Predictors of Coping With Expectation Violation: An Integrative Review

Abstract

The present review investigates factors that predict three processes that lead to persistence versus change of expectations after confrontation with expectation violations, based on the violated expectation (ViolEx) model and related models. We address four groups of predictors: (a) characteristics of the expectation, (b) characteristics of the expectation-violating event(s), (c) broader situational characteristics, and (d) personality characteristics. The bulk of studies conducted in this area looked at expectation change in the direction of the experienced violation (accommodation) as their central dependent variable. The strongest empirical support was found for accommodation being less likely and minimizing of the potential impact of the discrepant information (immunization) being more likely to occur (a) after the reality turns out to be worse rather than better than expected, (b) if disconfirming events are more ambiguous, and (c) if depressed rather than healthy people are confronted with better-than-expected events. Given the high heterogeneity between studies on assessed predictors, we recommend a more comprehensive and unifying approach that tests the relative impact and the interplay of the whole range of predictors across paradigms.

Keywords

expectation violation expectation disconfirmation ViolEx model coping

Expectations are beliefs about something that will occur or that will be revealed in the future (Hoorens, 2012). If expectations are disconfirmed, we speak of expectation violations. A number of models have been developed that explain why expectations are maintained or change after experiencing expectation violations: the model of coping with disconfirmed expectations by Roese and Sherman (2007), expectancy violations theory (e.g., Afifi & Burgoon, 2000; Burgoon, 2016), the expectation-disconfirmation model of consumer satisfaction (e.g., Pieters et al., 1995), associative learning theory (e.g., Rescorla & Wagner, 1972), the meaning maintenance model (Proulx & Inzlicht, 2012), the predictive processing framework (e.g., Ransom et al., 2020), and the ViolEx model (Gollwitzer et al., 2018; Rief et al., 2015; Rief & Glombiewski, 2016). Three core coping processes that contribute to persistence versus change of expectations have been identified across these models: minimization of the importance of expectation-disconfirming evidence, search for/production of future expectation-confirming evidence, and expectation change (Pinquart et al., 2021). For example, according to the violated expectation (ViolEx) model, individuals may respond to expectation violations in three different ways (Gollwitzer et al., 2018; Rief & Glombiewski, 2016). First, they may immunize their expectation against the discrepant information by ignoring it, doubting its validity, or by reformulating the expectation so that the former discrepant information can no longer disconfirm the expectation. For example, if students received a B instead of the expected A, they may perceive this as an exception from the rule, or they may change the expectation for the next exam to simply receiving a passing grade. Second, individuals may proactively decrease the risk of future expectation-disconfirming events (assimilation). For example, after receiving a B instead on the expected A, the students may work harder for the next exam. Finally, they may change their expectation in the direction of the disconfirming event (accommodation). For example, the students may now expect to receive a B on the next exam, an expectation that is consistent with the previous grade. The ViolEx model was influenced by Brandtstädter and colleagues’ model of coping with information that is inconsistent to the self-concept (e.g., Brandtstädter, 2007; Brandtstädter & Greve, 1994).

For predicting how individuals cope with expectation disconfirmation, solid knowledge is needed regarding conditions that promote or inhibit the use¹ of the strategies described above. The broad range of these predictors of assimilation, immunization, and accommodation in response to expectation violation has not yet been systematically addressed in the literature. Nonetheless, a number of assumptions can be synthetized from the literature. When focusing on responses toward expectation violations, it is self-evident that characteristics of the expectation and the expectation violation will be relevant predictors. The strength of the expectation (e.g., perceived certainty, previous confirmations, and elaborateness) and the credibility of the disconfirming event (e.g., unambiguousness) are particularly important, with the former promoting persistence and the latter promoting change of the expectation (Hohwy, 2017; Ransom et al., 2020; Roese & Sherman, 2007). Several authors have suggested that the size of the discrepancy between an a priori expectation and an expectation-disconfirming event is a major predictor of coping with expectation violation (e.g., Afifi & Burgoon, 2000; Roese & Sherman, 2007). In addition, the valence of expectations and disconfirming events plays a role as individuals tend to change their expectations more after better-than-expected events rather than worse-than-expected events (Garrett & Sharot, 2017; Sharot et al., 2011). Human behavior, such as coping with expectation violations, is also influenced by broader characteristics of the environment (e.g., whether the expectation is shared with relevant others) and individual characteristics (Hutchinson, 2019). Relevant, individual characteristics refer to dispositions that promote flexibility (accommodation) versus adherence to one’s beliefs, tenacity, and avoidance of conflicting information (Brandtstädter & Greve, 1994). Other individual characteristics, which will be discussed later, may also play a role.

Given the growing numbers of theoretical papers and empirical studies on coping with expectation disconfirmation, the goal of the present article was to provide a comprehensive review of the literature on factors that predict the use of immunization, assimilation, and accommodation after being confronted with an expectation violation and to identify empirical support for the proposed influences on coping with expectation violations. We address inadequacies of the available research and future research needs. These topics are of interest to readers from various areas of psychology. First, from a theoretical standpoint, this knowledge is relevant for the refinement and evaluation of the available models of coping with expectation violation. Second, it provides guidance for researchers regarding which potential influences on coping with expectation disconfirmation need to be analyzed more systematically due to lack of research or inconsistency between available results. Third, knowledge on predictors of coping with expectation violation is relevant for practitioners who want to change dysfunctional expectations, such as those contributing to persistent mental and psychosomatic disorders (e.g., anxiety disorders, depression, and chronic pain; Rief et al., 2015; Vlaeyen & Linton, 2000), contributing to insufficient support from teachers and parents for children (Strelow et al., 2020), or contributing to negative interactions with out-group members (Carnaghi & Yzerbyt, 2007). Applying this knowledge could also increase intervention effects. For example, Doering et al. (2018) proposed expectation-focused psychotherapy to improve clinical outcomes.

In our search for relevant literature, we reviewed articles that introduced the models on coping with expectation violations and searched the PsycINFO database for studies on coping with expectation violations (search terms were as follows: expectation violation OR expectation disconfirmation OR expectancy violation OR prediction error). Based on a general model of predictors of coping processes by Moos and Holahan (2003), the relevant factors will be categorized into four groups: (a) characteristics of the expectation, (b) characteristics of the disconfirming event(s), (c) broader situational characteristics, and (d) personality characteristics.

Characteristics of the Expectation

Valence of the Expectation

Coping with expectation violations depends upon the valence of the expectation and—as will be discussed at a later point—upon the valance of the violation. Individuals tend to respond differently to expectation violations depending on whether they initially expected something positive or negative. For instance, in a series of learning experiments, Fazio et al. (2004) showed that expectations about negative consequences of selecting a stimulus tended to be more stable than expectations about positive consequences because expectations about negative consequences encouraged avoidance. Grupe and Nitschke (2011), as well as Dieterich et al. (2016), assessed expectations evoked by a cue that preceded neutral and aversive pictures at exactly a 50/50 ratio. Individuals persistently overestimated the frequency of aversive pictures, most likely due to increased attention to aversive pictures following uncertain cues.

Research on perception of others demonstrates that expectations about a person having a more favorable trait are more easily disconfirmed than expectations about having a less favorable one (Rothbart & Park, 1986). Changing the expectation that a target person will behave aggressively or in a hostile manner may require more and stronger disconfirming evidence than changing the expectation that a person will behave nicely and in a friendly manner. We conclude from these studies that people tend to be more willing to accommodate after violations of positive expectations, compared with negative expectations, due to avoidance of testing negative expectations and/or immunizing against disconfirming information.

(Un-)Certainty of Expectations

According to the predictive processing framework, individuals with stronger expectations who are more confident, or even overconfident, are less likely to update their expectations (Benrimoh et al., 2018; Kube et al., 2020; Paulus et al., 2019); they will either misinterpret new information as support for their expectations or downplay disconfirming information (for a similar view from social psychology, see Roese & Sherman, 2007). For example, expectations that are accompanied by a high degree of certainty tend to promote expectation-consistent perception, as shown in research on hallucinations (Benrimoh et al., 2018) and interoception (Paulus et al., 2019). Yanagisawa and Mikami (2015) experimentally manipulated the certainty of an expectation about the weight and size of an object by using foggy glasses. They showed that, in cases of higher certainty, individuals were more likely to ignore that a new stimulus differed from their expectation, thus indicating data-oriented immunization. In a learning experiment, Spicer et al. (2020) showed that following surprising events, certain expectations that were evoked by a highly predictive cue were more persistent than less certain expectations evoked by a less predictive cue.

Degree of Elaborateness

This characteristic refers to the extent to which an initial expectation has become cognitively complex, or rich with information. Roese and Sherman (2007) suggested that, in the case of weakly elaborated expectations, individuals will be more likely to ignore discrepancies between expectations and new information. More precisely, individuals may lack expectation-relevant knowledge or expertise, so that expectations are vague and therefore hard to disconfirm. However, if such discrepancies are perceived, weakly elaborated expectations would be revised more readily. In contrast, the extent to which an initial expectation has become cognitively complex makes immunization strategies more likely to occur (Roese & Sherman, 2007). Other authors have suggested that simple beliefs or expectations may persist to limit cognitive complexity as simple beliefs may be more cognitively attractive than overcomplicated correction (Cook & Lewandowsky, 2011). Further research is needed for identifying conditions under which more versus less elaborated expectations tend to persist more.

Consistency of Previous Support for the Expectation

Reactions toward expectation violations are likely to depend on the ratio of remembered prior confirmations and disconfirmations. Research on the partial-reinforcement extinction effect indicates that expectations and behaviors learned under partial (intermittent) reinforcement are more persistent than expectations and behaviors built under continuous reinforcement (Hochman & Erey, 2013; Rescorla, 1999). In other words, if individuals have experienced both confirmations and disconfirmations in the past, expectation violations (i.e., change in the confirmation rate) will be less easily detected and less likely to lead to expectation change. In contrast, if an expectation has been constantly confirmed in the past, expectations change more rapidly after a few expectation violations.

Characteristics of the Disconfirming Event(s)

Intensity and Frequency of Expectation Disconfirmation

The model of coping with expectation disconfirmation by Roese and Sherman (2007), expectancy violations theory (Afifi & Burgoon, 2000; Burgoon, 2016), and learning theories (Niv, 2019; Rescorla & Wagner, 1972) argue that a major predictor of coping with expectation violation is the magnitude of the discrepancy between an a priori expectation and an expectation-disconfirming event. Onetime, mild expectation-discrepant events are easier to ignore than more frequent, more discrepant events. However, there is no agreement on whether larger discrepancies lead to larger expectation change. The “Delta-rule,” one of the most commonly used learning rules, assumes that larger discrepancies between expectations and disconfirming events will produce larger expectation change (Rescorla & Wagner, 1972). Many studies on serial learning (e.g., predicting subsequent numbers in a series of presented numbers) found support for the Delta rule (Nassar et al., 2010; Yanagisawa & Mikami, 2015).

However, Roese and Sherman (2007) and proponents of the representational learning framework (Niv, 2019) suggested that moderate discrepancies would lead to the strongest expectation change. By contrast, small discrepancies are more likely to be ignored, and large discrepancies are more likely to result in subtyping. Subtyping may be found most common if disconfirming events have some salient discriminative attributes. In fact, Seta and Seta (1994) found that mild deviation from expected behavior of a target person did not change observers’ beliefs that the target person would show expectation-confirming behavior in the future, thus downplaying the expectation-violating event. In contrast, a severe deviation from expected behavior changed participants’ expectation about how the target person specifically would behave in the future. However, they did not change expectations about how the target group would behave in general in the future. Although a specific expectation was changed, it was never generalized to expectations about other members of the target group, thus indicating subtyping. Similarly, Filipowicz et al. (2018) found that individuals were most likely to update their expectations after moderate discrepancies between expectations and events. In the case of strong discrepancies, participants seemed to base their predictions on a larger sample of previous outcomes rather than solely on the most recent (discrepant) one. When participants were asked about preconditions for updating their expectations after experiencing an expectation violation, about one quarter of them reported waiting for the same initially unexpected event to occur a number of times in quick succession. Hird et al. (2019) showed that the size of discrepancy between expected and actual pain intensity affected the intensity of pain perception. The effect of expectations on pain perceptions increased from low to medium discrepancies between expected and actual pain intensity. In the case of large discrepancies, the influence of expectation on perception decreased. Finally, Pinquart et al. (in press) found that, after performing worse than expected in an achievement task, students with higher self-reported immunization scores lowered their achievement expectation for the next trial less than those with low self-reported immunization scores. However, over a series of four successive trials with achievement that was worse than expected, the expectation-stabilizing effect of immunization became weaker and then nonexistent.

Credibility of the Disconfirming Information

The Predictive Coding framework proposes that the more individuals trust the credibility of expectation-disconfirming information, the more they update their expectations accordingly (Hohwy, 2017; Ransom et al., 2020). Source credibility is a major variable in the persuasion literature (e.g., Briñol & Petty, 2009; Petty & Wegener, 1998). In general, a message will be more persuasive when delivered by a trustworthy source (Hovland & Weiss, 1951; Pilditch et al., 2020). In line with this reasoning, Kube and Glombiewski (2020b) confronted participants with projections of the global temperature rise from the Federal Environment Agency, which were either presented as uncertain, certain, or with no additional information regarding certainty. Individuals who were skeptical about anthropogenic climate change were more likely to lower their expectations about a future temperature increase if the given information was presented as uncertain. Similarly, Kube, Rief, et al. (2019) showed that feedback about success on a test on “social competence” did not affect participants’ expectations about their performance in unknown tasks when they were also informed that the test’s validity has not yet been established. By contrast, participants who had not received information about the validity of the test changed their expectation based on the feedback. However, experimental manipulations of the credibility of tests did not always work as expected (Kube & Glombiewski, 2020a, 2020b).

Unambiguousness of the Expectation-Relevant Information

Unambiguous expectation-disconfirming information may be more likely to lead to expectation change than ambiguous information because it would be more difficult to process unambiguous information in a biased way (Epley & Kruger, 2005; Hoorens, 2012). Roese and Sherman (2007) suggested that ambiguous information is more likely to elicit the search for expectation-verifying information, so that expectations persist. In fact, there is empirical evidence for interpreting ambiguous and inconsistent information (i.e., a mix of confirming and disconfirming information) as consistent with one’s prior expectations (confirmation bias; for example, Darley & Gross, 1983; O’Brien, 2009). For example, Hoch and Ha (1986) found that consumers tend to rely on prior expectations that were based on advertising when their experience with the product was ambiguous. In contrast, consumers relied on the consumption experience after an unambiguous experience.

Valence of the Disconfirming Information/Event

If expectations predict desirable or undesirable events, disconfirming events can be better or worse than expected (Lebois et al., 2016). There is robust evidence for an asymmetrical update of expectations and beliefs after receipt of disconfirming information, colloquially referred to as the good news/bad news effect or optimistic reinforcement learning. Individuals are more likely to show expectation change (accommodation) in response to better-than-expected compared with worse-than-expected events (Chowdhury et al., 2017; Garrett & Sharot, 2017; Lefebvre et al., 2017; Sharot et al., 2011). Kube, Kirchner, et al. (2019) showed that the reduced updating of expectations after worse-than-expected events can be explained by immunizing against the unexpected information. Individuals discarded worse-than-expected performance feedback by questioning its credibility and perceiving it as atypical; higher level of immunization correlated with smaller changes of the task-specific expectations.

Controllability of the Disconfirming Event(s)

Pieters et al. (1995) suggested that consumers are more likely to become active to produce the expected outcomes if they have control over the provider’s behavior. Otherwise, individuals will change their expectations in response to the expectation violation. Similarly, Dalal and Agrawal (1987) showed that, after failing on an examination, individuals reduced the expectations about their achievement in a future examination of a similar nature more if they perceived the source of failure as stable and uncontrollable.

Broader Situational Characteristics (Including Relationship Variables)

Similarity of Expectation to Those of Relevant Others

Carolan (2017) suggested that violated expectations will change less if significant others share the same expectations. For example, if a group of high school friends expects to go to college, unexpected bad grades will not likely lead to abandoning the expectation of enrolling in college. In fact, a study showed that an increase in exposure to peer drinkers (who have positive expectations about alcohol consumption) led to an increase in positive expectations about alcohol use (Sadler, 2005). An experiment by Carnaghi and Yzerbyt (2007) showed that observers preserved consensual beliefs about a group after being confronted with counter-stereotypic group members. They were more likely to perceive people who behaved in an unexpected, counter-stereotypic way as atypical members of the group when the stereotype was shared among an in-group. In a study on processing information about a target person’s guilt, individuals were more likely to interpret ambiguous information in line with their expectation if they believed that their expectation was shared by another person (Willard, 2008).

Cognitive Load

Cognitive load refers to demand on the cognitive resources needed to store and process information. For example, individuals will have less working memory resources available for coping with expectation disconfirmation when it is challenging to extract all relevant information or when they simultaneously perform a second task. There is some controversy as to whether a higher cognitive load leads to less or more attention for expectation-disconfirming information while the ability for deeper cognitive processing is inhibited (see, for example, Sherman et al., 1998). A number of experiments by Sherman et al. (1998) showed that expectation-disconfirming (stereotype-disconfirming) information evoked stronger attentional processing than consistent information when cognitive resources were depleted. This indicates that ignoring expectation-disconfirming information will be inhibited. However, a high cognitive load also inhibited deeper encoding of disconfirming information. Thus, cognitively demanding forms of immunization, such as searching for reasons why the disconfirming event was an exception from the rule, will also be inhibited.

Prior Coping With the Disconfirming Information

Only one mode of coping with an expectation violation may not be sufficient if, for example, discrepant information cannot persistently be ignored. Therefore, researchers proposed a typical sequence of coping behaviors. Greve (2007) suggested that individuals react with immunizing processes first (perceptual avoidance, avoidance of acceptance of the information, and immunization). If immunization fails, individuals assimilate or accommodate their expectations. With regard to the latter processes, Brandtstädter and Greve (1994) suggest that the motivation to accommodate increases when confidence in assimilation is eroded by repeated failure.

Mili (2020) found that, after expectation violation, students tended to react with assimilation first, followed by accommodation of their expectations if assimilation did not lead to future expectation confirmation. As too few students had reported immunization in that study, the author could not test Greve’s claim that immunization may typically precede assimilation (Greve, 2007). Accommodation and assimilation may also be shown in parallel. For example, after disconfirmation of high achievement expectations in a prior exam, students reduced their achievement expectations and increased their effort and related performance in the next exam (Radhakrishnan et al., 1996).

Personality Characteristics

Goal-Related Coping Dispositions

The ViolEx model’s terms of accommodation, assimilation, and immunization are borrowed from Brandtstädter’s model on coping with information that is discrepant to the self-concept (e.g., Brandtstädter, 2007; Brandtstädter & Greve, 1994). Brandtstädter assessed individual dispositions in coping with discrepancies between actual and desired states, such as unattained goals (Brandtstädter & Renner, 1990). Individuals with high levels of tenacious goal pursuit (i.e., tenacity) show goal-focused resource mobilization and compensatory efforts to attain the goal. Flexible goal adjustment (i.e., flexibility), on the contrary, describes the tendency to employ accommodative strategies in the face of goal-outcome discrepancies. This means that individuals high in flexibility more readily adapt their goals and goal-related expectations. They also show “palliative” responses to reduce the aversiveness of failed goal attainment, for example, by concentrating on positive aspects of the negative event. While Brandtstädter’s questionnaire addresses coping with goal attainment failures rather than expectation violations, the coping dimensions of his model and the ViolEx model are similar to each other. Pinquart et al. (in press) found a small, albeit significant, association of children’s self-reported assimilative coping with violated achievement expectations and parental reports on their child’s dispositional tenacious goal pursuit. Whether significant results would also be found in adult samples remains to be tested.

Cognitive Preferences and Styles

Humans rely on their ability to structure information about the world into expectations, schemas, and rules that are simplified models of reality. This ability varies reliably between individuals, which is reflected by cognitive styles or epistemic preferences, such as personal need for structure (the preference for simple cognitive structures; Neuberg & Newsom, 1993), need for cognitive closure (the desire for clear answers as opposed to ambiguity; Kruglanski & Webster, 1996), and (in-)tolerance for ambiguity (viewing ambiguous stimuli in a neutral and open way or as a threat; Budner, 1962). Assessments of these traits are moderately to highly correlated (Neuberg et al., 1997), and some authors have grouped these traits under the label cognitive complexity (Ortega & Weinstein, 1988). In theory, higher trait levels might predispose individuals to ignore and resist expectation-inconsistent information (i.e., immunization, assimilation) to protect their models of the world (Neuberg & Newsom, 1993). In fact, individuals with a higher need for closure paid less attention to, and were less likely to, remember behavior that was inconsistent with their initial expectation (Dijksterhuis et al., 1996). When having the opportunity to choose between information that supports or challenges one’s decision to extend or end the contract of a manager, individuals with a higher need for cognitive closure showed a stronger tendency to pay attention to information that supported rather than challenged their expected decision (Hart et al., 2012).

However, more recent research demonstrates that the effects of a need for structure or cognitive closure are context-dependent. Selective attention for expectation-consistent information is mainly promoted when individuals have previously built explicit categories and were encouraged to engage in categorical processing (Dijksterhuis et al., 1996), and when available cognitive resources for information processing are limited (Strojny et al., 2016). Otherwise, a high need for structure or cognitive closure may even lead to an increase in both searching for and processing expectation-inconsistent information, possibly because thinking about such information might allow it to be reconciled with the expectation (Kemmelmeier, 2015; Strojny et al., 2016).

Preference for consistency is only weakly associated with the constructs discussed in the previous paragraph. It describes the individual’s desire for their beliefs, attitudes, perceptions, and behaviors to be congruent and consistent (Cialdini et al., 1995). With regard to expectations, Alós-Ferrer et al. (2016) used a risky-choice paradigm with monetary rewards to investigate the association between preference for consistency and decision inertia (i.e., sticking to a decision independent of decision outcomes). In each trial, participants took a ball from one of two urns that allowed for a possibility to win money, as well as a probabilistic inference on whether participants should stick with the chosen urn or change the urn for a second drawing. The authors found that individuals with a higher preference for consistency chose to stay with their first choice more often, even when changing urns would increase their probability of winning. In other words, participants with a high preference for consistency were less prone to show accommodation of their reward expectations associated with their choice. Thus, preference for consistency seems to inhibit accommodation and/or promote immunization of expectations to avoid doubts about the appropriateness of previous behavior.

Positive and Negative Affectivity

As the valence of expectations and disconfirming outcomes can be critical in influencing the selection of coping mechanisms, stable individual differences in the susceptibility to valent information may moderate the selection of coping strategies. In the case of positive affectivity, biased coping with expectation violations may contribute to stronger accommodation, better-than-expected outcomes, and/or stronger immunization against worse-than-expected outcomes. A very prominent positive affectivity trait relevant to expectations is optimism, the stable disposition to have positive outcome expectations that generalize across situations (Scheier & Carver, 1985). A recent study found that, after getting worse-than-expected achievement feedback, participants with high optimism scores showed a trend of updating their performance expectations less than those who scored low on optimism (Kube & Glombiewski, 2020b). In contrast, optimism did not predict stronger accommodation to better-than-expected feedback or weaker accommodation to worse-than-expected feedback in a study by Korn et al. (2014).

Optimism has repeatedly been associated with stronger effects of analgesic placebo expectations (Darragh et al., 2015; Kern et al., 2020). Importantly, stronger placebo effects among optimists might not only be caused by more positive expectations, but also by more efficient immunization against negative expectation violations. For example, it was found that—in contrast to optimist in the control condition or pessimists—only optimistic individuals who received analgesic expectation instructions reported less pain in the cold pressor task. In addition, dispositional optimism did not affect initial expectations about the pain intensity before immersing the hand into an ice water container. Thus, initial expectations did not mediate the effect of optimism on later pain judgments, supporting the suggested role of optimism for immunization against pain stimuli (Geers et al., 2010). Affective expectation research also provides evidence for more immunization tendencies in optimists, both for outcomes that are worse and better than expected. In one study, optimists who were promised a funny video, but were shown a boring one, rated the video as more positive than optimists who received no a priori information or pessimists. They also rated a video less positively when it was announced as boring but was rather funny (Geers & Lassiter, 2002). In a different study, participants had to judge whether the intensity of an emotional expression on the screen matched an expectation about the intensity that had been built before. Optimists tended to agree more often that the intensity of the emotion was consistent with their expectation, regardless of whether the shown emotion was more positive or more negative than expected (Morton et al., 2011).

On the other side of the affective spectrum, increased neuroticism, trait anxiety, and major depression have been associated with biased reactions toward expectation violations (Aue & Okon-Singer, 2015). One could suspect that highly neurotic, anxious, and depressed individuals (a) accommodate their expectations more strongly to worse-than-expected outcomes, and/or (b) immunize more strongly when negative expectations are disconfirmed by better-than-expected outcomes. In line with the latter assumption, Pavlovian fear conditioning studies have shown that individuals high in neuroticism or trait anxiety show extinction resistance once the previously threat-inducing cue is not followed by the aversive stimulus anymore. This indicates less accommodation of expectations about consequences of the former threat-inducing cue (e.g., Gazendam et al., 2015; Staples-Bradley et al., 2018). However, traits that reflect a dispositional cognitive dissonance between a positive hope and a negative expectation (e.g., rejection sensitivity, victim sensitivity) yield a different pattern: Participants high in victim sensitivity (i.e., a trait reflecting a strong need to trust others, yet a dispositional expectation that other people are untrustworthy) accommodate their social expectations more strongly to better-than-expected outcomes than people low in victim sensitivity, for example, when a target person behaves in a more friendly manner than expected (Süssenbach et al., 2016).

Turning to dispositional anxiety, individuals suffering from anxiety disorders take longer to reduce fear responding to former threat cues even when these cues do not predict harm anymore (e.g., Duits et al., 2015). Meanwhile, fear conditioning research does not support stronger accommodation to worse-than-expected outcomes in individuals with high-trait anxiety or neuroticism per se; fear conditioning paradigms typically employ unambiguous and highly predictable threats to which individuals adapt their expectations regardless of their trait anxiety/neuroticism levels (Lonsdorf & Merz, 2017). In research on academic achievement, a series of studies investigated the role of trait anxiety on a surprising violation of one’s performance expectations. They found that anxious individuals both decreased performance expectations after negative feedback more than low-anxious individuals and increased them more than low-anxious individuals after surprising positive feedback (Rychlak & Lerner, 1965; Shimkunas, 1970).

The diverging results of decreased accommodation in fear extinction research versus increased accommodation of performance expectations after better-than-expected outcomes are most likely explained by differences in expectation, confidence, and stability; fear extinction resistance has been observed after individuals have already completed several trials of fear conditioning. In contrast, the observed increased accommodation was based on studies where individuals worked on a performance task for the first time and in very few trials.

A series of experiments showed that, in contrast to healthy people, depressed individuals did not accommodate their negative performance expectations to positive performance feedback (Korn et al., 2014; Kube & Glombiewski, 2020a; Kube, Rief, et al., 2019). The authors attributed this to data-oriented immunization because providing cues that promoted immunization inhibited an update after the receipt of better-than-expected feedback, whereas providing immunization-inhibiting cues promoted expectation change (Kube, Glombiewski, et al., 2019; Kube, Rief, et al., 2019).

Metatraits of Maintenance Versus Change

A global perspective on personality and responding to expectation violation is provided by traits with broad transsituational application, such as the Big Five metatraits, Stability and Plasticity (DeYoung, 2015). According to DeYoung’s Cybernetic Big Five theory, individuals with high Stability levels (i.e., Emotional Stability, Conscientiousness, and Agreeableness) tend to show more behavior aimed at protecting goals, interpretations, and strategies. In contrast, high levels of Plasticity (i.e., Extraversion and Openness) predict more explorative behavior, such as creating new goals, interpretations, and strategies. Studies on associations between these metatraits and other variables do not (yet) include investigations on mechanisms of expectation maintenance versus change. One may speculate that high levels of Stability are related to low levels of accommodation and high levels of avoidance of expectation-disconfirming events and immunization against received discrepant information. It is less clear whether Plasticity predicts engagement in active behavior to approach expectation-confirming events, which would be a form of assimilative behavior, or higher accommodation of one’s expectations by approaching expectation-disconfirming events.

Discussion

The present review shows that many factors contribute to the ways of coping with expectation violations (see Table 1 for summary). Most evidence is available for reduced accommodation and increased immunization of expectations (a) after the reality turns out to be worse than expected rather than better, (b) if disconfirming events are more ambiguous, and (c) if depressed rather than healthy people are confronted with better-than-expected events. However, even here, the empirical evidence is based on a small number of studies. It should be mentioned that higher availability of empirical research for the three listed variables does not necessarily mean that these variables are also the strongest statistical predictors of how individuals cope with expectation violations as some potential predictor variables have not yet been systematically assessed. Until now, many predictors have been analyzed mainly in one specific context, such as performance on achievement tests (e.g., goal-related coping dispositions) or fear conditioning (e.g., neuroticism and anxiety). In addition, we noted a complete lack of empirical studies on some theoretically plausible predictors, such as dispositional use of the strategies of coping with expectation violation and personality metatraits.

Table 1.

Predictors of Assimilation, Accommodation, and Immunization after the Experience of Expectation Violation.

Predictors	Accommodation	Assimilations	Immunization
Characteristics of the expectation
Negative valence	−
Certainty	−		+
Elaborateness	−?		±^a?
Consistence of previous support	+		−
Characteristics of disconfirming information
Intensity of disconfirmation	Inverse U-shaped or +		U-shaped
Frequency of current disconfirmation	+		−
Credibility	+		−
Unambiguousness	+		−
Valence: Better than expected	+		−
Controllability	−	+
Characteristics of the broader situation
Similarity of expectation to those of relevant others	−		+
Cognitive load			±^b
Prior coping: Failed immunization and/or assimilation	+
Personality characteristics
Dispositional tenacious goal pursuit		+
Need for structure/cognitive closure		±^c	±^c
Need for consistency	−		+
Optimism	−/0 (after worse-than-expected information)		+
Neuroticism	− (after better-than-expected information)
Rejection/victim sensitivity	+ (after better-than expected information)
Trait anxiety	±^d
Depression	− (after better-than-expected information)		+ (after better-than-expected information)
Metatrait stability	−?	+?	+?

Note. “+” indicates a positive association between the predictor and coping process, “0” indicates lack of observed association, and “?” indicates that this assumption has not yet been empirically tested.

Negative association with non-detecting/ignoring, positive association with other forms of immunization. ^bDepending on the amount of cognitive resources needed for immunizing. ^cDepending on whether or not this coping process reduces uncertainty/ambiguity more than the other processes. ^dDepending on the previous amount of support for the expectation.

To gain reliable data on the relative effects of predictor variables, standardized expectation violation paradigms are required in which an extensive list of predictor variables is tested. In a first step, expectations could be induced and violated through adaptations of well-established paradigms (e.g., Kube & Glombiewski, 2020a, 2020b; Süssenbach et al., 2016). Researchers should include an extensive list of predictor variables rather than a single predictor because some of the predictors tend to be correlated. While this has already been shown with regard to different cognitive preferences and styles (e.g., Neuberg et al., 1997), perceived certainty of an expectation, degree of elaborateness, and the amount of previous expectation confirmation are most likely correlated as well (Jepma et al., 2020). Simultaneously, including these variables will provide insights into which of them best predicts persistence versus change of expectations, as well as the coping processes that contribute to persistence and change. In addition, they should directly assess all of the coping variables proposed by the ViolEx model and related models to separate effects of predictors on the different ways of coping. After testing the wide range of potential influences on coping within a unified research paradigm, subsequent studies should test whether the results on expectations in one domain can be generalized to different domains of expectations.

It is important for studies to also assess the interplay of situational and trait predictor variables as positivity of the discrepant information and dispositional optimism (Korn et al., 2014), as well as depressive symptoms (Korn et al., 2014; Kube, Rief, et al., 2019), have been shown to interact. The contradictory theoretical assumptions and empirical results on whether large discrepancies between expectations and disconfirming events lead to large expectation change (Nassar et al., 2010; Rescorla & Wagner, 1972; Yanagisawa & Mikami, 2015), or immunization against the discrepant information and persistence of the expectation (Filipowicz et al., 2018; Kube et al., 2019; Niv, 2019; Roese & Sherman, 2007; Seta & Seta, 1994), call for more attention to the boundary conditions for effects of predictor variables. For example, personality characteristics may have larger effects on the ways of coping with expectation violations if the discrepant information is more ambiguous.

While studies on event characteristics most often employed an experimental design that provides information on causality, they were mostly nonexperimental, correlational studies on the association of personality characteristics with coping with expectation violations. Although some variables such as personality traits cannot be experimentally manipulated, well-designed quasi-experiments that eliminate as many threats to internal validity as possible are needed (Leary, 2001). Thus, we recommend that strong, internally valid empirical designs are used in future research on factors that causally predict the occurrence of coping strategies.

Most available studies on predictors of coping with expectation violations assessed accommodation, whereas very few studies measured assimilation (Table 1). Many researchers may have been more interested in expectation change than in the persistence of expectations and related processes of assimilation and immunization that contribute to persistence. In addition, as accommodation indicates expectation update by definition, it can be easily assessed by retesting expectations compared with other strategies for which separate measures have to be developed. The seeming lack of studies on assimilation can be explained by the fact that assimilative behavior after the experience of an expectation violation is focused on the next cycle of expectation (dis)confirmation and is therefore only relevant when similar events repeatedly occur over time. In addition, this behavior will only be relevant if individuals expect to have some control over the occurrence of the future event (Dalal & Agrawal, 1987; Pieters et al., 1995). Finally, specific assimilative behaviors have to be measured according to the violated individual expectation, such as making more efforts for preparing the next exam or trying to change the behavior of a friend. This makes the assessment of assimilation more challenging than the assessment of accommodation and some forms of immunization as well, such as interpreting the expectation violation as an exception to the rule. In regard to getting more knowledge about effects of predictor variables on the whole range of coping strategies, more efforts are needed for developing assessments of assimilation and immunization, and applying these measures simultaneously in studies of coping with expectation violations.

The present review focused on four groups of influences on coping with expectation violations. We did not address whether coping may also vary by sociodemographic characteristics. Some research is available on age differences, but the results are difficult to generalize as different expectations have been assessed in different age groups, measures of coping differed across studies (e.g., Chowdhury et al., 2017; Pinquart & Block, 2020), and most studies addressed only a narrow age range. In principle, the same assessments could be applied from mid-adolescence until old age. In contrast, adaptations or the development of unique assessment methods are necessary when working with children with lower verbal and cognitive skills (e.g., Lessing et al., 2019). The present review also did not address genetic influences that build a biological base for personality differences in coping with expectation violation (see, for example, the study on the association of the dopaminergic COMT val158met polymorphism with susceptibility to expectation confirmation bias by Doll et al., 2011).

In sum, scientific knowledge on factors that affect how people cope with expectation violations has been growing recently, with almost two thirds of the cited studies conducted in the past decade. We conclude that the most consistent findings are available with regard to (a) a selective update of expectations after better-than-expected rather than worse-than expected events, (b) a stronger update in response to unambiguous expectation disconfirmations, and (c) an inhibition of expectation change if depressed individuals experience better-than-expected events. Nonetheless, we need more unifying research paradigms to test the whole range of potential predictors. As shown in the present review, the available research provides valuable insights in which factors affect coping with expectation violations and should therefore be included in future large-scale studies.

Future research should (a) provide a systematic test of the broad range of potential influences on coping with expectation violations and their interplay, (b) use this empirical knowledge for the refinement of the theoretical models in that field, and (c) apply this knowledge for the development of interventions aimed at changing persistent dysfunctional expectations, such as those contributing to persistent mental and psychosomatic disorders (Doering et al., 2018; Rief et al., 2015; Vlaeyen & Linton, 2000) or negative stereotypes about out-groups (Carnaghi & Yzerbyt, 2007).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was conducted in the context of the Research Training Group “Expectation Maintenance vs. Change in the Context of Expectation Violations: Connecting Different Approaches” funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG Ref. No. 290878970-GRK 2271).

ORCID iDs

Martin Pinquart

Mario Gollwitzer

Notes

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