unpleasant tastes led to feeling fear (rather than hope), seemingly because (according to a manipulation check) most participants in this condition thought that they would probably get an unpleasant taste.

Several studies by Reisenzein and his colleagues have manipulated unexpectedness and measured whether surprise results. Unexpectedness has been manipulated by (a) inducing and then disconfirming participants' expectations about the sequence in which stimuli would be shown (Reisenzein et al., 2006), (b) arranging for participants to unexpectedly succeed when working on a task (Stiensmeier-Pelster et al., 1995), and (c) presenting unexpected outcomes of a lottery (Juergensen et al., 2014). Across induction methods, unexpectedness caused participants to be surprised.

The full functional framework of which the Emotion System Theory is a part views emotions as only one of several influences on political phenomena. For example, in addition to supporting Donald Trump because he made them feel the emotion of hope (Hochschild, 2016), some people may have voted for him from a motive to improve their status (see Lamont, 2018), or based on cognitive factors such as ideology (Broockman and Kalla, 2024). When there has been no change in motive-relevant events, political outcomes may be primarily explained by cognitive factors; with small to moderate change, by motivational factors (Roseman, 2008); and with large or rapid change, by emotional factors (e.g., Mutz, 2018). When elicited, emotions add urgency (Frijda, 1986) as well as tendencies for the various responses shown in Figure 1, which would be less likely if only motives or cognitions were producing behavior. However, even if they are differentiable, the effects of cognition, motivation, and emotion on political behavior may co-occur and be intertwined.

Thus far, our consideration of causal claims has focused on hypotheses about causes of emotions. The following discussion of the effects of emotions will be relatively brief because it can be difficult to separate responses that are part of an emotion (already discussed above) from the emotion's effects. This is in part because the Emotion System Theory does not define emotions as purely mental states, or mental states having a neural substrate, which in turn cause behavior. Rather, as discussed above, emotions are conceptualized as having behavioral and motivational components (as in other contemporary emotion theories). The remainder of this section will discuss a number of additional responses of political actors that are associated with emotions, without resolving whether they should ultimately be considered consequences of the emotions or parts of their behavioral and emotivational components.

As shown in Figure 1, the Emotion System Theory predicts that hope will be associated with anticipation and potential approach, and a goal of having hoped-for events occur. According to Reicher and Haslam (2017), attendees at Trump rallies in 2016 were led to anticipate that he would make American great again. For example, after asking rally-goers in Monessen (Pennsylvania) when was the last time that the U.S. bested China in a trade deal, Trump said “I beat China all the time. All the time.” To which the crowd responded “We want Trump! We want Trump!” (p. 32). Phoenix (2020) found that felt hope motivated black people in Detroit to say they'd contact officials about a local political issue, and Finn and Glaser (2010) found that felt hope predicted voting for Barack Obama in 2008 (cf. Redlawsk et al., 2018 on hope for Cruz, Rubio, and Trump).

Fear is hypothesized to be associated with inhibition or flight, and to prepare people to move away from or to stop moving toward something. For example, Lerner and Keltner (2001) found that fear inhibits risk-taking. Valentino et al. (2018) found that showing survey participants the face of a woman expressing fear (in order to elicit that emotion) reduced the extent to which hostile sexism predicted voting for Donald Trump (thus, fear inhibited participants' sexism-related behavior).

Anger is hypothesized to involve a tendency to verbal or physical aggression, and a goal of compelling other people to alter their behavior. In a study by Sadler et al. (2005), anger after seeing videos of the September 11 attacks predicted Americans' ratings of the acceptability of verbally confronting a Muslim person, leaving a threatening message on a Muslim's answering machine, and defacing a mosque. Vasilopoulos et al. (2019) found that anger after the 2015 Paris terror attacks predicted voting for the far right in France (an Emotion System perspective would interpret this as representing support for aggression by the anti-immigrant National Front party). Tausch et al. (2011) found that anger predicted people's willingness to act “to change British foreign policy toward Muslim countries,” as well as support for violence “to stop Western interference in Muslim countries.”

Thus, there is some evidence that hope, fear, and anger cause political behavior in line with Emotion System Theory predictions. With the exception of the Valentino et al. (2018) study, each of these findings comes from correlational research. To firmly establish causal relationships, more experimental research will be needed.

6 Falsifiability: has the theory been empirically tested against alternatives?

Consistent with Marcus' (2023) criteria, hypotheses from the Emotion System Theory have been tested against a number of alternatives, and revised based on results of this research.

Roseman et al. (1990) tested predictions from the 1984 version of the Emotion System Theory against predictions from the competing appraisal theories of Arnold (1960) and Scherer (1988). Participants recalled experiences of all the emotions in Figure 1 except contempt, and rated measures of eight appraisals. Results supported Emotion System Theory predictions for motive-consistency, appetitive vs. aversive motivational state, and agency, but suggested that appraisals of ability to cope with an event (Arnold, 1960) rather than power (Osgood et al., 1957; Roseman, 1984) distinguished emotions that accommodate to negative outcomes (such as sadness) from emotions that contend with them (such as anger). There was no support for the prediction that extreme uncertainty caused surprise (Roseman, 1984).

Roseman et al. (1996) tested theory revisions based on these and other findings. Participants recalled experiences of particular emotions and rated a larger set of appraisals. Results showed that unexpectedness (Izard, 1977)—rather than novelty (Scherer, 1984), unfamiliarity (Scherer, 1988), or uncertainty (Roseman, 1984)—was associated with surprise (in agreement with later findings from Reisenzein's lab, discussed above). Appraisals of who caused an event (Kemper, 1978; Roseman, 1984; Weiner, 1985), more than appraisals of who was responsible (Lazarus and Smith, 1988), distinguished emotions felt toward other people (e.g., love and anger) and the self (e.g., pride and shame).

Contending emotions were not consistently distinguished from accommodating emotions by appraisals of one's power (Kemper, 1978; Roseman, 1984), or the potency of the eliciting stimulus (Osgood et al., 1975), or the extent to which eliciting stimulus was controllable by the person feeling the emotion (Frijda, 1986; cf. Bandura, 1977). Only the appraisal of whether there was “something I could do about” (as opposed to “nothing I could do about”) an event significantly did so. This led the hypothesis about the role of control potential, as discussed above and now shown in Figure 1.

The attack emotions in Figure 1 were not distinguished from the exclusion emotions by appraisals of legitimacy (Ausubel, 1955; Izard, 1991). Instead, an item measuring whether the emotion-eliciting event was or was not an intrinsic problem (revealing “the basic nature of someone or something”) distinguished contempt from anger, and shame from guilt, in both experiences recalled by participants (though it only differentiated disgust from frustration in one of two experiences).

Research has also evaluated aspects of the definition of emotion proposed by the Emotion System Theory. Several studies have addressed the question of whether emotions should be conceptualized as discrete, with multiple states differing in multiple properties (e.g., Marcus et al., 2019; Roseman, 1984) or dimensional, either unidimensional (positive…negative; Lodge and Taber, 2013), or two dimensional (positive…negative and low…high arousal; e.g., Russell, 1980, 2003). For example, differences in multiple response components have been found among 10 negative emotions (Roseman et al., 1994b) as well as five positive emotions and surprise (Roseman et al., 1994a, 2013). In a meta-analysis of 687 studies with over 49,000 participants, Lench et al. (2011) found greater support for discrete than dimensional theories, with differences in behavior, experience, and physiology among happiness, sadness, anxiety, and anger. In the political science literature, Huddy et al. (2007) found support for the discrete perspective in distinguishing anger from anxiety (see also Marcus, 2023), and Redlawsk (2023) reviews evidence for distinguishing contempt and disgust.

Research has also tested hypothesized responses in the Emotion System Theory against hypotheses from other theories. For example, Roseman et al. (1994a) examined whether action tendencies in hope involved preparation for action or rather actual approach behavior (e.g., Mowrer, 1960). Participants differentially said they felt like “planning for the future” rather than moving toward something, consistent with the view that hope more often prepares for (rather than initiates) action. Hope thus seems more conditional and future oriented than enthusiasm, as the latter is conceptualized in AIT (Gadarian and Brader, 2023).

Ongoing studies in our lab are continuing to attempt to replicate prior findings and test alternative hypotheses (e.g., Roseman et al., 2018; Steele et al., 2024).

7 Parallel processing: are multiple emotions and their elicitors processed simultaneously?

Marcus (2023) maintains that the appraisals which cause emotional responses should be processed continuously in parallel, and the Emotion System Theory shares this view. The appraisals around the borders of Figure 1 are hypothesized to combine as shown to elicit each of the emotions (empirical support was discussed above). Functionally, it would seem that multiple appraisals must be able to be processed together in order to allow a situation-appropriate emotion to be generated. For example, if an event is appraised as motive-inconsistent without appraising control potential, it is impossible to know whether it would be more adaptive to accommodate to it (e.g., feel fear and prepare to avoid it) or contend with it (e.g., feel anger and prepare to attack it). Coping adaptively with enemy soldiers on the battlefield, or an autocrat's demands, or a rival candidate's invitation to debate, provide examples that require processing both of these appraisals.

Some work of emotion theorists and researchers may seem to suggest that only one emotion, with a distinctive response profile or neural signature, can be experienced at a time. This may be a holdover from theories that conceptualize emotions as emergency responses (Cannon, 1939), if this implies that only one overall response pattern (e.g., fight or flight) can exist at a given time. If emotions have evolved to cope with crises and time-limited opportunities, perhaps all the resources of the brain and body must be mobilized at once, leaving no room for extraneous processes.

Empirically, Ekman (2003) reports that he has seen rapidly changing emotions more often than the once-anticipated emotion “blends” (Ekman et al., 1972). This could reflect a focus on emotional facial expressions, as it is hard to see how one could simultaneously turn the corners of the lips up when smiling in joy while turning them down in sadness, or raise the brows in fear while lowering them in anger. The same might be said of emotional behaviors: How can a person move both toward and away, approach and avoid, or accommodate and contend at the same time?

Yet as Marcus (2023) correctly observes, research participants often report that they are experiencing multiple emotions (e.g., Marcus et al., 2017; Neuman et al., 2018; Smith and Ellsworth, 1985; Watson and Clark, 1992). Plutchik (1984) explicitly built his theory to include combinations of “primary” emotions, even though each primary represented a distinctive pattern of adaptive behavior. For example, awe was held to be a combination of fear and surprise; disappointment a combination of surprise and sadness; and contempt a combination of anger and disgust.

It is understandable that the specification of which appraisal combinations elicit which emotions (as in Figure 1, or Table 5.4 in Scherer, 2001) could lead a reader to assume that only one emotion can occur at a given time. But if multiple appraisals can be made simultaneously, this need not be the case. For example, Larsen and McGraw (2014) maintained that “bittersweet” events (such as graduating from college) could simultaneously elicit both happiness and sadness, insofar as such events have both positive (successfully completing a degree) and negative (leaving good friends behind) aspects.

Indeed, couldn't a U.S. Senator feel both afraid of and angry at a powerful and vengeful president (and even feel both emotions about his vengefulness)? And couldn't that combination lead to attacking him but cautiously? Don't hope and fear often co-occur, as in “risk assessment” behavior that involves a combination of approach and avoidance (e.g., Blanchard et al., 2011; Lopes, 1987)?

I have repeatedly maintained (Roseman, 2011, 2024) that the co-occurrence of multiple emotions is one phenomenon that can make it difficult to definitively pin down the response characteristics of particular emotions, as when fear and anger combine to result in attacking a group in order to avoid some harm they might inflict (e.g., Vasilopoulos, 2019). There is nothing in the Emotion System Theory that would preclude such combinations. Recently (Israelsson et al. (2023) have found that all pairwise combinations of happiness, sadness, fear, anger, and disgust can be identified from dynamic facial and vocal expressions.

If multiple appraisals are processed simultaneously and multiple emotions can co-occur, might the causal connections between particular appraisals and emotions be probabilistic rather than definitive? Research by Kuppens et al. (2003) suggests that the relationships between appraisals and emotions (and between emotions and action tendencies) are contingent—that they usually rather than always co-occur. EST can also accommodate such relationships if appraisals can change rapidly, as when a candidate's supporters shift quickly (even within fractions of a second) from thinking of reasons why the candidate might prevail vs. be defeated (a shift in appraised motive-consistency, and, thus, for example between hope and fear); or from blaming one's own group's efforts for an electoral failure to blaming the candidate (and with this shift in agency appraisals, moving between guilt and anger). The possibility of such rapid shifts in appraisal and emotion (or between measurements of appraisals and emotions)—as well as that of multiple and differing simultaneous appraisals and emotions—could thus also account for observed probabilistic appraisal-emotion relationships.

8 Preconscious (or primitive) processing: can these emotions be elicited without reflective consciousness?

Marcus (2023) makes an important contribution in calling on emotion theories to take account of work on implicit cognition and humans' dual attitude system (e.g., Kahneman, 2011; Wilson et al., 2000). What role do preconscious or other low-level processes (and even unconscious processes) play in the causation of emotions in the Emotion System?

Roseman et al. (1996) suggested that it might be possible to identify “primitive” versions of the appraisals in their theory. For example, they proposed that a person simply recognizing a situation as one for which they have an existing action schema could constitute a primitive appraisal of high control potential. In that case, recognition memory could suffice to provide a preconscious (or unconscious) sense that there is something one can do about a situation. For example, merely seeing an image of an often-disparaged outparty candidate could preconsciously or unconsciously prime appraisals of high control potential and contribute to resulting anger or contempt.

Roseman et al. (1996) found some evidence that perceiving there was something one could do about an event distinguished emotions proposed to contend with eliciting stimuli from emotions that accommodate to them. The above-mentioned “primitive” analog suggests a bridge from unconscious to conscious processing of control potential information. A primitive appraisal of low control potential might also result from changes that occur too fast for an identification of what's happening, and high control potential from events changing slowly enough for categorization or comprehension to occur. For example, if a danger is rapidly looming (Riskind, 2024) or is presented as extremely imminent, as in the Bush administration's portrayal of the threat of Saddam Hussein possessing weapons of mass destruction (Glezos, 2013), then a primitive sense of low control potential and fear could result.

I will briefly note, with regard to the AIT alternative to control potential, that it's not clear how appraisals of norm compatibility would be generated preconsciously. How exactly do people “sense that some are intentionally causing or otherwise violating critical norms that enable safe cooperative behaviors” (Marcus, 2023, p. 14)? What are the “sensory and interoceptive inputs”, which Marcus (2023), p. 3) contends are characteristic of preconscious processing, that identify the norm violations and grievances proposed to elicit anger? Asserting that this information is processed preconsciously, without specifying exactly what is processed and how the processing is done, leaves both political psychologists and scientists seeking explanations. Above I have given some specifics about how control potential might be processed primitively according to EST, and researchers could test these proposals, e.g., by manipulating the rate at which situations are changing, and the imminence of change (the length of time from a signal that change is possible to the actual or expected occurrence of the change).

With regard to primitive appraisals of motive-consistency, Zajonc (1968) found that adults respond more positively to nonsense words, Chinese-like characters, and faces shown repeatedly, and do so without conscious awareness of which were presented more frequently than others (Zajonc, 1980). More than 400 studies have confirmed such mere exposure effects on favorability (Bornstein and Craver-Lemley, 2022). Thus, perceptual familiarity could serve as a primitive appraisal of motive-consistency, as when name recognition increases support for a candidate (Kam and Zechmeister, 2013). Stimuli exceeding a particular level of intensity (e.g., campaign ad loudness) may constitute a primitive appraisal of motive-inconsistency, or what Scherer (2001) would regard as “intrinsic unpleasantness.”

A primitive appraisal of agency might assess whether a person caused an outcome based on perceptions of spatial and/or temporal contiguity. For example, one might perceive an event to be caused by an individual or group seen to be physically present at, or moving in the immediate vicinity of, an event; or just before the event occurred (Michotte, 1963; White, 2006). Political leaders are often blamed for negative outcomes that occur during their tenure, even if those outcomes arose from natural disasters (Arceneaux and Stein, 2006).

The Emotion System Theory also acknowledges that emotions can be elicited non-consciously by facial expressions of other people (Leventhal, 1984), by drugs or electrical brain stimulation (Izard, 2007), and by music (Brader, 2005; see Scherer, 2004). These appear to be separate mechanisms from the causation by appraisal discussed above, perhaps by influencing neural events further along in the process by which appraisals typically elicit emotions.

It should be noted that LeDoux and Daw (2018) maintain that “many cognitive actions can be planned and executed without requiring explicit conscious deliberation” (p. 273), and can involve memory that is unconscious. This suggests that appraisals such as those identified in Figure 1 or in AIT (whether primitive or not) might also elicit emotions unconsciously. But it would be a mistake to limit models of emotion elicitation to preconscious or unconscious determinants. Even if, as Marcus (2023) correctly maintains, emotions can sometimes be generated more rapidly by non-reflective processes, it remains the case that at other times higher-level and conscious thinking and deliberation have causal roles. As Imbir (2016) observes, many instances of emotion are “reflective,” arising from comparisons of current situations against standards or criteria. In political contexts we might cite assessments of causal agency in generating anger in response to economic crises (Wagner, 2014) or of norm violations in eliciting national guilt over (Branscombe et al., 2004). Much work in political science uses survey responses, including some employing ratings of self-reported (conscious) emotions on the ANES affect battery, to predict outcomes, such as candidate evaluation (Johnston et al., 2015), voting (Panagopoulos and Prysby, 2017), and political polarization (Webster et al., 2022).

9 Summary discussion

In this paper, I have considered the significant contributions made to the understanding of emotions in political science and psychology by Marcus (2023); discussed the criteria he helpfully proposed for an adequate and comprehensive theory of emotions; noted gaps and problems in his analysis and critique of appraisal and valence theories; and identified both shortcomings and points of agreement between EST and the influential and still developing Affective Intelligence Theory that Marcus and his colleagues have proposed. As shown by the impressive body of theoretical and empirical work that Marcus and his colleagues have published over two decades, AIT makes predictions that are supported [under some conditions] e.g.,

• [it's sometimes the case that] people feeling anger will adhere more to their current positions than people feeling fear;

• uncertainty [is one of the appraisals that] elicits fear;

• norm violation [often] leads to anger [especially if control potential is not too low];

• preconscious appraisals [can] elicit emotions.

EST agrees with each of these predictions, but modifies them by specifying conditions under which they will and will not hold, e.g., as elaborated in the body of this paper and highlighted by the [bracketed, italicized] qualifications in this paragraph.

Shortcomings in AIT include the small number of emotions encompassed; the limited number of appraisals proposed to elicit them; the treatment of novelty, uncertainty, and unexpectedness as synonymous rather than distinctly different appraisals with different emotional sequelae; the failure to account for anger in the absence of norm violation; and the evidence that surprise (elicited by unexpectedness) leads to greater cognitive openness and relatively deliberative, unbiased processing of new information, whereas fear (especially as it increases in intensity) biases and narrows attention to threats and leads to increasingly constrained behavior (e.g., Mobbs et al., 2015).

As an alternative, I have discussed in detail the Emotion System Theory (e.g., Roseman, 2008, 2011, 2013) which encompasses 16 positive or negative emotions plus surprise; typical phenomenological, expressive, behavioral, and goal properties of each of these emotions; seven appraisals proposed to interactively differentiate which of them occur in any given situation; and the functions of each emotion and the emotion system as a whole. The Emotion System Theory is shown to address each of the criteria identified by Marcus (2023), and may offer a more comprehensive, empirically grounded theory of emotions, with many examples of how it predicts important outcomes within and beyond political science that are not included within AIT.

However, some hypotheses in the Emotion System Theory remain unconfirmed, and most if not all could benefit from replication. EST at present lacks the extensive body of empirical investigation that AIT has attracted within political science across a wide variety of contexts. For example, research has not firmly established that the proposed appraisal of low vs. high control potential in fact determines whether people feel emotions that accommodate to unwanted elicitors vs. emotions that attempt to change them (see, e.g., Scherer and Fontaine, 2013); or that an appraisal of instrumental vs. intrinsic problem type consistently distinguishes attack emotions from exclusion emotions, as shown in Figure 1. Further work is also needed to assess whether there is enough consistency across instances, cultures, and political and historical contexts—taking into account variation due to specific elicitors, emotion intensity, situational affordances/constraints, and the co-occurrence of an emotion with other emotions, emotion regulation, and non-emotional processes—to empirically validate or modify the response profiles of those particular emotions and their differentiation from each other.

The Emotion System Theory might also be improved by considering additional emotions that could influence political and other outcomes, such as excitement (e.g., if that is closer to enthusiasm and differs from hope in responses across multiple components) and hate (if hatred is sufficiently distinct from anger; see Steele, 2020).

In addition, future research is needed to investigate what happens when co-occurring emotions are elicited by multiple appraisals. What determines which will have the strongest impact on behavior?

Variation in the responses of particular emotions that correlate with or result from differences in emotion intensity—a relatively neglected topic in emotion theory and research that Marcus (e.g., 2023) considers in some depth—also deserves thorough examination. For example, does the goal that individuals and groups pursue in anger shift (from seeking to compel change in the behavior of other persons to seeking to hurt them in some way) when anger increases beyond some threshold of intensity (Roseman, 2024)?

As Marcus (2023) suggests, progress will be aided by continuing to empirically test competing hypotheses and theories against each other, and by further elaborating how emotions can be elicited by preconscious processes (as well as by primitive or unconscious processes and by higher-level cognition, as EST proposes).

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

IR: Conceptualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Acknowledgments

I am grateful to David Redlawsk, Amanda Steele, Anitha Varghese, Saheed Bello, and two reviewers for helpful comments on previous drafts.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

1. ^The relationship between appraisals and emotions can be confusing in AIT. Marcus (2023) writes that preconscious affective processes “are subsequently expressed in consciousness as subjective feeling states” (p. 4). This seems to imply that AIT's preconscious appraisals cause the subjective feeling states. Thus the present paper (e.g., in Table 1) represents AIT as holding that its preconscious appraisals are causes of the corresponding emotions, particularly as its appraisals are said to cause their “downstream consequences” (p. 5). Elsewhere Marcus (2023) repeatedly equates emotions with appraisals, e.g., describing “Emotion as preconscious appraisal” (p. 4); and says that AIT “has not been especially interested in subjective feeling states” (p. 9). If so, why use the language of “anxiety,” “anger,” and “enthusiasm” (e.g., Marcus and MacKuen, 1993; Marcus et al., 2017; Marcus, 2021)? If emotions should really be understood as preconscious appraisals, then political scientists should be thinking of AIT's published work in those terms, e.g., ‘Novelty/uncertainty, success of reward-seeking actions, and the vote' (Marcus and MacKuen, 1993). The use of emotion words may result in misunderstandings, or be attracting unwarranted interest from researchers concerned with what are generally thought of as emotions. This seeming inconsistency could be easily resolved by acknowledging that (a) emotions are typically caused by appraisals; (b) neither the eliciting appraisals nor the consequent emotions need be conscious; and (c) the emotions, whether unconscious, preconscious, or conscious, can have downstream effects (e.g., on voting, rioting, and other behaviors) that interest political scientists.

References

Abelson, R. P., Kinder, D. R., Peters, M. D., and Fiske, S. T. (1982). Affective and semantic components in political person perception. J. Pers. Soc. Psychol. 42:619. doi: 10.1037/0022-3514.42.4.619

Crossref Full Text | Google Scholar

Adams, T. G., Stewart, P. A., and Blanchar, J. C. (2014). Disgust and the politics of sex: exposure to a disgusting odorant increases politically conservative views on sex and decreases support for gay marriage. PLoS ONE 9:e95572. doi: 10.1371/journal.pone.0095572

PubMed Abstract | Crossref Full Text | Google Scholar

Ali, A. (2019). Politics of Exclusion Through Language in the Presidential Speeches of Donald Trump. Available online at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3462228 (Accessed January 28, 2025).

Google Scholar

American National Election Studies (2021). ANES 2020 Time Series Study Full Release [dataset and documentation]. Avalable online at: www.electionstudies.org (Accessed July 29, 2025).

Google Scholar

Amsel, A. (1992). Frustration Theory: An Analysis of Dispositional Learning and Memory. Cambridge: Cambridge University Press.

Google Scholar

Arceneaux, K., and Stein, R. M. (2006). Who is held responsible when disaster strikes? The attribution of responsibility for a natural disaster in an urban election. J. Urban Aff. 28, 43–53. doi: 10.1111/j.0735-2166.2006.00258.x

Crossref Full Text | Google Scholar

Arnold, M. B. (1960). Emotion and Personality. New York, NY: Columbia.

Google Scholar

Arnsdorf, I., Dawsey, J., and Barrett, D. (2023). Trump and Allies Plot Revenge, Justice Department Control in a Second Term. The Washington Post. Available online at: https://www.washingtonpost.com/politics/2023/11/05/trump-revenge-second-term/ (Accessed July 29, 2025).

Google Scholar

Ausubel, D. P. (1955). Relationships between shame and guilt in the socializing process. Psychol. Rev. 62:378. doi: 10.1037/h0042534

PubMed Abstract | Crossref Full Text | Google Scholar

Averill, J. R. (1980). “A constructivist view of emotion,” in Emotion: Theory, Research and Experience, Vol. 1: Theories of Emotion, eds. R. Plutchik, and H. Kellerman (New York, NY: Academic Press), 305–339.

Google Scholar

Averill, J. R. (1982). Anger and Aggression: An Essay on Emotion. New York, NY: Springer- Verlag.

Google Scholar

Avis, W. R. (2017). Scoping study on defining and measuring distress migration. (GSDRC Helpdesk Research Report no. 1406). Rome: Food and Agriculture Organization of the United Nations. Retrieved from: https://www.gsdrc.org/wp-content/uploads/2017/04/HDR1406.pdf (Accessed August 22, 2025).

Google Scholar

Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84, 191–215. doi: 10.1037/0033-295X.84.2.191

Crossref Full Text | Google Scholar

Banks, A. J. (2014). The public's anger: white racial attitudes and opinions toward health care reform. Polit. Behav. 36, 493–514. doi: 10.1007/s11109-013-9251-3

Crossref Full Text | Google Scholar

Barkan, E. (2001). The Guilt of Nations: Restitution and Negotiating Historical Injustices. New York, NY: Norton.

Google Scholar

Barrett, L. F. (2006). Emotions as natural kinds? Perspect. Psychol. Sci. 1, 28–58. doi: 10.1111/j.1745-6916.2006.00003.x

PubMed Abstract | Crossref Full Text | Google Scholar

Barrett, L. F. (2009). Variety is the spice of life: A psychological construction approach to understanding variability in emotion. Cogn. Emot. 23, 1284–1306.

Google Scholar

Batson, C. D., Fultz, J., and Schoenrade, P. A. (1987). Distress and empathy: two qualitatively distinct vicarious emotions with different motivational consequences. J. Pers. 55, 19–39. doi: 10.1111/j.1467-6494.1987.tb00426.x

PubMed Abstract | Crossref Full Text | Google Scholar

Baumeister, R. F., Bratslavsky, E., Finkenauer, C., and Vohs, K. D. (2001). Bad is stronger than good. Rev. Gen. Psychol. 5, 323–370. doi: 10.1037/1089-2680.5.4.323

Crossref Full Text | Google Scholar

Behnke, M., Kreibig, S. D., Kaczmarek, L. D., Assink, M., and Gross, J. J. (2022). Autonomic nervous system activity during positive emotions: a meta-analytic review. Emot. Rev. 14, 132–160. doi: 10.1177/17540739211073084

Crossref Full Text | Google Scholar

Bell, M. (2013). Hard Feelings: The Moral Psychology of Contempt. New York, NY: Oxford University Press.

Google Scholar

Berkowitz, L. (2010). “Appraisals and anger: How complete are the usual appraisal accounts of anger?” in International Handbook of Anger: Constituent and Concomitant Biological, Psychological, and Social Processes, eds. M. Potegal, G. Stemmler, and C. Spielberger (New York, NY: Springer), 267–286.

Google Scholar

Blanchard, D. C. (2023). Sex, defense, and risk assessment: who could ask for anything more? Neurosci. Biobehav. Rev. 144:104931. doi: 10.1016/j.neubiorev.2022.104931

PubMed Abstract | Crossref Full Text | Google Scholar

Blanchard, D. C., and Blanchard, R. J. (2003). What can animal aggression research tell us about human aggression?. Horm. Behav. 44, 171–177. doi: 10.1016/S0018-506X(03)00133-8

Crossref Full Text | Google Scholar

Blanchard, D. C., Griebel, G., Pobbe, R., and Blanchard, R. J. (2011). Risk assessment as an evolved threat detection and analysis process. Neurosci. Biobehav. Rev. 35, 991–998. doi: 10.1016/j.neubiorev.2010.10.016

PubMed Abstract | Crossref Full Text | Google Scholar

Blanchard, D. C., Hynd, A. L., Minke, K. A., Minemoto, T., and Blanchard, R. J. (2001). Human defensive behaviors to threat scenarios show parallels to fear-and anxiety-related defense patterns of non-human mammals. Neurosci. Biobehav. Rev. 25, 761–770. doi: 10.1016/S0149-7634(01)00056-2

Crossref Full Text | Google Scholar

Bol, D., and Verthé, T. (2019). “Strategic voting versus sincere voting.” Oxford Research Encyclopedia: Politics, ed. W. R. Thompson (New York, NY: Oxford University Press), Retrieved from: https://oxfordre.com/politics/view/10.1093/acrefore/9780190228637.001.0001/acrefore-9780190228637-e-932 (Accessed August 20, 2025)

Google Scholar

Bornstein, R. F., and Craver-Lemley, C. (2022). “Mere exposure effect,” in Cognitive Illusions: Intriguing Phenomena in Thinking, Judgment, and Memory, ed. R. F. Pohl (New York, NY: Routledge), 256–275.

Google Scholar

Boyd, R. L., Ashokkumar, A., Seraj, S., and Pennebaker, J. W. (2022). The Development and Psychometric Properties of LIWC-22. Austin, TX: University of Texas at Austin.

Google Scholar

Brader, T. (2005). Striking a responsive chord: How political ads motivate and persuade voters by appealing to emotions. Am. J. Pol. Sci. 49, 388–405. doi: 10.1111/j.0092-5853.2005.00130.x

Crossref Full Text | Google Scholar

Brader, T., and Marcus, G. E. (2013). “Emotion and political psychology,” in The Oxford Handbook of Political Psychology, eds. L. Huddy, D.O. Sears, and J. S. Levy (Oxford), 165–204.

Google Scholar

Branscombe, N. R., Slugoski, B., and Kappen, D. M. (2004). “The measurement of collective guilt: what it is and what it is not,” in Collective Guilt: International Perspectives, ed. N. Branscombe and B. Doosje (New York, NY: Cambridge University Press), 16–34.

Google Scholar

Breeze, R. (2020). Angry tweets: A corpus-assisted study of anger in populist political discourse. J. Lang. Aggress. Conflict 8, 118–145. doi: 10.1075/jlac.00033.bre

Crossref Full Text | Google Scholar

Brehm, S. S., and Brehm, J. W. (1981). Psychological Reactance: A Theory of Freedom and Control. New York, NY: Academic Press.

Google Scholar

Brod, G., Hasselhorn, M., and Bunge, S. A. (2018). When generating a prediction boosts learning: the element of surprise. Learn. Instruct. 55, 22–31. doi: 10.1016/j.learninstruc.2018.01.013

Crossref Full Text | Google Scholar

Broockman, D., and Kalla, J. L. (2024). Candidate ideology and vote choice in the 2020 US presidential election. Am. Polit. Res. 52, 83–96. doi: 10.1177/1532673X231220652

Crossref Full Text | Google Scholar

Burnette, C. E., Renner, L. M., and Figley, C. R. (2019). The framework of historical oppression, resilience and transcendence to understand disparities in depression amongst Indigenous peoples. Br. J. Soc. Work 49, 943–962. doi: 10.1093/bjsw/bcz041

PubMed Abstract | Crossref Full Text | Google Scholar

Cacioppo, J. T., Larsen, J. T., Smith, N. K., and Berntson, G. G. (2004). “The affect system: what lurks below the surface of feelings,” in Feelings and Emotions: The Amsterdam Symposium, eds. A. S. R. Manstead, N. H. Frijda, and A. Fischer (Cambridge), 223–242.

Google Scholar

Camras, L. A., Meng, Z., Ujiie, T., Dharamsi, S., Miyake, K., Oster, H., et al. (2002). Observing emotion in infants: facial expression, body behavior, and rater judgments of responses to an expectancy-violating event. Emotion 2:179. doi: 10.1037/1528-3542.2.2.179

PubMed Abstract | Crossref Full Text | Google Scholar

Cannon, W. B. (1939). The Wisdom of the Body. New York, NY: W. W. Norton.

Google Scholar

Carlsmith, K. M., and Sood, A. M. (2009). The fine line between interrogation and retribution. J. Exp. Soc. Psychol. 45, 191–196. doi: 10.1016/j.jesp.2008.08.025

Crossref Full Text | Google Scholar

Casas, A., and Williams, N. W. (2019). Images that matter: online protests and the mobilizing role of pictures. Polit. Res. Q. 72, 360–375. doi: 10.1177/1065912918786805

Crossref Full Text | Google Scholar

Ceron, A., Curini, L., and Iacus, S. M. (2015). Using sentiment analysis to monitor electoral campaigns: method matters—evidence from the United States and Italy. Soc. Sci. Comput. Rev. 33, 3–20. doi: 10.1177/0894439314521983

Crossref Full Text | Google Scholar

Cheung-Blunden, V., and Blunden, B. (2008). Paving the road to war with group membership, appraisal antecedents, and anger. Aggress. Behav. 34, 175–189. doi: 10.1002/ab.20234

PubMed Abstract | Crossref Full Text | Google Scholar

Cheung-Blunden, V., and Ju, J. (2016). Anxiety as a barrier to information processing in the event of a cyberattack. Polit. Psychol. 37, 387–400. doi: 10.1111/pops.12264

Crossref Full Text | Google Scholar

Clifford, S., Erisen, C., Wendell, D., and Cantú, F. (2023). Disgust sensitivity and support for immigration across five nations. Polit. Life Sci. 42, 65–80. doi: 10.1017/pls.2022.6

PubMed Abstract | Crossref Full Text | Google Scholar

Cohen-Chen, S., Halperin, E., Porat, R., and Bar-Tal, D. (2014). The differential effects of hope and fear on information processing in intractable conflict. J. Soc. Polit. Psychol. 2, 11–30. doi: 10.5964/jspp.v2i1.230

Crossref Full Text | Google Scholar

Cohen-Chen, S., and Van Zomeren, M. (2018). Yes we can? Group efficacy beliefs predict collective action, but only when hope is high. J. Exp. Soc. Psychol. 77, 50–59. doi: 10.1016/j.jesp.2018.03.016

Crossref Full Text | Google Scholar

Cordaro, D. T., Sun, R., Keltner, D., Kamble, S., Huddar, N., and McNeil, G. (2018). Universals and cultural variations in 22 emotional expressions across five cultures. Emotion 18, 75–93. doi: 10.1037/emo0000302

PubMed Abstract | Crossref Full Text | Google Scholar

Corrigan, B., and Brader, T. (2011). “Campaign advertising: reassessing the impact of campaign ads on political behavior,” in New Directions in Campaigns and Elections, ed. S. K. Medvic (New York, NY: Routledge), 79–97.

Google Scholar

Coulson, M. (2004). Attributing emotion to static body postures: recognition accuracy, confusions, and viewpoint dependence. J. Nonverbal Behav. 28, 117–139. doi: 10.1023/B:JONB.0000023655.25550.be

Crossref Full Text | Google Scholar

Crowell, J. L., Roseman, I. J, Redlawsk, D. P., Mattes, K., and Katz, S. (2018). “Voters' emotions and perceived candidate traits distinguish those most favorable to Donald Trump (and Hillary Clinton),” in Poster Presented at the 30th Annual Convention (San Francisco, CA: Association for Psychological Science).

Google Scholar

Davies, J. C. (1962). Toward a theory of revolution. Am. Sociol. Rev. 27, 5–19. doi: 10.2307/2089714

Crossref Full Text | Google Scholar

Davitz, J. R. (1969). The Language of Emotion. New York, NY: Academic Press.

Google Scholar

de León, E., and Trilling, D. (2021). A sadness bias in political news sharing? The role of discrete emotions in the engagement and dissemination of political news on Facebook. Soc. Media Soc. 7, 1–10. doi: 10.1177/20563051211059710

Crossref Full Text | Google Scholar

Denson, T. F., Moulds, M. L., and Grisham, J. R. (2012). The effects of analytical rumination, reappraisal, and distraction on anger experience. Behav. Ther. 43, 355–364. doi: 10.1016/j.beth.2011.08.001

PubMed Abstract | Crossref Full Text | Google Scholar

Dickens, L. R., and Robins, R. W. (2022). Pride: a meta-analytic project. Emotion 22, 1071–1087. doi: 10.1037/emo0000905

PubMed Abstract | Crossref Full Text | Google Scholar

Dokecki, P. R. (2004). The Clergy Sexual Abuse Crisis: Reform and Renewal in the Catholic Community. Washington, DC: Georgetown University Press.

Google Scholar

Dollard, J., Miller, N. E., Doob, L. W., Mowrer, O. H., and Sears, R. R. (1939). Frustration and Aggression. New Haven, CT: Yale University Press.

Google Scholar

Doosje, B., Branscombe, N. R., Spears, R., and Manstead, A. S. R. (1998). Guilty by association: when one's group has a negative history. J. Pers. Soc. Psychol. 75, 872–886. doi: 10.1037/0022-3514.75.4.872

Crossref Full Text | Google Scholar

Drinkwater, S., and Jennings, C. (2022). The Brexit referendum and three types of regret. Public Choice 193, 275–291. doi: 10.1007/s11127-022-00997-z

Crossref Full Text | Google Scholar

Eben, C., Billieux, J., and Verbruggen, F. (2020). Clarifying the role of negative emotions in the origin and control of impulsive actions. Psychol. Belg. 60, 1–17. doi: 10.5334/pb.502

PubMed Abstract | Crossref Full Text | Google Scholar

Ekman, P. (2003). Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. New York, NY: Times Books.

Google Scholar

Ekman, P., and Friesen, W. V. (1982). Felt, false, and miserable smiles. J. Nonverbal Behav. 6, 238–252. doi: 10.1007/BF00987191

Crossref Full Text | Google Scholar

Ekman, P., Friesen, W. V., and Ellsworth, P. (1972). Emotion in the Human Face: Guidelines for Research and an Integration of Findings. New York, NY: Pergamon Press.

Google Scholar

Elfenbein, H. A., and Ambady, N. (2002). On the universality and cultural specificity of emotion recognition: a meta-analysis. Psychol. Bull. 128:203. doi: 10.1037/0033-2909.128.2.203

PubMed Abstract | Crossref Full Text | Google Scholar

Elfenbein, H. A., Beaupré, M., Lévesque, M., and Hess, U. (2007). Toward a dialect theory: cultural differences in the expression and recognition of posed facial expressions. Emotion 7:131. doi: 10.1037/1528-3542.7.1.131

PubMed Abstract | Crossref Full Text | Google Scholar

Ellsworth, P. C. (2024a). “Appraisal theories of emotions,” in Emotion Theory: The Routledge Comprehensive Guide, Vol. 1, ed. A. Scarantino (New York, NY: Routledge), 331–349.

Google Scholar

Ellsworth, P. C. (2024b). “Convergence among emotion theories,” in Paper presented at the 2024 Conference of the International Society for Research on Emotion (Belfast).

Google Scholar

Ellsworth, P. C., and Smith, C. A. (1988). Shades of joy: patterns of appraisal differentiating pleasant emotions. Cogn. Emot. 2, 301–331. doi: 10.1080/02699938808412702

Crossref Full Text | Google Scholar

Engen, H. G., and Singer, T. (2013). Empathy circuits. Curr. Opin. Neurobiol. 23, 275–282. doi: 10.1016/j.conb.2012.11.003

PubMed Abstract | Crossref Full Text | Google Scholar

Erisen, C., and Vasilopoulou, S. (2022). The affective model of far-right vote in Europe: anger, political trust, and immigration. Soc. Sci. Q. 103, 635–648. doi: 10.1111/ssqu.13153

Crossref Full Text | Google Scholar

Feldman, L., and Hart, P. S. (2018). Is there any hope? How climate change news imagery and text influence audience emotions and support for climate mitigation policies. Risk Anal. 38, 585–602. doi: 10.1111/risa.12868

PubMed Abstract | Crossref Full Text | Google Scholar

Fessler, D. M. T. (2010). “Madmen: an evolutionary perspective on anger and men's violent responses to transgression,” in International Handbook of Anger: Constituent and Concomitant Biological, Psychological, and Social Processes, eds. M. Potegal, G. Stemmler, and C. Spielberger (New York, NY: Springer New York),361–381.

Google Scholar

Finn, C., and Glaser, J. (2010). Voter affect and the 2008 US presidential election: hope and race mattered. Anal. Soc. Issues Public Policy 10, 262–275. doi: 10.1111/j.1530-2415.2010.01206.x

Crossref Full Text | Google Scholar

Fischer, A., and Giner-Sorolla, R. (2016). Contempt: derogating others while keeping calm. Emot. Rev. 8, 346–357. doi: 10.1177/1754073915610439

Crossref Full Text | Google Scholar

Fischer, A. H., and Roseman, I. J. (2007). Beat them or ban them: the characteristics and social functions of anger and contempt. J. Pers. Soc. Psychol. 93, 103–115. doi: 10.1037/0022-3514.93.1.103

PubMed Abstract | Crossref Full Text | Google Scholar

Fontaine, J. J. R., Scherer, K. R., and Soriano, C. (eds.), (2013). Components of Emotional Meaning: A Sourcebook. Oxford: Oxford University Press.

Google Scholar

Fredrickson, B. L. (2013). “Positive emotions broaden and build,” in Advances in Experimental Social Psychology, Vol. 47 (Burlington, NJ: Academic Press), 1–53.

Google Scholar

French, J. R. P. Jr., and Raven, B. (1959). “The bases of social power,” in Studies in Social Power, ed. D. Cartwright (Ann Arbor, MI: University of Michigan), 150–167.

Google Scholar

Fridkin, K. L., Gershon, S. A., Courey, J., and LaPlant, K. (2021). Gender differences in emotional reactions to the first 2016 presidential debate. Polit. Behav. 43, 55–85. doi: 10.1007/s11109-019-09546-9

Crossref Full Text | Google Scholar

Friedman, B. H., and Thayer, J. F. (2024). “Is emotion physiology more compatible with discrete, dimensional, or appraisal accounts?,” in Emotion Theory: The Routledge Comprehensive Guide, Vol. 1, ed. A. Scarantino (New York, NY: Routledge), 488–510.

Google Scholar

Frijda, N. H. (1986). The Emotions. New York, NY: Cambridge University Press.

Google Scholar

Frijda, N. H. (2007). The Laws of Emotion. Mahwah, NJ: Erlbaum.

Google Scholar

Frijda, N. H. (2010). Impulsive action and motivation. Biol. Psychol. 84, 570–579. doi: 10.1016/j.biopsycho.2010.01.005

PubMed Abstract | Crossref Full Text | Google Scholar

Frijda, N. H., Kuipers, P., and Ter Schure, E. (1989). Relations among emotion, appraisal, and emotional action readiness. J. Pers. Soc. Psychol. 57, 212–228. doi: 10.1037/0022-3514.57.2.212

Crossref Full Text | Google Scholar

Gadarian, S. K. (2010). The politics of threat: how terrorism news shapes foreign policy attitudes. J. Polit. 72, 469–483. doi: 10.1017/S0022381609990910

Crossref Full Text | Google Scholar

Gadarian, S. K., and Brader, T. (2023). “Emotion and political psychology,” in The Oxford Handbook of Political Psychology, 2nd Edn, eds. L. Huddy, D. O. Sears, J. S. Levy, and J. Jerit (New York, NY: Oxford), 191.

Google Scholar

Gadassi-Polack, R., Siemer, M., and Joormann, J. (2024). “Sadness and depression,” in Emotion Theory: The Routledge Comprehensive Guide, Vol. 2, ed. A. Scarantino (New York, NY: Routledge), 341–351.

Google Scholar

Garg, N., and Lerner, J. S. (2013). Sadness and consumption. J. Cons. Psychol. 23, 106–113. doi: 10.1016/j.jcps.2012.05.009

Crossref Full Text | Google Scholar

Gillioz, C., Fontaine, J. R., Soriano, C., and Scherer, K. R. (2016). Mapping emotion terms into affective space. Swiss J. Psychol. 75, 141–148. doi: 10.1024/1421-0185/a000180

Crossref Full Text | Google Scholar

Glezos, S. (2013). The Politics of Speed: Capitalism, the State and War in an Accelerating World. New York, NY: Routledge.

Google Scholar

Graham, A., Cullen, F. T., Butler, L. C., Burton, A. L., and Burton Jr, V. S. (2021). Who wears the MAGA hat? Racial beliefs and faith in Trump. Socius 7:2378023121992600. doi: 10.1177/2378023121992600

Crossref Full Text | Google Scholar

Grupe, D. W., and Nitschke, J. B. (2013). Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective. Nat. Rev. Neurosci. 14, 488–501. doi: 10.1038/nrn3524

PubMed Abstract | Crossref Full Text | Google Scholar

Hackert, B., Lumma, A. L., Menzel, P., Sparby, T., and Weger, U. (2021). Enquiring into the qualitative nature of anger: challenges and strengths of the introspective method. Curr. Psychol. 40, 3174–3189. doi: 10.1007/s12144-019-00221-0

Crossref Full Text | Google Scholar

Harmon-Jones, E. (2003). Anger and the behavioral approach system. Pers. Individ. Dif. 35, 995–1005. doi: 10.1016/S0191-8869(02)00313-6

Crossref Full Text | Google Scholar

Harper, C. A., Satchell, L. P., Fido, D., and Latzman, R. D. (2021). Functional fear predicts public health compliance in the COVID-19 pandemic. Int. J. Ment. Health Addict. 19, 1875–1888. doi: 10.1007/s11469-020-00281-5

PubMed Abstract | Crossref Full Text | Google Scholar

Harris, L. T., and Fiske, S. T. (2006). Dehumanizing the lowest of the low: neuroimaging responses to extreme out-groups. Psychol. Sci. 17, 847–853. doi: 10.1111/j.1467-9280.2006.01793.x

PubMed Abstract | Crossref Full Text | Google Scholar

Harrison, S. (2020). Democratic frustration: Concept, dimensions and behavioural consequences. Societies 10:19. doi: 10.3390/soc10010019

Crossref Full Text | Google Scholar

Harris-Perry, M. (2011). Sister Citizen: Shame, Stereotypes, and Black Women in America. New Haven: Yale University Press.

Google Scholar

Hitman, G. (2024). What went wrong? Israeli misconceptions and the October 2023 surprise. Middle East Policy 31, 82–94. doi: 10.1111/mepo.12762

Crossref Full Text | Google Scholar

Hochschild, A. R. (2016). Strangers in Their Own Land: Anger and Mourning on the American Right. New York, NY: The New Press.

Google Scholar

Hoefling, A., Likowski, K. U., Deutsch, R., Häfner, M., Seibt, B., Mühlberger, A., et al. (2009). When hunger finds no fault with moldy corn: food deprivation reduces food-related disgust. Emotion 9, 50–58. doi: 10.1037/a0014449

PubMed Abstract | Crossref Full Text | Google Scholar

Horstmann, G., and Schützwohl, A. (2024). “Surprise,” in Emotion Theory: The Routledge Comprehensive Guide (Vol. 2: Theories of Specific Emotions and Major Theoretical Challenges, ed. A. Scarantino (New York, NY: Routledge), 371–391.

Google Scholar

Huddy, L. (2013). “From group identity to political commitment and cohesion,” in The Oxford Handbook of Political Psychology, 2nd Edn, eds. L. Huddy, D. O. Sears, J. S. Levy, and J. Jerit (New York, NY: Oxford University Press), 737–773.

Google Scholar

Huddy, L., Feldman, S., and Cassese, E. (2007). “On the distinct political effects of anxiety and anger,” in The Affect Effect: Dynamics of Emotion in Political Thinking and Behavior, eds. W. R. Neuman, G. E. Marcus, M, MacKuen, and A. N. Crigler (Chicago, IL: University of Chicago Press), 202–230.

Google Scholar

Huddy, L., Feldman, S., Lahav, G., and Taber, C. (2003). “Fear and terrorism: psychological reactions to 9/11,” in Framing Terrorism, eds. P. Norris, M. Kern, and M. Just (New York, NY: Routledge), 255–278.

Google Scholar

Huddy, L., Smirnov, O., Snider, K. L., and Perliger, A. (2021). Anger, anxiety, and selective exposure to terrorist violence. J. Conflict Resol. 65, 1764–1790. doi: 10.1177/00220027211014937

Crossref Full Text | Google Scholar

Hutcherson, C. A., and Gross, J. J. (2011). The moral emotions: a social–functionalist account of anger, disgust, and contempt. J. Pers. Soc. Psychol. 100, 719–737. doi: 10.1037/a0022408

PubMed Abstract | Crossref Full Text | Google Scholar

Imbir, K. K. (2016). From heart to mind and back again. A duality of emotion overview on emotion-cognition interactions. New Ideas Psychol. 43, 39–49. doi: 10.1016/j.newideapsych.2016.04.001

Crossref Full Text | Google Scholar

Imhoff, R., Wohl, M. J. A., and Erb, H. (2013). When the past is far from dead: how ongoing consequences of genocides committed by the ingroup impact collective guilt. J. Soc. Iss. 69, 74–91. doi: 10.1111/josi.12004

Crossref Full Text | Google Scholar

Israelsson, A., Seiger, A., and Laukka, P. (2023). Blended emotions can be accurately recognized from dynamic facial and vocal expressions. J. Nonverbal Behav. 47, 267–284. doi: 10.1007/s10919-023-00426-9

Crossref Full Text | Google Scholar

Izard, C. E. (1977). Human Emotions. New York, NY: Plenum.

Google Scholar

Izard, C. E. (1991). The Psychology of Emotions. New York, NY: Plenum.

Google Scholar

Izard, C. E. (2007). Basic emotions, natural kinds, emotion schemas, and a new paradigm. Perspect. Psychol. Sci. 2, 260–280. doi: 10.1111/j.1745-6916.2007.00044.x

PubMed Abstract | Crossref Full Text | Google Scholar

Izard, C. E., Dougherty, F. E., Bloxom, B. M., and Kotsch, N. E. (1974). The Experience of Discrete Emotions. Differential Emotions Scale: A Method of Measuring the Meaning of the Subjective Experience of Discrete Emotions. Nashville, TN: Vanderbilt University Press.

Google Scholar

James, W. (1890). The Principles of Psychology. New York, NY: Henry Holt.

Google Scholar

Janoff-Bulman, R. (1979). Characterological versus behavioral self-blame: inquiries into depression and rape. J. Pers. Soc. Psychol. 37, 1798–1809. doi: 10.1037/0022-3514.37.10.1798

Crossref Full Text | Google Scholar

Jepson, C., and Chaiken, S. (1990). Chronic issue-specific fear inhibits systematic processing of persuasive communications. J. Soc. Behav. Pers. 5, 61–84.

Google Scholar

Jerit, J., and Kam, C. D. (2023). “Information processing,” in The Oxford Handbook of Political Psychology, 3rd Edn, eds. L. Huddy, D. O. Sears, and J. S. Levy (New York, NY: Oxford University Press), 517–554.

Google Scholar

Johnson, D. (2009). Anger about crime and support for punitive criminal justice policies. Punish. Soc. 11, 51–66. doi: 10.1177/1462474508098132

Crossref Full Text | Google Scholar

Johnston, C. D., Lavine, H., and Woodson, B. (2015). Emotion and political judgment: expectancy violation and affective intelligence. Polit. Res. Q. 68, 474–492. doi: 10.1177/1065912915593644

Crossref Full Text | Google Scholar

Juergensen, J., Weaver, J. S., Burns, K. J., Knutson, P. E., Butler, J. L., and Demaree, H. A. (2014). Surprise is predicted by event probability, outcome valence, outcome meaningfulness, and gender. Motiv. Emot. 38, 297–304. doi: 10.1007/s11031-013-9375-0

Crossref Full Text | Google Scholar

Justice, J. W. (2020). We Alone Can Fix it: Donald Trump's Campaign Rallies and the Rhetoric of Community (Unpublished doctoral dissertation, Lawrence: University of Kansas).

Google Scholar

Kahn, J. H., Tobin, R. M., Massey, A. E., and Anderson, J. A. (2007). Measuring emotional expression with the linguistic inquiry and word count. Am. J. Psychol. 120, 263–286. doi: 10.2307/20445398

Crossref Full Text | Google Scholar

Kahneman, D. (2011). Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux.

Google Scholar

Kam, C. D., and Zechmeister, E. J. (2013). Name recognition and candidate support. Am. J. Pol. Sci. 57, 971–986. doi: 10.1111/ajps.12034

Crossref Full Text | Google Scholar

Karinen, A. K., Molho, C., Kupfer, T. R., and Tybur, J. M. (2019). Disgust sensitivity and opposition to immigration: does contact avoidance or resistance to foreign norms explain the relationship? J. Exp. Soc. Psychol. 84:103817. doi: 10.1016/j.jesp.2019.103817

Crossref Full Text | Google Scholar

Karlsson, G., and Sjöberg, L. G. (2009). The experiences of guilt and shame: a phenomenological–psychological study. Hum. Stud. 32, 335–355. doi: 10.1007/s10746-009-9123-3

Crossref Full Text | Google Scholar

Keinan, G. (1987). Decision making under stress: scanning of alternatives under controllable and uncontrollable threats. J. Pers. Soc. Psychol. 52, 639–644. doi: 10.1037/0022-3514.52.3.639

Crossref Full Text | Google Scholar

Keltner, D., Sauter, D., Tracy, J. L., Wetchler, E., and Cowen, A. S. (2022). How emotions, relationships, and culture constitute each other: advances in social functionalist theory. Cogn. Emot. 36, 388–401. doi: 10.1080/02699931.2022.2047009

PubMed Abstract | Crossref Full Text | Google Scholar

Kemper, T. D. (1978). Toward a sociology of emotions: some problems and some solutions. Am. Sociol. 13, 30–41.

Google Scholar

Kennedy, C., Blumenthal, M., Clement, S., Clinton, J. D., Durand, C., Franklin, C., et al. (2018). An evaluation of the 2016 election polls in the United States. Public Opin. Q. 82, 1–33. doi: 10.1093/poq/nfx047

Crossref Full Text | Google Scholar

Klandermans, B., van der Toorn, J., and van Stekelenburg, J. (2008). How immigrants turn grievances into action. Am. Sociol. Rev. 73, 992–1012. doi: 10.1177/000312240807300606

Crossref Full Text | Google Scholar

Kleinginna, P. R., and Kleinginna, A. M. (1981). A categorized list of emotion definitions, with suggestions for a consensual definition. Motiv. Emot. 5, 345–379. doi: 10.1007/BF00992553

Crossref Full Text | Google Scholar

Kohn, A. (2021). “Trumped: making sense of the narcissist-in-chief,” in The Psychology of Political Behavior in a Time of Change, eds. J. D. Sinnott, and J. S. Rabin (Cham: Springer), 529–537.

Google Scholar

Kövecses, Z. (2010). “Cross-cultural experience of anger: a psycholinguistic analysis,” in International Handbook of Anger: Constituent and Concomitant Biological, Psychological, and Social Processes, eds. M. Potegal, G. Stemmler, and C. Spielberger (New York, NY: Springer-Verlag), 157–174.

Google Scholar

Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: a review. Biol. Psychol. 84, 394–421. doi: 10.1016/j.biopsycho.2010.03.010

PubMed Abstract | Crossref Full Text | Google Scholar

Kreibig, S. D. (2014). “Autonomic nervous system aspects of positive emotion,” in Handbook of Positive Emotions, eds. M. M. Tugade, M. N. Shiota, and L. D. Kirby (New York, NY: Guilford), 133–158.

Google Scholar

Kuppens, P., Van Mechelen, I., Smits, D. J., and De Boeck, P. (2003). The appraisal basis of anger: specificity, necessity and sufficiency of components. Emotion 3, 254–269. doi: 10.1037/1528-3542.3.3.254

PubMed Abstract | Crossref Full Text | Google Scholar

Lamont, M. (2018). Addressing recognition gaps: destigmatization and the reduction of inequality. Am. Sociol. Rev. 83, 419–444. doi: 10.1177/0003122418773775

Crossref Full Text | Google Scholar

Landwehr, C., and Ojeda, C. (2021). Democracy and depression: a cross-national study of depressive symptoms and nonparticipation. Am. Political Sci. Rev. 115, 323–330. doi: 10.1017/S0003055420000830

Crossref Full Text | Google Scholar

Lang, P. J. (2014). Emotion's response patterns: the brain and the autonomic nervous system. Emot. Rev. 6, 93–99. doi: 10.1177/1754073913512004

Crossref Full Text | Google Scholar

Larsen, J. T., and McGraw, A. P. (2014). The case for mixed emotions. Soc. Personal. Psychol. Compass 8, 263–274. doi: 10.1111/spc3.12108

Crossref Full Text | Google Scholar

Lau, R. R., and Redlawsk, D. P. (2006). How Voters Decide: Information Processing in Election Campaigns. New York, NY: Cambridge University Press.

Google Scholar

Lazarus, R. S. (1991). Emotion and Adaptation. New York, NY: Oxford University Press.

Google Scholar

Lazarus, R. S., and Smith, C. A. (1988). Knowledge and appraisal in the cognition—emotion relationship. Cogn. Emot. 2, 281–300. doi: 10.1080/02699938808412701

Crossref Full Text | Google Scholar

Lebow, R. N. (2024). “Fear, pathogens and political order,” in Covid-19 and the Politics of Fear, eds. D. Degerman, M. Flinders, and M. Johnson (Bristol: Bristol University Press), 233–255.

Google Scholar

LeDoux, J., and Daw, N. D. (2018). Surviving threats: neural circuit and computational implications of a new taxonomy of defensive behaviour. Nat. Rev. Neurosci. 19, 269–282. doi: 10.1038/nrn.2018.22

PubMed Abstract | Crossref Full Text | Google Scholar

Lee, D. H., and Anderson, A. K. (2018). “Question 10: how and why are emotions communicated?,” in The Nature of Emotion: Fundamental Questions, 2nd Edn, eds. A. S. Fox, R. C. Lapate, A. J. Shackman, and R. J. Davidson (New York, NY: Oxford University Press), 241–276.

Google Scholar

Lemay, E. P. Jr., Overall, N. C., and Clark, M. S. (2012). Experiences and interpersonal consequences of hurt feelings and anger. J. Pers. Soc. Psychol. 103, 982–1006. doi: 10.1037/a0030064

PubMed Abstract | Crossref Full Text | Google Scholar

Lench, H. C., Flores, S. A., and Bench, S. W. (2011). Discrete emotions predict changes in cognition, judgment, experience, behavior, and physiology: a meta-analysis of experimental emotion elicitations. Psychol. Bull. 137, 834–855. doi: 10.1037/a0024244

PubMed Abstract | Crossref Full Text | Google Scholar

Lerner, J. S., and Keltner, D. (2001). Fear, anger, and risk. J. Pers. Soc. Psychol. 81, 146–159. doi: 10.1037/0022-3514.81.1.146

Crossref Full Text | Google Scholar

Lerner, M. J. (2015). “Understanding how the justice motive shapes our lives and treatment of one another: exciting contributions and misleading claims,” in The Oxford Handbook of Justice in the Workplace, eds. R. S. Cropanzano and M. L. Ambrose (New York, NY: Oxford University Press), 205–234.

Google Scholar

Leshem, O. A., and Halperin, E. (2020). “Hope during conflict,” in Historical and Multidisciplinary Perspectives on Hope, eds. S. C. Van den Heuvel (Cham: Springer Nature), 179–196.

Google Scholar

Leventhal, H. (1984). “A perceptual motor theory of emotion,” in Approaches to Emotion, eds. K. R. Scherer, and P. Ekman (Hillsdale, NJ: Erlbaum).

Google Scholar

Levine, L. J. (1996). The anatomy of disappointment: a naturalistic test of appraisal models of sadness, anger, and hope. Cogn. Emot. 10, 337–360. doi: 10.1080/026999396380178

Crossref Full Text | Google Scholar

Lewis, H. B. (1971). Shame and Guilt in Neurosis. New York, NY: International Universities Press.

Google Scholar

Lewis, M., Alessandri, S. M., and Sullivan, M. W. (1990). Violation of expectancy, loss of control, and anger expressions in young infants. Dev. Psychol. 26:745. doi: 10.1037/0012-1649.26.5.745

Crossref Full Text | Google Scholar

Litvak, P. M., Lerner, J. S., Tiedens, L. Z., and Shonk, K. (2010). “Fuel in the fire: how anger impacts judgment and decision-making,” in International Handbook of Anger: Constituent and Concomitant Biological, Psychological, and Social Processes, eds. M. Potegal, G. Stemmler, and C. Spielberger (New York, NY: Springer-Verlag), 287–310.

Google Scholar

Lodge, M., and Taber, C. S. (2013). The Rationalizing Voter. Cambridge: Cambridge University Press.

Google Scholar

Loewenstein, J. (2019). Surprise, recipes for surprise, and social influence. Top. Cogn. Sci. 11, 178–193. doi: 10.1111/tops.12312

PubMed Abstract | Crossref Full Text | Google Scholar

Lopes, L. L. (1987). “Between hope and fear: the psychology of risk,” in Advances in Experimental Social Psychology, Vol. 20 (New York, NY: Academic Press), 255–295.

Google Scholar

Luo, F., Ghanei Gheshlagh, R., Dalvand, S., Saedmoucheshi, S., and Li, Q. (2021). Systematic review and meta-analysis of fear of COVID-19. Front. Psychol. 12:661078. doi: 10.3389/fpsyg.2021.661078

PubMed Abstract | Crossref Full Text | Google Scholar

Mackie, D. M., Devos, T., and Smith, E. R. (2000). Intergroup emotions: explaining offensive action tendencies in an intergroup context. J. Pers. Soc. Psychol. 79:602. doi: 10.1037/0022-3514.79.4.602

PubMed Abstract | Crossref Full Text | Google Scholar

MacKuen, M., Wolak, J., Keele, L., and Marcus, G. E. (2010). Civic engagements: Resolute partisanship or reflective deliberation. Am. J. Pol. Sci. 54, 440–458. doi: 10.1111/j.1540-5907.2010.00440.x

Crossref Full Text | Google Scholar

Marcus, G. E. (2021). “The rise of populism: the politics of justice, anger, and grievance,” in The Psychology of Populism: The Tribal Challenge to Liberal Democracy, eds. K. Fiedler, J. P. Forgas, and W. D. Crano (New York, NY: Routledge), 81–104.

Google Scholar

Marcus, G. E. (2023). Evaluating the status of theories of emotion in political science and psychology. Front. Polit. Sci. 4:1080884. doi: 10.3389/fpos.2022.1080884

Crossref Full Text | Google Scholar

Marcus, G. E., MacKuen, M., and Neuman, W. R. (2011). Parsimony and complexity: developing and testing theories of affective intelligence. Polit. Psychol. 32, 323–336. doi: 10.1111/j.1467-9221.2010.00806.x

Crossref Full Text | Google Scholar

Marcus, G. E., and MacKuen, M. B. (1993). Anxiety, enthusiasm and the vote: the emotional underpinnings of learning and involvement during presidential campaigns. Am. Polit. Sci. Rev. 87, 688–701. doi: 10.2307/2938743

Crossref Full Text | Google Scholar

Marcus, G. E., Neuman, W. R., and MacKuen, M. B. (2000). Affective Intelligence and Political Judgment. Chicago, IL: University of Chicago Press.

Google Scholar

Marcus, G. E., Neuman, W. R., and MacKuen, M. B. (2017). Measuring emotional response: comparing alternative approaches to measurement. Polit. Sci. Res. Methods 5, 733–754. doi: 10.1017/psrm.2015.65

Crossref Full Text | Google Scholar

Marcus, G. E., Valentino, N. A., Vasilopoulos, P., and Foucault, M. (2019). Applying the theory of affective intelligence to support for authoritarian policies and parties. Adv. Polit. Psychol. 40, 109–139. doi: 10.1111/pops.12571

Crossref Full Text | Google Scholar

Masket, S. (2020). Learning From Loss: The Democrats, 2016–2020. New York, NY: Cambridge University Press.

Google Scholar

Matsumoto, D., Hwang, H. C., and Frank, M. G. (2013). Emotional language and political aggression. J. Lang. Soc. Psychol. 32, 452–468. doi: 10.1177/0261927X12474654

Crossref Full Text | Google Scholar

Matsumoto, D., Hwang, H. C., and Frank, M. G. (2014). Emotions expressed by leaders in videos predict political aggression. Behav. Sci. Terror. Polit. Aggress. 6, 212–218. doi: 10.1080/19434472.2013.769116

Crossref Full Text | Google Scholar

Matsumoto, D., Keltner, D., Shiota, M. N., O'Sullivan, M., and Frank, M. (2008). “Facial expressions of emotion,” in Handbook of Emotions, 3rd Edn, eds. M. Lewis, J. M. Haviland-Jones, and L. F. Barrett (The Guilford Press), 211–234.

Google Scholar

Mattes, K., Roseman, I. J., Redlawsk, D. P., and Katz, S. (2018). “Contempt and anger in the 2016 U.S. Presidential election,” in Conventional Wisdom, Parties, and Broken Barriers in the 2016 Election, eds. J. C. Lucas, C. J. Galdieri, and T. S. Sisco (Lanham, MD: Lexington Books), 101–113.

Google Scholar

McDonald, J. (2023). Feeling Their Pain: Why Voters Want Leaders Who Care. New York, NY: Oxford University Press.

Google Scholar

McGarty, C., Pedersen, A., Wayne Leach, C., Mansell, T., Waller, J., and Bliuc, A. M. (2005). Group-based guilt as a predictor of commitment to apology. British J. Soc. Psychol. 44, 659–680. doi: 10.1348/014466604X1897

Crossref Full Text | Google Scholar

Mehlhaff, I. D., Ryan, T. J., Hetherington, M. J., and MacKuen, M. B. (2024). Where motivated reasoning withers and looms large: fear and partisan reactions to the COVID-19 pandemic. Am. J. Pol. Sci. 68, 5–23. doi: 10.1111/ajps.12808

Crossref Full Text | Google Scholar

Mercadante, E., Witkower, Z., and Tracy, J. L. (2021). The psychological structure, social consequences, function, and expression of pride experiences. Curr. Opin. Behav. Sci. 39, 130–135. doi: 10.1016/j.cobeha.2021.03.010

Crossref Full Text | Google Scholar

Michotte, A. (1963). The Perception of Causality. Transl. from the French by T. R. Miles and E. Miles. New York, NY: Basic Books.

Google Scholar

Mobbs, D., Hagan, C. C., Dalgleish, T., Silston, B., and Prévost, C. (2015). The ecology of human fear: survival optimization and the nervous system. Front. Neurosci. 9:121062. doi: 10.3389/fnins.2015.00055

PubMed Abstract | Crossref Full Text | Google Scholar

Moors, A. (2022). Demystifying Emotions: A Typology of Theories in Psychology and Philosophy. New York, NY: Cambridge University Press.

Google Scholar

Mowrer, O. (1960). Learning Theory and Behavior. New York, NY: Wiley.

Google Scholar

Mushaben, J. M. (2020). A spectre haunting Europe. German Polit. Soc. 38, 7–29. doi: 10.3167/gps.2020.380102

Crossref Full Text | Google Scholar

Mutz, D. C. (2018). Status threat, not economic hardship, explains the 2016 presidential vote. Proc. Nat. Acad. Sci. U. S. A. 115, E4330–E4339. doi: 10.1073/pnas.1718155115

PubMed Abstract | Crossref Full Text | Google Scholar

Neuman, W. R., Marcus, G. E., and MacKuen, M. B. (2018). Hardwired for news: affective intelligence and political attention. J. Broadcast. Electron. Media 62, 614–635. doi: 10.1080/08838151.2018.1523169

Crossref Full Text | Google Scholar

Ng, V., and Chan, K. (2017). Emotion politics: Joyous resistance in Hong Kong. China Rev. 17, 83–115. Available online at: https://muse.jhu.edu/article/649719

Google Scholar

Norgaard, J. R., and Walbert, H. (2023). Group polarization?: an analysis of Parler data in the wake of the capitol riot. J. Entrepreneur. Public Policy 12, 145–171. doi: 10.1108/JEPP-08-2022-0087

Crossref Full Text | Google Scholar

O'Hara, B. J., Owen, K. B., Bauman, A. E., Dunlop, S., Phongsavan, P., Furestad, E., et al. (2023). Hope and sadness: balancing emotions in tobacco control mass media campaigns aimed at smokers. Health Promot. J. Aust. 34, 856–866. doi: 10.1002/hpja.683

PubMed Abstract | Crossref Full Text | Google Scholar

Ojeda, C. (2025). The Sad Citizen: How Politics Is Depressing and Why It Matters. Chicago: University of Chicago Press.

Google Scholar

Orederu, T., Lennon, V., Vervliet, B., and Schiller, D. (2024). “Fear,” in Emotion Theory: The Routledge Comprehensive Guide, Vol. 2, ed. A. Scarantino (New York, NY: Routledge), 152–175.

Google Scholar

Ortony, A., Clore, G. L., and Collins, A. (2022). The Cognitive Structure of Emotions, 2nd ed. New York, NY: Cambridge University Press.

Google Scholar

Osgood, C. E., May, W. H., and Miron, M. S. (1975). Cross-Cultural Universals of Affective Meaning. Urbana, IL: University of Illinois Press.

Google Scholar

Osgood, C. E., Suci, G. J., and Tannenbaum, P. H. (1957). The Measurement of Meaning. Urbana, IL: University of Illinois Press.

Google Scholar

Panagopoulos, C. (2010). Affect, social pressure and prosocial motivation: field experimental evidence of the mobilizing effects of pride, shame and publicizing voting behavior. Polit. Behav. 32, 369–386. doi: 10.1007/s11109-010-9114-0

Crossref Full Text | Google Scholar

Panagopoulos, C., and Prysby, C. (2017). Polls and elections: Socioemotional selectivity theory and vote choice. Pres. Stud. Q. 47, 552–560. doi: 10.1111/psq.12400

Crossref Full Text | Google Scholar

Panksepp, J. (2004). Affective Neuroscience: The Foundations of Human and Animal Emotions. New York, NY: Oxford University Press.

Google Scholar

Parkinson, B., and Manstead, A. S. R. (1992). “Appraisal as a cause of emotion,” in Review of Personality and Social Psychology, Vol. 13, ed. M. S. Clark (Newbury Park, CA: Sage), 122–149.

Google Scholar

Pels, F., and Kleinert, J. (2016). Does exercise reduce aggressive feelings? An experiment examining the influence of movement type and social task conditions on testiness and anger reduction. Percept. Motor Skills 122, 971–987. doi: 10.1177/0031512516647802

PubMed Abstract | Crossref Full Text | Google Scholar

Peterson, A., Wahlström, M., and Wennerhag, M. (2018). Pride Parades and LGBT Movements: Political Participation in an International Comparative perspective. New York, NY: Routledge.

Google Scholar

Petrenko, V., Mitina, O. G., and Brown, R. (1995). The semantic space of Russian political parties on a federal and regional level. Eur. Asia Stud. 47, 835–857. doi: 10.1080/09668139508412290

Crossref Full Text | Google Scholar

Petty, R. E., Fleming, M. A., Priester, J. R., and Feinstein, A. H. (2001). Individual versus group interest violation: surprise as a determinant of argument scrutiny and persuasion. Soc. Cogn. 19, 418–442. doi: 10.1521/soco.19.4.418.20758

Crossref Full Text | Google Scholar

Phoenix, D. L. (2020). Black hope floats: racial emotion regulation and the uniquely motivating effects of hope on black political participation. J. Soc. Polit. Psychol. 8, 662–685. doi: 10.5964/jspp.v8i2.847

Crossref Full Text | Google Scholar

Plante, T. G. (1999). Bless Me Father for I Have Sinned: Perspectives on Sexual Abuse Committed by Roman Catholic Priests. Bloomsbury Publishing USA.

Google Scholar

Plutchik, R. (1984). “Emotions: a general psychoevolutionary theory,” in Approaches to Emotion, eds. K. R. Scherer, and P. Ekman (Hillsdale, NJ: Erlbaum), 197–219.

Google Scholar

Potegal, M., and Stemmler, G. (2010). “Constructing a neurology of anger,” in International Handbook of Anger: Constituent and Concomitant Biological, Psychological, and Social Processes, eds. M. Potegal, G. Stemmler, and C. Spielberger (New York, NY: Springer-Verlag), 39–59.

Google Scholar

Qiu, J., and Golman, R. (2024). Curio ws consumption. Appl. Cogn. Psychol. 38:e4195. doi: 10.1002/acp.4195

Crossref Full Text | Google Scholar

Quealy, K. (2021). The Complete List of Trump's Twitter insults (2015-2021). The New York Times. Available online at: https://www.nytimes.com/interactive/2021/01/19/upshot/trump-complete-insult-list.html (Accessed January 23, 2025).

Google Scholar

Quigley, K. S., and Barrett, L. F. (2014). Is There consistency and specificity of autonomic changes during emotional episodes? Guidance from the conceptual act theory and psychophysiology. Biol. Psychol. 98, 82–94. doi: 10.1016/j.biopsycho.2013.12.013

PubMed Abstract | Crossref Full Text | Google Scholar

Redlawsk, D. P. (2023). Expanding our thinking about discrete emotions and politics. Polit. Life Sci. 42, 146–157. doi: 10.1017/pls.2022.23

PubMed Abstract | Crossref Full Text | Google Scholar

Redlawsk, D. P., Civettini, A. J. W., and Lau, R. R. (2007). “Affective intelligence and voting: Information processing and learning in a campaign,” in The Affect Effect: Dynamics of Emotion in Political Thinking and Behavior, eds. W. R. Neuman, G. E. Marcus, M. MacKuen, and A. N. Crigler (Chicago, IL: University of Chicago Press), 1–23.

Google Scholar

Redlawsk, D. P., and Mattes, K. (2022). “Emotions and politics,” in Cambridge Handbook of Political Psychology, eds. D. Osborne, and C. G. Sibley (Cambridge: Cambridge University Press), 139–158.

Google Scholar

Redlawsk, D. P., and Pierce, D. R. (2017). “Emotions and voting,” in Sage Handbook of Electoral Behaviour, eds. K. Arzheimer, J. Evans, and M. Lewis-Beck (London: Sage), 406–432.

Google Scholar

Redlawsk, D. P., Roseman, I. J., Mattes, K., and Katz, S. (2018). Donald Trump, contempt, and the 2016 GOP Iowa Caucuses. J. Elect. Public Opin. Part. 28, 173–189. doi: 10.1080/17457289.2018.1441848

Crossref Full Text | Google Scholar

Reicher, S., and Haslam, S. A. (2017). “The politics of hope: Donald Trump as an entrepreneur of identity,” in Why Irrational Politics Appeals: Understanding the Allure of Trump, ed. M. Fitzduff (Praeger), 25–40.

Google Scholar

Reisenzein, R. (2000). “The subjective experience of surprise,” in The Message Within: The Role of Subjective Experience in Social Cognition and Behavior, eds. H. Bless and J. P. Forgas (Philadelphia, PA: Psychology Press), 262–279.

Google Scholar

Reisenzein, R., Bordgen, S., Holtbernd, T., and Matz, D. (2006). Evidence for strong dissociation between emotion and facial displays: the case of surprise. J. Pers. Soc. Psychol. 91, 295–315. doi: 10.1037/0022-3514.91.2.295

PubMed Abstract | Crossref Full Text | Google Scholar

Reisenzein, R., Horstmann, G., and Schützwohl, A. (2019). The cognitive-evolutionary model of surprise: a review of the evidence. Top. Cogn. Sci. 11, 50–74. doi: 10.1111/tops.12292

PubMed Abstract | Crossref Full Text | Google Scholar

Rico, G., Guinjoan, M., and Anduiza, E. (2017). The emotional underpinnings of populism: How anger and fear affect populist attitudes. Swiss Polit. Sci. Rev. 23, 444–461. doi: 10.1111/spsr.12261

Crossref Full Text | Google Scholar

Riedel, N., Köckler, H., Scheiner, J., van Kamp, I., Erbel, R., Loerbroks, A., et al. (2019). Urban road traffic noise and noise annoyance—a study on perceived noise control and its value among the elderly. Eur. J. Public Health 29, 377–379. doi: 10.1093/eurpub/cky141

PubMed Abstract | Crossref Full Text | Google Scholar

Riley, J. K. (2022). Angry enough to riot: an analysis of in-group membership, misinformation, and violent rhetoric on TheDonald.win between election day and inauguration. Soc. Media Soc. 8:9189. doi: 10.1177/20563051221109189

Crossref Full Text | Google Scholar

Riskind, J. H. (2024). Unscrambling the dynamics of danger: scientific foundations and evidence for the looming vulnerability model and looming cognitive style in anxiety. Cogn. Therapy Res. 48, 808–832. doi: 10.1007/s10608-024-10481-1

Crossref Full Text | Google Scholar

Rodríguez-Fuertes, A., Alard-Josemaría, J., and Sandubete, J. E. (2022). Measuring the candidates' emotions in political debates based on facial expression recognition techniques. Front. Psychol. 13:785453. doi: 10.3389/fpsyg.2022.785453

PubMed Abstract | Crossref Full Text | Google Scholar

Roseman, I. (2024). “Anger and interpersonal dislike,” in Emotion Theory: The Routledge Comprehensive Guide, Vol. 2, ed. A. Scarantino (New York, NY: Routledge), 46–66.

Google Scholar

Roseman, I. J. (1984). “Cognitive determinants of emotions: a structural theory,” in Review of Personality and Social Psychology, Vol. 5, ed. P. Shaver (Beverly Hills, CA: Sage Publications), 11–36.

Google Scholar

Roseman, I. J. (1991). Appraisal determinants of discrete emotions. Cogn. Emot. 5, 161–200. doi: 10.1080/02699939108411034

Crossref Full Text | Google Scholar

Roseman, I. J. (2008). “Motivations and emotivations: approach, avoidance, and other tendencies in motivated and emotional behavior,” in Handbook of Approach and Avoidance Motivation, ed. A. J. Elliot (New York, NY: Psychology Press), 343–366.

Google Scholar

Roseman, I. J. (2011). Emotional behaviors, emotivational goals, emotion strategies: multiple levels of organization integrate variable and consistent responses. Emot. Rev. 3, 434–443. doi: 10.1177/1754073911410744

Crossref Full Text | Google Scholar

Roseman, I. J. (2013). Appraisal in the emotion system: coherence in strategies for coping. Emot. Rev. 5, 141–149. doi: 10.1177/1754073912469591

Crossref Full Text | Google Scholar

Roseman, I. J. (2018). “Functions of anger in the emotion system,” in The Function of Emotions: When and Why Emotions Help Us, eds. H. Lench (Cham: Springer), 141–173.

Google Scholar

Roseman, I. J., Antoniou, A. A., and Jose, P. E. (1996). Appraisal determinants of emotions: constructing a more accurate and comprehensive theory. Cogn. Emot. 10, 241–277. doi: 10.1080/026999396380240

Crossref Full Text | Google Scholar

Roseman, I. J., and Evdokas, A. (2004). Appraisals cause experienced emotions: experimental evidence. Cogn. Emot. 18, 1–28. doi: 10.1080/02699930244000390

Crossref Full Text | Google Scholar

Roseman, I. J., King, E., Nugent, M. K., and Gordon, P. L. (2013). “Are positive emotions empirically distinguishable?,” in Paper Presented at the 20th Conference of the International Society for Research on Emotions (Berkeley, CA).

Google Scholar

Roseman, I. J., Mattes, K., Redlawsk, D. P., and Katz, S. (2020). Reprehensible, laughable: The role of contempt in negative campaigning. Am. Polit. Res. 48, 44–77. doi: 10.1177/1532673X19857968

Crossref Full Text | Google Scholar

Roseman, I. J., Sibley, C., Kapij, A. M., Jose, P. E., and Bloore, R. (2018). “Appraisal determinants of attack and rejection emotions,” in Poster presented at the Annual Meeting of the Eastern Psychological Association (Philadelphia, PA).

Google Scholar

Roseman, I. J., Spindel, M. S., and Jose, P. E. (1990). Appraisals of emotion-eliciting events: testing a theory of discrete emotions. J. Pers. Soc. Psychol. 59, 899–915. doi: 10.1037/0022-3514.59.5.899

Crossref Full Text | Google Scholar

Roseman, I. J., Swartz, T. S., Newman, L., and Nichols, N. (1994a). “Behaviors and goals can differentiate positive emotions,” in Paper presented at the 6th Annual Convention (Washington, DC: American Psychological Society).

Google Scholar

Roseman, I. J., Wiest, C., and Swartz, T. S. (1994b). Phenomenology, behaviors, and goals differentiate discrete emotions. J. Pers. Soc. Psychol. 67, 206–221. doi: 10.1037/0022-3514.67.2.206

Crossref Full Text | Google Scholar

Rosen, J. B., and Schulkin, J. (1998). From normal fear to pathological anxiety. Psychol. Rev. 105, 325–350. doi: 10.1037/0033-295X.105.2.325

Crossref Full Text | Google Scholar

Rothbart, D. (2021). Righteous rage as political power. Peace Conflict 27, 681–684. doi: 10.1037/pac0000544

Crossref Full Text | Google Scholar

Rozin, P., Haidt, J., and McCauley, C. (2016). “Disgust,” in Handbook of Emotions, 4th Edn, eds. L. F. Barrett, M. Lewis, and J. M. Haviland-Jones (New York, NY: Guilford), 815–834.

Google Scholar

Rudolph, B., and Roseman, I. J. (2024). “Data from theory of ideology predicts support for Trump and antidemocratic action,” in Paper Presented at the Annual Meeting (Philadelphia, PA: American Political Science Association).

Google Scholar

Russell, J. A. (1980). A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178. doi: 10.1037/h0077714

Crossref Full Text | Google Scholar

Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychol. Rev. 110:145. doi: 10.1037/0033-295X.110.1.145

Crossref Full Text | Google Scholar

Saarimäki, H., Gotsopoulos, A., Jääskeläinen, I. P., Lampinen, J., Vuilleumier, P., Hari, R., et al. (2016). Discrete neural signatures of basic emotions. Cereb. Cortex 26, 2563–2573. doi: 10.1093/cercor/bhv086

PubMed Abstract | Crossref Full Text | Google Scholar

Sabti, Y. M., and Ramalu, S. S. (2024). Home country economic, political, social push factors and intention to migrate in Iraq: psychological distress as mediator. Cogent Bus. Manag. 11:2299507. doi: 10.1080/23311975.2023.2299507

Crossref Full Text | Google Scholar

Sadler, M. S., Lineberger, M., Correll, J., and Park, B. (2005). Emotions, attributions, and policy endorsement in response to the September 11th terrorist attacks. Basic Appl. Soc. Psych. 27, 249–258. doi: 10.1207/s15324834basp2703_6

Crossref Full Text | Google Scholar

Sauter, D. A., Eisner, F., Ekman, P., and Scott, S. K. (2015). Emotional vocalizations are recognized across cultures regardless of the valence of distractors. Psychol. Sci. 26, 354–356. doi: 10.1177/0956797614560771

PubMed Abstract | Crossref Full Text | Google Scholar

Sauter, D. A., and Russell, J. A. (2024). “What do nonverbal expressions tell us about emotion,” in Emotion Theory: The Routledge Comprehensive Guide, Vol. 1 (New York, NY: Routledge), 543–560.

Google Scholar

Scarantino, A. (2024). “Motivational theories of emotions in philosophy and affective science,” in Emotion Theory: The Routledge Comprehensive Guide (New York, NY: Routledge), 429–466.

Google Scholar

Scheff, T. J. (2019). Bloody Revenge: Emotions, Nationalism, and War. Boulder: Westview Press.

Google Scholar

Schencking, J. C. (2022). Generosity betrayed: Pearl Harbor, ingratitude, and American humanitarian assistance to Japan in 1923. Pac. Hist. Rev. 91, 66–103. doi: 10.1525/phr.2022.91.1.66

Crossref Full Text | Google Scholar

Scherer, K. R. (1984). “On the nature and function of emotion: a component process approach,” in Approaches to Emotion, eds. K. R. Scherer, and P. Ekman (Hillsdale, NJ: Erlbaum), 293–317.

Google Scholar

Scherer, K. R. (1988). “Criteria for emotion-antecedent appraisal: a review,” in Cognitive Perspectives on Emotion and Motivation, eds. V. Hamilton, G. H. Bower, and N. H. Frijda (Kluwer Academic), 89–126.

Google Scholar

Scherer, K. R. (2001). “Appraisal considered as a process of multilevel sequential checking,” in Appraisal Processes in Emotion: Theory, Methods, Research, eds. K. R. Scherer, A. Schorr, and T. Johnstone (New York, NY: Oxford University Press), 92–120.

Google Scholar

Scherer, K. R. (2004). Which emotions can be induced by music? What are the underlying mechanisms? And how can we measure them? J. New Music Res. 33, 239–251. doi: 10.1080/0929821042000317822

Crossref Full Text | Google Scholar

Scherer, K. R. (2005). What are emotions? And how can they be measured? Soc. Sci. Inf. 44, 695–729.

Google Scholar

Scherer, K. R. (2009). The dynamic architecture of emotion: evidence for the component process model. Cogn. Emot. 23, 1307–1351. doi: 10.1080/02699930902928969

Crossref Full Text | Google Scholar

Scherer, K. R., and Fontaine, J. J. R. (2013). “Driving the emotion process: The appraisal component,” in Components of Emotional Meaning: A Sourcebook, eds. J. J. R. Fontaine, K. R., Scherer, and C. Soriano (Oxford: Oxford University Press), 186–209.

Google Scholar

Scherer, K. R., and Wallbott, H. G. (1994). Evidence for universality and cultural variation of differential emotion response patterning. J. Pers. Soc. Psychol. 66, 310–328. doi: 10.1037/0022-3514.66.2.310

Crossref Full Text | Google Scholar

Sell, A., Tooby, J., and Cosmides, L. (2009). Formidability and the logic of human anger. Proc. Nat. Acad. Sci. U. S. A. 106, 15073–15078. doi: 10.1073/pnas.0904312106

PubMed Abstract | Crossref Full Text | Google Scholar

Shaver, P., Schwartz, J., Kirson, D., and O'Connor, C. (1987). Emotion knowledge: further exploration of a prototype approach. J. Pers. Soc. Psychol. 52, 1061–1086. doi: 10.1037/0022-3514.52.6.1061

Crossref Full Text | Google Scholar

Shino, E., and Smith, D. A. (2021). Pandemic politics: COVID-19, health concerns, and vote choice in the 2020 General Election. J. Elect. Public Opin. Part. 31, 191–205. doi: 10.1080/17457289.2021.1924734

PubMed Abstract | Crossref Full Text | Google Scholar

Shiota, M. N. (2024). “Basic and discrete emotion theories,” in Emotion Theory: The Routledge Comprehensive Guide, Vol. 1, ed. A. Scarantino (Routledge), 310–330.

Google Scholar

Short, R., and Magaña, L. (2002). Political rhetoric, immigration attitudes, and contemporary prejudice: a Mexican American dilemma. J. Soc. Psychol. 142, 701–712. doi: 10.1080/00224540209603930

PubMed Abstract | Crossref Full Text | Google Scholar

Sides, J., Tesler, M., and Vavreck, L. (2017). The 2016 US election: how trump lost and won. J. Democr. 28, 34–44. doi: 10.1353/jod.2017.0022

Crossref Full Text | Google Scholar

Sirin, C. V., and Villalobos, J. D. (2021). “The study of discrete emotions in politics,” in Oxford Encyclopedia of Political Decision Making, ed. D. P. Redlawsk (New York, NY: Oxford University Press), 1462–1484.

Google Scholar

Smith, C. A., and Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. J. Pers. Soc. Psychol. 48, 813–838. doi: 10.1037/0022-3514.48.4.813

Crossref Full Text | Google Scholar

Smith, C. A., and Kirby, L. D. (2011). “The role of appraisal and emotion in coping and adaptation,” in The Handbook of Stress Science: Biology, Psychology, and Health, eds. R. J. Contrade, and A. Baum (New York, NY: Springer), 195–208.

Google Scholar

Smith, J., Nevitte, N., and Kornberg, A. (1990). National Images of Canada and the United States: their structure, coherence, and meaning. Am. Rev. Can. Stud. 20, 327–355. doi: 10.1080/02722019009481528

Crossref Full Text | Google Scholar

Steele, A. K. (2020). Are Interpersonal Dislike and Hatred Discrete Emotions? (Publication No. 28262389) (Master's thesis, Rutgers University, Camden, NJ). ProQuest Dissertations and Theses Global.

Google Scholar

Steele, A. K., Roseman, I. J., and Parkinson, B. (2024). “Reducing hate: How hatred may be reduced by changing people's appraisals,” in Poster Presents at the 25th Conference of the International Society for Research on Emotion (Belfast).

Google Scholar

Stemmler, G. (2010). “Somatovisceral activation during anger,” in International Handbook of Anger: Constituent and Concomitant Biological, Psychological, and Social Processes, eds. M. Potegal, G. Stemmler, and C. Spielberger (New York, NY: Springer-Verlag), 103–121.

Google Scholar

Stiensmeier-Pelster, J., Martini, A., and Reisenzein, R. (1995). The role of surprise in the attribution process. Cogn. Emot. 9, 5–31. doi: 10.1080/02699939508408963

Crossref Full Text | Google Scholar

Strozier, C. B. (2011). Until the Fires Stopped Burning: 9/11 and New York City in the Words and Experiences of Survivors and Witnesses. New York, NY: Columbia University Press.

Google Scholar

Susskind, J. M., Lee, D. H., Cusi, A., Feiman, R., Grabski, W., and Anderson, A. K. (2008). Expressing fear enhances sensory acquisition. Nat. Neurosci. 11, 843–850. doi: 10.1038/nn.2138

PubMed Abstract | Crossref Full Text | Google Scholar

Taber, C. S., and Young, E. (2013). “Political information processing,” in The Oxford Handbook of Political Psychology, 2nd Edn, eds. L. Huddy, D. O. Sears, and J. S. Levy (New York, NY: Oxford University Press), 525–558.

Google Scholar

Tangney, J. P. (1995). Recent advances in the empirical study of shame and guilt. Am. Behav. Sci. 38, 1132–1145. doi: 10.1177/0002764295038008008

Crossref Full Text | Google Scholar

Tangney, J. P., Miller, R. S., Flicker, L., and Barlow, D. H. (1996). Are shame, guilt, and embarrassment distinct emotions. J. Pers. Soc. Psychol. 70, 1256–1269. doi: 10.1037/0022-3514.70.6.1256

Crossref Full Text | Google Scholar

Tausch, N., and Becker, J. C. (2013). Emotional reactions to success and failure of collective action as predictors of future action intentions: a longitudinal investigation in the context of student protests in Germany. Br. J. Soc. Psychol. 52, 525–542. doi: 10.1111/j.2044-8309.2012.02109.x

PubMed Abstract | Crossref Full Text | Google Scholar

Tausch, N., Becker, J. C., Spears, R., Christ, O., Saab, R., Singh, P., et al. (2011). Explaining radical group behavior: developing emotion and efficacy routes to normative and nonnormative collective action. J. Pers. Soc. Psychol. 101, 129–148. doi: 10.1037/a0022728

PubMed Abstract | Crossref Full Text | Google Scholar

Tollen, L. (2024). Is it working? Evaluating the first round of Medicare drug price negotiations. Health Aff. 43, 1206–1208. doi: 10.1377/hlthaff.2024.00994

PubMed Abstract | Crossref Full Text | Google Scholar

Tolman, E. C. (1923). A behavioristic account of the emotions. Psychol. Rev. 30, 217. doi: 10.1037/h0071152

Crossref Full Text | Google Scholar

Tomkins, S. S. (1962). Affect Imagery Consciousness: Volume I: The Positive Affects. New York, NY: Springer.

Google Scholar

Tomkins, S. S. (1963). Affect Imagery Consciousness: Volume I: The Positive Affects. New York, NY: Springer.

Google Scholar

Tong, E. M. W. (2010). The sufficiency and necessity of appraisals for negative emotions. Cogn. Emot. 24, 692–701. doi: 10.1080/13854040902933601

Crossref Full Text | Google Scholar

Tong, E. M. W. (2015). Differentiation of 13 positive emotions by appraisals. Cogn. Emot. 29, 484–503. doi: 10.1080/02699931.2014.922056

PubMed Abstract | Crossref Full Text | Google Scholar

Tracy, J. L., Robins, R. W., and Tangney, J. P. (2007). The Self-Conscious Emotions: Theory and Research. New York, NY: Guilford Press.

Google Scholar

Valentino, N. A., Brader, T., Groenendyk, E. W., Gregorowicz, K., and Hutchings, V. L. (2011). Election night's alright for fighting: the role of emotions in political participation. J. Polit. 73, 156–170. doi: 10.1017/S0022381610000939

Crossref Full Text | Google Scholar

Valentino, N. A., Gregorowicz, K., and Groenendyk, E. W. (2009). Efficacy, emotions and the habit of participation. Polit. Behav. 31, 307–330. doi: 10.1007/s11109-008-9076-7

Crossref Full Text | Google Scholar

Valentino, N. A., Wayne, C., and Oceno, M. (2018). Mobilizing sexism: the interaction of emotion and gender attitudes in the 2016 US presidential election. Public Opin. Q. 82, 799–821. doi: 10.1093/poq/nfy003

Crossref Full Text | Google Scholar

Van Troost, D., Van Stekelenburg, J., and Klandermans, B. (2013). “Emotions of protest,” in Emotions in politics: The affect dimension in political tension, eds. G. E. Marcus and N. Demertzis (Basingstoke, Palgrave Macmillan), 186-203.

Google Scholar

Vasilopoulos, P. (2019). “Affective intelligence: emotional dynamics in voters' decisionmaking processes,” in The Oxford Encyclopedia of Political Decision-Making, ed. D. Redlawsk (New York, NY: Oxford University Press), 1–17.

Google Scholar

Vasilopoulos, P., Marcus, G. E., Valentino, N. A., and Foucault, M. (2019). Fear, anger, and voting for the far right: evidence from the November 13, 2015 Paris terror attacks. Polit. Psychol. 40, 679–704. doi: 10.1111/pops.12513

Crossref Full Text | Google Scholar

Verbeke, W., Belschak, F., and Bagozzi, R. P. (2004). The adaptive consequences of pride in personal selling. J. Acad. Market. Sci. 32, 386–402. doi: 10.1177/0092070304267105

Crossref Full Text | Google Scholar

Volynets, S., Glerean, E., Hietanen, J. K., Hari, R., and Nummenmaa, L. (2020). Bodily maps of emotions are culturally universal. Emotion 20, 1127–1136. doi: 10.1037/emo0000624

PubMed Abstract | Crossref Full Text | Google Scholar

Wagner, M. (2014). Fear and anger in Great Britain: blame assignment and emotional reactions to the financial crisis. Polit. Behav. 36, 683–703. doi: 10.1007/s11109-013-9241-5

PubMed Abstract | Crossref Full Text | Google Scholar

Walter, N., Demetriades, S. Z., and Nabi, R. L. (2021). Seeing red through rose-colored glasses: subjective hope as a moderator of the persuasive influence of anger. J. Commun. 71, 79–103. doi: 10.1093/joc/jqaa037

Crossref Full Text | Google Scholar

Wang, W., and Ahern, L. (2015). Acting on surprise: emotional response, multiple-channel information seeking and vaccination in the H1N1 flu epidemic. Soc. Influ. 10, 137–148. doi: 10.1080/15534510.2015.1011227

Crossref Full Text | Google Scholar

Warner, C. D. (2024). What Makes Love Good? Guru Devotion to the Dalai Lama and Donald Trump. Barcelona: European Association of Social Anthropology (EASA).

Google Scholar

Watson, D., and Clark, L. A. (1992). Affects separable and inseparable: on the hierarchical arrangement of the negative affects. J. Pers. Soc. Psychol. 62, 489–505. doi: 10.1037/0022-3514.62.3.489

Crossref Full Text | Google Scholar

Watson, D., and Clark, L. A. (1994). The PANAS-X: Manual for the Positive and Negative Affect Schedule-Expanded Form (Iowa City: University of Iowa).

Google Scholar

Weber, C. (2013). Emotions, campaigns, and political participation. Polit. Res. Q. 66, 414–428. doi: 10.1177/106591291244969

Crossref Full Text | Google Scholar

Webster, S. W., Connors, E. C., and Sinclair, B. (2022). The social consequences of political anger. J. Politics 84, 1292–1305. doi: 10.1086/71897

Crossref Full Text | Google Scholar

Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychol. Rev. 92, 548–573. doi: 10.1037/0033-295X.92.4.548

Crossref Full Text | Google Scholar

Weisfeld, G. E., and Dillon, L. M. (2012). Applying the dominance hierarchy model to pride and shame, and related behaviors. J. Evol. Psychol. 10, 15–41. doi: 10.1556/JEP.10.2012.1.2

Crossref Full Text | Google Scholar

White, P. A. (2006). The role of activity in visual impressions of causality. Acta Psychol. 123, 166–185. doi: 10.1016/j.actpsy.2006.05.002

PubMed Abstract | Crossref Full Text | Google Scholar

Wiens, S. (2005). Interoception in emotional experience. Curr. Opin. Neurol. 18, 442–447. doi: 10.1097/01.wco.0000168079.92106.99

PubMed Abstract | Crossref Full Text | Google Scholar

Williams, L. A., and DeSteno, D. (2008). Pride and perseverance: the motivational role of pride. J. Pers. Soc. Psychol. 94, 1007–1017. doi: 10.1037/0022-3514.94.6.1007

PubMed Abstract | Crossref Full Text | Google Scholar

Wilson, T. D., Lindsey, S., and Schooler, T. Y. (2000). A model of dual attitudes. Psychol. Rev. 107, 101–126. doi: 10.1037/0033-295X.107.1.101

Crossref Full Text | Google Scholar

Wirtz, J. J. (2006). Responding to surprise. Ann. Rev. Polit. Sci. 9, 45–65. doi: 10.1146/annurev.polisci.9.062404.170600

Crossref Full Text | Google Scholar

Wortman, C. B., and Brehm, J. W. (1975). “Responses to uncontrollable outcomes: an integration of reactance theory and the learned helplessness model,” in Advances in Experimental Social Psychology, Vol. 8 (New York, NY: Academic Press), 277–336.

Google Scholar

Wundt, W. M. (1902). Outlines of Psychology. (C. H. Judd, trans.). Leipzig: W. Engelmann.

Google Scholar

Zajonc, R. B. (1968). Attitudinal effects of mere exposure. J. Person. Soc. Psychol. 9(Suppl. No. 2):h0025848. doi: 10.1037/h0025848

Crossref Full Text | Google Scholar

Zajonc, R. B. (1980). Feeling and thinking: preferences need no inferences. Am. Psychol. 35, 151–175. doi: 10.1037/0003-066X.35.2.151

Crossref Full Text | Google Scholar

Zeelenberg, M., and Pieters, R. (2006). “Looking backward with an eye on the future: propositions toward a theory of regret regulation,” in Judgments Over Time: The Interplay of Thoughts, Feelings, and Behaviors, eds. L. J. Sanna and E. C. Chang (New York, NY: Oxford University Press), 210–229.

Google Scholar

Zhang, Z., and Rosenberg, M. D. (2024). Brain network dynamics predict moments of surprise across contexts. Nat. Hum. Behav. 9, 554–568. doi: 10.1038/s41562-024-02017-0

PubMed Abstract | Crossref Full Text | Google Scholar

Zillmann, D. (1988). Cognition-excitation interdependences in aggressive behavior. Aggress. Behav. 14, 51–64. doi: 10.1002/1098-2337(1988)14:1<51::AID-AB2480140107>3.0.CO;2-C

Crossref Full Text | Google Scholar

Zlotnik, M. (2003). Yeltsin and Gorbachev: the politics of confrontation. J. Cold War Stud. 5, 128–164.

Google Scholar

Keywords: Affective Intelligence Theory, emotions, appraisal, functions, systems

Citation: Roseman IJ (2025) An Emotion System Theory to address gaps in Affective Intelligence Theory and conceptualization of emotional phenomena. Front. Polit. Sci. 7:1570686. doi: 10.3389/fpos.2025.1570686

Received: 04 February 2025; Accepted: 16 July 2025;
Published: 01 October 2025.

Edited by:

Cengiz Erisen, Yeditepe University, Türkiye

Reviewed by:

Hüseyin Batuhan Sar, Yeditepe University, Türkiye
Isabella Rebasso, University of Vienna, Austria

Copyright © 2025 Roseman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ira J. Roseman, aXJhLnJvc2VtYW5AcnV0Z2Vycy5lZHU=

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