Running head: BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 1

When our beliefs face reality: An integrative review of belief updating in mental health and

illness

Tobias Kube*

Harvard Medical School, Program in Placebo Studies, Beth Israel Deaconess Medical

Center, 330 Brookline Avenue, Boston, 02115 Massachusetts, USA

AND

University of Koblenz-Landau, Department of Psychology, Clinical Psychology and

Psychotherapy, Ostbahnstraße 10, D-76829 Landau, Germany

Liron Rozenkrantz*

Harvard Medical School, Program in Placebo Studies, Beth Israel Deaconess Medical

Center, 330 Brookline Avenue, Boston, 02115 Massachusetts, USA

AND

Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences,

77 Massachusetts Avenue, Cambridge, MA 02139, USA

*Both authors contributed equally to this work.

Correspondence can be addressed to: Tobias Kube, PhD, University of Koblenz-Landau,

Department of Psychology, Clinical Psychology and Psychotherapy, Ostbahnstraße 10, D-

76829 Landau, Germany, Email: [email protected], Phone: +49 6341 28035652 BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 2

Abstract

“Belief updating" is a relatively nascent field of research, in which it is examined how people update their beliefs in the light of new evidence. So far, belief updating has been investigated in partly unrelated lines of research from different psychological disciplines. In this article, we aim to connect these disparate lines of research in an integrative approach.

After presenting some prominent theoretical frameworks and experimental designs that have been used for the study of belief updating, we review how healthy people and people with mental disorders update their beliefs after receiving new information that supports or challenges their views. Available evidence suggests that both healthy people and people with particular mental disorders are prone to certain biases when updating their beliefs, although the nature of the respective biases varies considerably (depending on the particular mental disorder, for instance). Anomalies in belief updating are discussed both in terms of new insights into the psychopathology of various mental disorders and societal implications, such as irreconcilable political and societal controversies due to the failure to take information into account that disconfirms one's own view. We conclude by proposing a novel integrative model of belief updating and derive directions for future research.

Keywords: Bayesian brain; belief updating; mental health; optimism bias; predictive processing BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 3

Introduction

Current political controversies (in Western countries), such as the debate about the

“Brexit” in the United Kingdom or discussions about possible misconducts of the President of the United States, reveal that conflicting parties seem to have diverging perspectives on it and do not take into account information that contradicts their views, resulting in fairly divisive and irreconcilable controversies. We believe that much of this dilemma relates to how people construe their subjective reality and how they deal with information that disconfirms it. Therefore, this article aims to review the literature on how beliefs shape perception and how people update their beliefs in the light of new evidence. After a brief introduction to the importance of beliefs in mental health and illness, we will present prominent frameworks and experimental paradigms for the investigation of belief updating.

In the main part of this article, we will then review what is known about characteristics of belief updating in healthy people and certain mental disorders. Subsequently, we aim to synthesize previous research in an integrative model of belief updating and will discuss its implications both with regard to new insights into the psychopathology of some mental disorders and societal issues. Finally, we will critically evaluate strengths and shortcomings of past research into belief updating and provide some suggestions for future work.

The importance of beliefs in human life

How beliefs modulate perception and well-being

It is becoming increasingly evident that people’s beliefs significantly modulate their perception of - and response to - the world, affecting various aspects of life, from decision- making to well-being. Empirical support for the role of beliefs in well-being comes from several studies linking large-scaled national health surveys with databases of well-being measures. These studies suggest that holding negative beliefs about aging in young adulthood is associated with up to three times higher likelihood of experiencing BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 4 cardiovascular events 38 years later (Levy, Zonderman, Slade, & Ferrucci, 2009). Further, individuals reporting high levels of stress had a 43% increased risk of premature death, only if they perceived stress as significantly impacting health (Keller et al., 2012). Also, individuals who perceived themselves as less physically active than others had an up to 71% higher mortality risk as compared those who believed they were more active than others, even after controlling for actual levels of activity (Zahrt & Crum, 2017).

A more direct link between beliefs and health can be drawn from research into placebo effects, where expectations for symptom improvement have been found to contribute significantly to clinical outcomes after an inert treatment (i.e. placebo) (for reviews see e.g.

Petrie and Rief (2019) and Schedlowski, Enck, Rief, and Bingel (2015)). Neuroimaging studies found that even prior to treatment, expectations modulate treatment-relevant neuronal processes, and these modulations mediate treatment outcomes (Wager et al., 2004;

Zilcha-Mano et al., 2019). Expanding our knowledge about the effects of expectations on pain perception, two recent neuroimaging studies revealed that the expectation of pain-relief directly influenced the perception of pain. At the neurobiological level, higher-order brain regions acted on lower-order brain regions to suppress coding of the dissonance between expectation and perception (Jepma, Koban, van Doorn, Jones, & Wager, 2018; Schenk,

Sprenger, Onat, Colloca, & Büchel, 2017). In other words, the brain matches its perception to its expectations, much like a self-fulfilling prophecy.

Remarkably, this phenomenon of expectation-guided perception also affects human’s most dominant sense: vision. It was found that expectations and desires influence the very early stages of visual information processing by activating brain regions associated with the expected visual stimulus, biasing perceptual judgement (Balcetis & Dunning, 2006; Leong,

Hughes, Wang, & Zaki, 2019). Moreover, it has been found that even performance is BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 5 affected by beliefs and expectations, as demonstrated by research indicating enhanced cognitive abilities such as memory and creativity after placebo treatment (Parker, Garry,

Einstein, & McDaniel, 2011; Rozenkrantz et al., 2017; Weger & Loughnan, 2013).

Collectively, these findings reveal that human information processing, from sensory perception to decision making, is significantly shaped by people’s beliefs.

When our beliefs make us vulnerable

The importance of beliefs is further emphasized by studies using clinical samples, indicating vulnerabilities resulting from dysfunctional1 beliefs. For instance, research from behavioral medicine has shown that dysfunctional beliefs of patients about their illness can have adverse effects on symptom development and treatment success. In patients with coronary heart disease, e.g., negative beliefs about the controllability and consequences of the disease predict worse symptom recovery from myocardial infarction (Petrie, Weinman,

Sharpe, & Buckley, 1996) and reduced benefit from cardiac surgery (Juergens, Seekatz,

Moosdorf, Petrie, & Rief, 2010; Kube, Meyer, et al., 2019). Even long-term mortality has been found to be increased for patients with dysfunctional illness perceptions (Barefoot et al., 2011). Similar results have been provided for patients with chronic obstructive pulmonary disease (Zoeckler, Kenn, Kuehl, Stenzel, & Rief, 2014), breast cancer (Nestoriuc et al., 2016), diabetes (Griva, Myers, & Newman, 2000; Mann, Ponieman, Leventhal, &

Halm, 2009), and chronic pain (Cormier, Lavigne, Choinière, & Rainville, 2016).

Dysfunctional beliefs are also regarded as core features of almost all mental disorders. In major depression, for instance, it has been shown that dysfunctional expectations about one’s

1 It should be noted that there is to date no widely accepted definition of when beliefs are “dysfunctional”. In this article, we refer to this term mostly when speaking about beliefs that are overly negative; however, lack of positive beliefs (which may not be the same as overly negative beliefs) is also often regarded as dysfunctional. More generally, researchers often refer to the term “dysfunctional beliefs” when describing particular types of beliefs that are associated with certain mental health problems. Sometimes, researchers also use the term “maladaptive” beliefs, which is mostly interchangeable to dysfunctional beliefs. To avoid confusion, though, we use only the term “dysfunctional beliefs” in this article. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 6 own abilities and the personal future predict the course of depressive symptoms and suicidal ideation (Czyz, Horwitz, & King, 2016; Horwitz, Berona, Czyz, Yeguez, & King, 2017;

Kube et al., 2018). In post-traumatic stress disorder (PTSD), dysfunctional beliefs in relation to the traumatic experience are assumed to be a core factor that contributes to the development and maintenance of the disorder (Ehlers & Clark, 2000). For instance, a recent study by Murray (2018) has shown that beliefs about being guilty after the survival of a traumatic event is very common among PTSD patients and is associated with higher PTSD symptom severity. Further evidence for the contribution of dysfunctional beliefs to the development and maintenance of mental health problems has been provided for social anxiety (D. M. Clark & Wells, 1995; Rapee & Heimberg, 1997); hypochondriasis (Barsky et al., 2001; Barsky, Coeytaux, Sarnie, & Cleary, 1993); obsessive-compulsive disorders

(Hansmeier, Exner, Rief, & Glombiewski, 2016; Salkovskis et al., 2000; Shafran,

Thordarson, & Rachman, 1996), eating disorders (Corstorphine, 2006; Konstantakopoulos et al., 2012; Waller, Dickson, & Ohanian, 2002), and schizophrenia (Lincoln, Mehl, et al.,

2010; Moritz et al., 2010).

Belief updating: The integration of new information into existing beliefs

The evidence summarized in the brief introduction above suggests that beliefs tremendously influence the way we perceive our world, and constitute a major risk factor of mental illness in the case of dysfunctional beliefs. An important aspect in this context is that our beliefs do not reflect the world as it actually is, but our construction of it. In other words, beliefs construe a template through which we perceive ourselves, other people, and the world in general. With this template in mind, beliefs are permanently tested against the background of facts and new experiences. As illustrated in Figure 1, such new information can either confirm or disconfirm prior beliefs, each resulting in different effects of the new information BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 7 on previous beliefs. Anomalies in belief updating in the light of new information, as found in some clinical populations, will be an important part of this article.

Insert Figure 1 here.

The basic design of research studies in the field of belief updating includes a first assessment of participants’ beliefs before they receive new information (that can support or challenge their prior beliefs), followed by a second assessment of beliefs. Thus, this basic design enables researchers to examine the extent to which beliefs are updated after receiving new information. Importantly, researchers using this study design are often interested in relating specific patterns of belief updating to different psychological processes, such as: anticipatory reactions; encoding or interpreting new information; and further processing or appraising new information. As illustrated in Figure 2, at each of these stages of information processing, experimental manipulations can be performed if researchers are interested in the influence of a particular psychological process on the update of beliefs. As will be discussed later in this article, distortions in these psychological processes may lead to anomalies in belief updating, such as persistent lack of belief update or hasty changes in beliefs.

Insert Figure 2 here.

Before we discuss such abnormalities in comparison to belief updating in healthy people, we will first introduce a powerful current theory of cognition and the brain, which has become increasingly important in the field of belief updating, referred to as predictive processing.

A prominent framework in the study of belief updating: predictive processing

The roots of the predictive processing framework (PPF) go back to von Helmholtz

(1867), who introduced the concept of unconscious inference, assuming that the human brain BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 8 fills in visual information to make sense of a visual scene. In recent years, predictive processing has become a dominating theoretical framework in neuroscience and related areas interested in operating principles of the brain. The basic idea of the PPF is that perception is inextricably linked to prediction, meaning that prior predictions bias perception, which in turn shapes posterior predictions. In particular, researchers using the PPF assume that the brain does not “passively” process incoming sensory signals; instead, it permanently generates predictions about expected sensory input, which is then compared with prior predictions (A. Clark, 2013b; Huang & Rao, 2011; O’Reilly, Jbabdi, & Behrens, 2012). Any mismatches between prior predictions and actual sensory signals are referred to as prediction errors. Prediction errors normally provide corrective feedback and are used to update future predictions (referred to as belief updating). Appealing to the “free energy principle” (Friston

& Kiebel, 2009; Moran et al., 2013), neuroscientists suggest that the brain aims to adjust predictions such that prediction errors are minimized and incoming sensory information is optimally used (Barrett & Simmons, 2015). With active inference, people can also fulfill their predictions by acting on the world in such a way that prediction errors are minimized

(Friston et al., 2015; Pezzulo, Rigoli, & Friston, 2015; Seth & Friston, 2016).

In active inference, precision plays a crucial role. As with all forms of Bayesian inference, the precision of various sources of sensory evidence - or indeed prior beliefs - can have a profound effect on belief updating (Ernst & Banks, 2002). These effects will become prescient later when we consider aberrant precision and the false inferences that accompany psychopathology. Simple examples here include inferring something is there, when it is not

(i.e., hallucinations) or something is not there, when it is (i.e., agnosia). Technically, the key determinant of the rate of belief updating or evidence accumulation is the precision afforded to prior beliefs, relative to sensory evidence (i.e., sensory prediction errors). In other words, a weakening of prior precision corresponds to a relative increase (or failure to attenuate) BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 9 sensory precision and vice versa. To keep things simple, we will describe this imbalance of precision in terms of prior precision (Pellicano & Burr, 2012). Psychologically, getting the right balance between prior and sensory precision has been associated with attention; such that attending to a particular source of sensory evidence enhances the corresponding precision of prediction errors and the accompanying rate of belief updating (Feldman &

Friston, 2010; Kanai, Komura, Shipp, & Friston, 2015; Parr & Friston, 2019; Vossel,

Mathys, Stephan, & Friston, 2015). From a neurobiological perspective, this is thought to be encoded by the excitability or post synaptic gain of neuronal populations encoding prediction error; namely superficial pyramidal cells. This will become an important aspect later when we talk about the pathophysiology of conditions like schizophrenia and Parkinson's disease - that usually involve an abnormality of neuromodulatory transmitter systems (e.g., dopamine). In short, a pathophysiology of neuromodulation may translate into a failure of belief updating due to aberrant precision control (A. Clark, 2013a; Haarsma et al., 2018; C.

E. Palmer, Auksztulewicz, Ondobaka, & Kilner, 2019; C. J. Palmer, Seth, & Hohwy, 2015;

Rae, Critchley, & Seth, 2019; Van de Cruys et al., 2014; Vuust, Dietz, Witek, &

Kringelbach, 2018).

Originally, the PPF has been developed in neuroscience and provided a new understanding of sensory processing. In recent years, several clinical applications of the PPF have been proposed, e.g. for mental disorders such as depression (Barrett, Quigley, &

Hamilton, 2016; J. E. Clark, Watson, & Friston, 2018; Kube, Schwarting, Rozenkrantz,

Glombiewski, & Rief, in press), PTSD (Wilkinson, Dodgson, & Meares, 2017), and psychosis (Corlett et al., 2019; Sterzer et al., 2018); persistent physical symptoms (Edwards,

Adams, Brown, Parees, & Friston, 2012; Henningsen et al., 2018; Kube, Rozenkrantz, Rief,

& Barsky, in press; Van den Bergh, Witthöft, Petersen, & Brown, 2017); pain perception

(Wiech, 2016); and placebo analgesia (Büchel, Geuter, Sprenger, & Eippert, 2014; Grahl, BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 10

Onat, & Büchel, 2018; Ongaro & Kaptchuk, 2019). We will refer to some of these clinical applications of the PPF later in this article when discussing certain distortions related to belief updating in clinical populations.

Importantly, to our knowledge, there is to date no widely accepted agreement on how the

PPF precisely relates to belief updating; therefore, we briefly point out how we treat the two frameworks in relation to each other. Within the PPF, there is the idea of a predictive hierarchy, assuming that (the contents of) predictions differ in respect to the level of abstraction. That is, there are predictions at lower levels of the hierarchy that are close to the explanation of sensory stimuli (such as “This is a face”), whereas predictions at higher levels of the hierarchy reflect a higher degree of abstraction (such as “I like this person”) (Bastos et al., 2012; Pezzulo, 2014). While the former are thought to be mostly unconscious, the latter might be more accessible to conscious experience and reflect what researchers from other fields refer to as “beliefs”. In this article, we will thus refer to belief updating as an application of the PPF with respect to the update of higher-level predictions after the receipt of new information2. Sources of information can vary from factual information received from other people or the environment to exteroceptive sensory information and interoceptive sensations (that is, information from the inner milieu of the body).

Characteristics of belief updating in healthy people

Building upon the above presented frameworks for the investigation of belief updating, we next review the literature on characteristics of belief updating in people who are in good mental health; afterwards, we will focus on distortions in belief updating that have been observed in clinical populations. Notably, this review is not meant to be exhaustive; when we invoke findings from specific areas of research, our aim is not to review the respective

2 Of note, Bayesian statistics also refers to belief updating and regards beliefs as probabilities of prior knowledge, while belief-updating is seen as a computation of these probabilities after obtaining new data. This differs from beliefs as we treat them in this paper, namely a personal construe of the world. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 11 body of literature systematically, or to cover all scientific controversies being held therein.

Rather, we compile evidence from different psychological disciplines to examine how it contributes to the knowledge about belief updating.

The optimism bias

One particularly striking example of how healthy people integrate new information into their existing beliefs is the optimism bias that Tali Sharot and colleagues have studied extensively. According to this concept, (healthy) people’s beliefs about the future are often unrealistically optimistic, and remain so despite disconfirmatory evidence. Remarkably, in order to maintain optimistic beliefs about the future, people selectively embrace information that favors their optimistic view, while discarding information that contradicts it. In other words, people display asymmetric learning from new information, based on whether it confirms their existing (biased) beliefs (Lefebvre, Lebreton, Meyniel, Bourgeois-Gironde, &

Palminteri, 2017; Sharot, 2011; Sharot & Garrett, 2016). In recent years, this has been demonstrated in a series of experimental studies (Garrett & Sharot, 2017; Sharot, Guitart-

Masip, Korn, Chowdhury, & Dolan, 2012; Sharot, Kanai, et al., 2012; Sharot, Korn, &

Dolan, 2011; Sharot, Riccardi, Raio, & Phelps, 2007). In these studies, a trial-by-trial belief updating design was used: that is, participants are asked to estimate their risk for several adverse life events; next, they are presented with the real probabilities to encounter these events (that is, the correct estimation). When then asked to re-estimate their risk, participants preferably use this new knowledge when favorable, but not unfavorable, probabilities are presented. For example, an initial estimation of 30% risk for cancer might be updated to 22% following base-rate of 20% (favorable information; large update), but an initial estimation of

10% risk might be updated to 13% following the same base-rate (unfavorable information; smaller update) (Sharot & Garrett, 2016). Notably, this “good news-bad news” effect has BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 12 also been found in found in research from behavioral economics, indicating that people make their decisions not solely based on rationality (Eil & Rao, 2011).

A series of neuroimaging studies elucidated the neural mechanisms of the optimistic bias: it was shown that undesirable information is coded differently than desirable information, which is thought to underlie the relative neglect of the integration of the former

(Kuzmanovic, Jefferson, & Vogeley, 2016; Sharot, Kanai, et al., 2012; Sharot et al., 2011).

Thus, over-optimistic beliefs are further reinforced by a selective neural filter that generates a feedback loop which facilitates the persistency of biased beliefs. A further recent study found that this optimistic bias in belief updating diminishes when people are under perceived threat: In both experimentally induced stress and firefighters on duty, Garrett, González-

Garzón, Foulkes, Levita, and Sharot (2018) found that under perceived threat, healthy people are more sensitive to bad news and better integrate it into their beliefs. The authors interpreted their findings in the light of psychological flexibility, meaning that healthy people can flexibly adjust their otherwise optimistic belief updating bias when situational circumstances require it.

Cognitive consistency and confirmation bias

According to theories from cognitive and social psychology, people strive for cognitive consistency, meaning that they aim to reduce contradictions between different beliefs. One of the most influential theories in this context is the theory of cognitive dissonance by

Festinger (1962). This theory proposes that holding conflicting cognitions is perceived as being aversive, resulting in the preference to reduce this cognitive dissonance by cognitive or behavioral strategies. To apply this theory to the terminology of belief updating, consider the example of a healthy person holding the belief, “Overall, the world is safe”. If the person was confronted with disconfirming information, such as reported crimes in the news, a BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 13 conflicting cognition such as, “Some people endanger safety” might come up. Since this cognition is in contradiction to the belief in a safe world, the person may aim to reduce cognitive dissonance, e.g., by thinking, “These things only happen to others. My personal world is safe.”

Relatedly, other lines of research from cognitive psychology have dealt with the phenomenon of confirmation bias. This means that people have the propensity to interpret information in such a way that their pre-existing beliefs are confirmed (Nickerson, 1998;

Oswald & Grosjean, 2004). According to this concept, people generally assume that their beliefs are true, which is why they use their pre-existing beliefs as a heuristic to evaluate new information. Further, people tend to use “positive test strategies,” meaning that they prefer strategies that are likely to confirm prior beliefs when integrating novel information

(Klayman & Ha, 1987).

Note that we have moved beyond perceptual processing and now consider the problem of active inference; namely, how to optimize belief updating when we are in charge of gathering the sensory evidence upon which to base our beliefs. Active inference takes the view that the best way to gather data is to resolve uncertainty in relation to prior beliefs.

Implicit in this formulation is a 'Bayes-optimal optimism bias'. In other words, if we are gathering evidence for our models of the world, we will naturally tend to select those data that provide confirmatory evidence (Bruineberg, Kiverstein, & Rietveld, 2018). Crucially, active inference does not just deal with selecting sources of sensory information (e.g., which news channels to watch). There can be mental actions in terms of deploying precision or attention to various modalities or hierarchical levels of inference (Limanowski & Friston,

2018). In other words, positive test strategies can also be manifest in the things that we intend to - and the things that we ignore, without any overt action. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 14

Self-concept stability

Another bias in belief updating among healthy people has been revealed in social and personality psychology. In this field, it is well-established that people’s beliefs about themselves remain remarkably stable over time. One aspect contributing to this self-concept stability is that people selectively search for information that confirms their self-concept

(Swann & Read, 1981a, 1981b). In other words, people are inclined to search for information that is consistent with their beliefs about themselves. To put it in active inference terms, when it comes to self-concept relevant information, people act on the world to gather evidence that supports their perceptual model of the self. Conversely, when people receive new information that is inconsistent with their self-concept, they are likely to reject it, which further contributes to the self-concept stability (Markus, 1977; Markus & Wurf,

1987; Swann & Hill, 1982). With regard to the latter, one study, for instance, has shown that participants’ expectations for their performance were highly influenced by their pre-existing level of self-esteem, but not by novel performance feedback received during the task

(McFarlin & Blascovich, 1981). Another study has demonstrated that also the valence of self-relevant information influences belief updating: Korn, Prehn, Park, Walter, and

Heekeren (2012) have shown that people update their beliefs about themselves after receiving social feedback from peers more toward desirable information than toward undesirable information, suggesting that healthy people are positively biased when integrating social feedback into their beliefs about themselves. Thus, collectively, research into the self-concept suggests that belief updating in healthy people is biased toward the integration of (positive) self-concept congruent information, whereas self-concept discrepant information is disregarded, thereby maintaining the person’s self-perception. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 15

Truth decay and post-truth: persistence of political attitudes

Insightful examples of how selectively people use novel information to update their beliefs, can also be found in the literature on political attitudes. Research in this area suggests that people stick to their attitudes and opinions regardless of disproving facts

(Taber, Cann, & Kucsova, 2009; Tappin, van der Leer, & McKay, 2017). This has been referred to as a "truth decay", meaning that factual evidence is insufficiently taken into account in political opinion-forming and decision-making (e.g. in elections) (Kavanagh &

Rich, 2018). Authors who assume that this truth decay has increased in recent years even speak of a “post-truth” era (Jasanoff & Simmet, 2017; Sismondo, 2017) or “post-factual” politics (Sayer, 2017) when characterizing the current (Western) political discourse.

Prominent examples from current political discussions for the persistence of political beliefs despite disconfirmatory evidence are climate change denial (G. T. Farmer & Cook, 2013;

Häkkinen & Akrami, 2014; McCright & Dunlap, 2011), the “vaccination confidence gap”

(Browne, Thomson, Rockloff, & Pennycook, 2015; Larson, Cooper, Eskola, Katz, & Ratzan,

2011; Tafuri et al., 2014), and the discussion about gun control in the United States

(Rogowski & Tucker, 2018).

After reviewing basic characteristics of belief updating in healthy people, we will now turn to belief updating in certain clinical populations.

Between persistence and hasty changes: Belief updating in clinical populations

As discussed in the first part of this article, dysfunctional (i.e. overly negative) beliefs are regarded as core features of almost all mental disorders. Consequently, researchers were interested in whether dysfunctional beliefs are adjusted if people with certain mental health problems receive new information that questions the validity of such beliefs. In this context, some interesting results have been provided in recent years, converging on the finding that BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 16 people with various mental disorders have difficulty updating disorder-specific dysfunctional beliefs.

Lack of updating disorder-specific beliefs

In major depression, it is well-established that negative beliefs persist despite disconfirming positive information. One study, for instance, found that people with major depression, unlike healthy subjects, maintained negative performance-related expectations despite disconfirming positive performance feedback (Kube, Rief, Gollwitzer, Gärtner, &

Glombiewski, 2019). Similarly, two other studies showed that once negative beliefs in social situations were established, people with depressive symptoms had difficulty using novel positive information to adjust their negative beliefs (Everaert, Bronstein, Cannon, &

Joormann, 2018; Liknaitzky, Smillie, & Allen, 2017). In contrast, another recent study found no differences between healthy people and people with depression in updating positive beliefs after novel negative information (Kube, Kirchner, Rief, Gärtner, & Glombiewski,

2019), suggesting that there are no pathologies in belief updating per se in depression, but that this applies only to the revision of negative prior beliefs, a well-known core feature of depression (Beck & Haigh, 2014; Beck, Rush, Shaw, & Emery, 1979). Moreover, when it comes to the revision of beliefs about the undesirable life events in the face of new factual information, research has demonstrated that the above-referenced optimism bias (which is typical of healthy people) is absent in people with depression (Garrett et al., 2014; Korn,

Sharot, Walter, Heekeren, & Dolan, 2014).

Besides major depression, people with social anxiety (Koban et al., 2017) and borderline personality disorder (BPD) (Korn, La Rosée, Heekeren, & Roepke, 2016; Liebke et al.,

2018) were found to have problems updating their beliefs in accordance with social feedback. With regard to BPD, research has also revealed that people with BPD have BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 17 difficulty using performance feedback to avoid disadvantageous, risky decisions

(Schuermann, Kathmann, Stiglmayr, Renneberg, & Endrass, 2011; Svaldi, Philipsen, &

Matthies, 2012). Furthermore, it has been shown that people with obsessive-compulsive disorder, unlike healthy people, overestimate the likelihood of experiencing an adverse event in disorder-related areas (e.g., related to checking, estimating the average percentage of accidents per German household each year), and continue to worry about these issues even after receiving the correct statistics (Moritz & Jelinek, 2009; Moritz & Pohl, 2009). In a similar vein, patients with schizophrenia were found to fail to adjust delusion-related beliefs after receiving new information that disconfirms delusional beliefs (Speechley, Ngan,

Moritz, & Woodward, 2012; Woodward, Moritz, Cuttler, & Whitman, 2006; Woodward,

Moritz, Menon, & Klinge, 2008). In addition, researchers indicated that people with the somatization syndrome (i.e., experiencing multiple somatic symptoms that cannot be adequately explained from a medical point of view) continue to be concerned about having a serious illness despite receiving medical reassurance (Donkin et al., 2006; Nijher, Weinman,

Bass, & Chambers, 2001; Rief, Heitmuller, Reisberg, & Ruddel, 2006).

All these examples speak to the notion that people with certain mental health problems maintain such kind of beliefs that reflect core features of the particular disorder, despite disonfirmatory evidence. Intriguingly, however, there is also an opposite pattern of belief updating that has been reported in some clinical populations: hasty changes in beliefs based on fairly thin evidence, as will be reviewed next. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 18

The other side: Hasty changes in beliefs3

Jumping-to-conclusion bias in psychosis. To explain the genesis and maintenance of unsubstantiated delusional beliefs in people with schizophrenia (e.g., the belief of being pursued by the CIA because an unknown car is parked in front of the house), Hemsley and

Garety (1986) introduced the hypothesis of a jumping-to-conclusion bias (JTC). According to this hypothesis, deluded patients update their beliefs hastily in the direction of a feared event, on the basis of remarkably little evidence. This hypothesized JTC bias has been empirically confirmed in a number of studies (Garety, Hemsley, & Wessely, 1991; Lincoln,

Ziegler, Mehl, & Rief, 2010; Moritz & Woodward, 2005). In most studies examining the

JTC bias, a bead task was used. In this task, participants are presented with two glasses containing colored beads in different proportions (e.g., glass A containing 85% red beads and 15% blue beads; glass B: 85% blue beads and 15% red beads). Glasses are then removed and beads (one at a time) are drawn from one of the two containers, with participants being unable to see from which of the containers the bead was drawn. After each bead, the participants’ task is to indicate whether they can make a decision as to from which of the container the beads were drawn. Consistent with the hypothesized JTC bias, deluded schizophrenic patients indicate to be sure from which glass the beads come from earlier than healthy and clinical control subjects. Intriguingly, 40-70% of deluded schizophrenic patients state already after the first bead that they would know which container the bead comes from

(Fear & Healy, 1997; Garety et al., 1991; Huq, Garety, & Hemsley, 1988).

More recently, it has been suggested that a liberal acceptance bias may additionally contribute to biased belief updating in schizophrenia, meaning that people with

3 When examining the examples of hasty changes in beliefs, it should be noted that some of the research studies did not use the basic study design presented above (first assessment of beliefs; presentation of new evidence; second assessment of beliefs). Notwithstanding this methodological shortcoming, we believe that these studies still provide valuable insights into how people update their beliefs after new information, which is why we do refer to them here. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 19 schizophrenia more readily accept ambiguous information and draw conclusions from it. In a well-designed series of experiments, Moritz and colleagues have provided evidence for this hypothesis (Moritz, Woodward, Jelinek, & Klinge, 2008; Moritz & Woodward, 2004;

Moritz, Woodward, & Lambert, 2007).

Belief updating in autism spectrum disorder. Autism Spectrum Disorder (ASD) is an umbrella term for a wide spectrum of symptoms, characterized mainly by social and communication difficulties and repetitive and restrictive behaviors, manifesting in various degrees of severity. One additional, often overlooked, characteristic of ASD is a more rational, consistent and unbiased information processing and reasoning (G. D. Farmer,

Baron-Cohen, & Skylark, 2017; Gosling & Moutier, 2018; South et al., 2014). For example,

ASD participants are less susceptible to negative vs. positive framing of choices, i.e. the framing effect (De Martino, Harrison, Knafo, Bird, & Dolan, 2008; Shah, Catmur, & Bird,

2016). This raises interesting questions regarding their process of believe updating.

Specifically, one could assume that such enhanced rationalism is reflected by an overly accurate (and less biased) update of beliefs. To the best of our knowledge, only one belief- updating task has been tested in ASD so far, and this was the above-referenced optimistic bias task (Kuzmanovic, Rigoux, & Vogeley, 2019). Confirming the initial hypothesis, people with ASD were found in this study to display a reduced optimistic bias, as reflected in more equal update of positive and negative news than the control group, which showed a significant tendency to integrate good – and disregard bad – news.

In a broader context, such a tendency to integrate new information equally, regardless of its valence, falls nicely within the predictive processing account of ASD. In the last few years, researchers harnessed the PPF to provide a unifying theory of the otherwise-unrelated

ASD symptoms (Lawson, Rees, & Friston, 2014; Pellicano & Burr, 2012; Sinha et al., 2014; BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 20

Van de Cruys et al., 2014). Beyond ASD’s two core symptoms (social-communicational difficulties and restrictive, repetitive behaviors), which have seemingly no shared underlying mechanism, ASD often includes additional symptoms, such as sensory hyper-sensitivities, impaired sensory-motor coordination, insistence on sameness and more (Leekam, Nieto,

Libby, Wing, & Gould, 2007; Mostofsky et al., 2009; Rozenkrantz et al., 2015), all seemingly disparate from one another. Based on the PPF, ASD entails an imbalance in the precision afford to incoming information relative to prior predictions, such that new information that does not match the prior is constantly favored. Whether this is due to

“weak” priors (i.e. priors that are afforded too little precision) or enhanced precision of incoming information, is subject to debate. Anyway, this framework nicely accounts for various ASD symptoms: incoming sensory information is not attenuated by prior predictions, thus leading to sensory overflow (i.e., a failure to attenuate sensory precision), weak priors regarding motor action and outcome (internal action models) may harm the ability to modulate a motor response following sensory input, for example following with one’s eyes after a moving object, leading to impaired sensory-motor coordination.

Furthermore, impaired social predictions may account for one of the social hallmarks of

ASD, that is, impaired theory of mind, which can be explained by unpredictability of others’ responses and thoughts. Finally, lack of predictability may lead to a constant experience of surprise and uncertainty. Repetitive behaviors and need of structured routines (i.e. need of sameness) are thought to be coping mechanisms of such uncertainty (Lawson et al., 2014;

Pellicano & Burr, 2012; Sinha et al., 2014; Van de Cruys et al., 2014). Taken together, current views suggest that individuals with ASD update their beliefs in a rational, information-driven manner, bypassing cognitive and psychological biases, which leads to more consistent – and often better – decision making. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 21

Ambivalent decision making in borderline personality disorder. The borderline personality disorder (BPD) is characterized by affective instability, impulsivity, and difficulties in social relationships. One core feature of BPD, which can be rephrased in terms of hasty changes in beliefs, is that persons with BPD rapidly change the way they think about important others (DSM 5, APA 2013). This means, for example, that although on one day the partner is considered the most lovable person in the world, the next day he can be seen as a person who does her no good and brings chaos into her life. Accordingly, persons with BPD have difficulty in social relationships; in particular, they are often torn between engaging in a very close relationship and breaking up. As a result, relationships of persons with BPD are often characterized by an “on-off” pattern, meaning that breakups are often followed by reconciliation and being together again. These hasty changes in beliefs (e.g., from, “My boyfriend is the best that ever happened to me” to, “My boyfriend is a dishonest, bad person”) are often caused by information that is regarded as disconfirming “evidence”

(e.g., seeing boyfriend talking to another woman), even though other people may consider this information quite harmless. In line with these notions, recent studies have shown that

BPD is also related to behavioral inconsistencies in social interactions and inconsistency in social decision-making (Preuss et al., 2016). Furthermore, there is partial evidence of more general decision-making deficits in persons with BPD (for a review, see Paret, Jennen-

Steinmetz, and Schmahl (2017)).

Factors contributing to anomalies in belief updating

After the presentation of several examples of both lack of and hasty belief updating in clinical populations, we next discuss factors that might underlie these anomalies in updating beliefs after receiving new information. In doing so, we will also highlight whether there is evidence suggesting that these mechanisms are specific to certain conditions or whether they may also apply to healthy people. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 22

Aberrant precision given to prior beliefs

With active inference, it is assumed that the degree of precision that is afforded to prior beliefs significantly influences the extent to which new information (i.e. prediction errors) is used to update beliefs. Following this view, research from computational psychiatry suggests that too much precision given to prior beliefs, accompanied by an attenuation of disconfirming information, might be a mechanism underlying the persistence of dysfunctional beliefs despite disconfirmatory evidence in various mental disorders (Adams,

Huys, & Roiser, 2016; Friston, Stephan, Montague, & Dolan, 2014; Paulus, Feinstein, &

Khalsa, 2019). Evidence for this strong-prior hypothesis has first been provided for psychosis: E.g., Powers, Mathys, and Corlett (2017) demonstrated in an intricate approach that people who hear voices were more susceptible to conditioning-induced hallucinations; using neurocomputational modeling, they showed that this effect was based on strong perceptual priors. Recently, this finding has been replicated by Benrimoh, Parr, Vincent,

Adams, and Friston (2018) for auditory hallucinations. Based on these findings, the assumption of strong priors that override disconfirming sensory information has been discussed as a core feature of psychosis (Corlett et al., 2019; Sterzer et al., 2018). Similar to this aberrant precision account of psychosis, it has been proposed that too much precision afforded to negative prior beliefs accounts for the lack of updating these beliefs despite positive experiences (i.e. prediction errors) in major depression (Barrett et al., 2016; J. E.

Clark et al., 2018; Kube, Schwarting, et al., in press).

Whereas too much precision afforded to priors is thought to contribute to persistent beliefs, we suggest that too little precision given to priors might contribute to the above described phenomena of hasty changes in beliefs. This is consistent with current thinking in computational neuroscience, assuming that a low degree of precision in priors (i.e. weak priors) leads to a higher relative impact of sensory information (i.e. prediction errors) on the BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 23 posterior predictions (Barrett & Simmons, 2015; Kanai et al., 2015; Paulus et al., 2019).

Some empirical support for this idea can be found in research on autism, as described above.

Additional support for the weak-prior hypothesis has been provided by research using dynamic causal modelling, e.g. in schizophrenia (Adams, Bauer, Pinotsis, & Friston, 2016;

Bastos-Leite et al., 2014; Fogelson, Litvak, Peled, Fernandez-del-Olmo, & Friston, 2014).

In more general psychological terms, the assumption of weak prior beliefs that are highly susceptible to new information might be regarded as a form of fragility of beliefs. That is, the less firmly people hold their beliefs, the more likely they use new information to update them. This corresponds well to some of the difficulties of people with BPD as discussed in the hasty changes section. For instance, under the assumption that their beliefs about their relationships are fraught with uncertainty, it is understandable that people with BPD are highly sensitive to any new information that they feel is relevant to the stability of their relationship, both negative and positive (such as seeing the boyfriend with another woman vs. receiving a compliment from the boyfriend).

Importantly, although aberrant precision has so far been linked to anomalies in belief updating mostly in clinical populations, it is well conceivable that this may also apply to healthy people under some circumstances. For example, applying the argument of aberrant precision to political attitudes, we suggest that too much precision given to prior beliefs might be an important mechanism underlying the maintenance of particular political beliefs despite disconfirmatory political news. In other words, if we hold our political attitudes with a high degree of certainty, we tend to be skeptical about any information that contradicts our beliefs; hence, such information is likely to be ignored or given reduced weight. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 24

Interpretation biases

Another mechanism that may underlie the persistence of dysfunctional beliefs despite disconfirmatory evidence is biased interpretation of discrepant information. In particular, researchers have found that people with mental disorders have the tendency to interpret novel information through the lenses of their prior beliefs, thereby increasing the likelihood of confirming them. For instance, people with major depression tend to interpret ambiguous situations negatively, especially if they contain self-referential information (Everaert,

Podina, & Koster, 2017). Furthermore, once negative interpretations have been established, people with depressive symptoms fail to use novel positive information to revise their interpretations, although novel information would clearly favor another, more positive interpretation (Everaert et al., 2018; Liknaitzky et al., 2017). Relatedly, research on affective forecasting has found that people with depression are biased in their prediction of their future affective states, meaning that they overestimate the anticipated presence of negative affect (Hoerger, Quirk, Chapman, & Duberstein, 2012; Marroquín & Nolen-Hoeksema,

2015; Radomsky, Wong, Dussault, Gilchrist, & Tesolin, 2019; Wenze, Gunthert, & German,

2012; Zetsche, Bürkner, & Renneberg, 2019). Similar interpretation biases have been found in patients with social anxiety: people with social anxiety favor negative interpretations of ambiguous social situations, and tend to interpret unambiguous but mildly negative social events in a catastrophic fashion (Amin, Foa, & Coles, 1998; D. M. Clark & McManus, 2002;

Stopa & Clark, 2000).

As with aberrant precision, interpretation biases may not be specific to clinical populations only. In fact, there is a large literature in social and cognitive psychology on biased assimilation, which is conceptually very similar to the clinical literature on interpretation biases. Specifically, according to the concept of biased assimilation, people’s interpretations of new information are assimilated into pre-existing beliefs, thus sustaining BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 25 and further “confirming” their beliefs (Lord & Taylor, 2009). For further discussion of persistent political beliefs in social and political sciences, readers may also be referred to the literature on attitude polarization (Boysen & Vogel, 2008; McHoskey, 1995; Munro & Ditto,

1997), resistance to persuasion (Tormala & Petty, 2004; Zuwerink & Devine, 1996); partisan bias (Bartels, 2002; Bullock, Gerber, Hill, & Huber, 2015); motivated reasoning (Redlawsk,

2002; Taber et al., 2009); and metacognitive sensitivity (Rollwage, Dolan, & Fleming,

2018).

Reappraisal

An additional factor that has been discussed as a factor contributing to the maintenance of dysfunctional beliefs is cognitive reappraisal of disconfirmatory evidence (Rief &

Glombiewski, 2016; Rief et al., 2015; Rief & Joormann, 2019). In particular, it has been suggested that people with mental disorders are prone to devaluing positive information that disconfirms disorder-specific negative beliefs by post-hoc questioning its credibility or considering it to be an exception rather than the rule. This negative reappraisal of disconfirming information resulting in a lack of belief updating has been referred to as

“cognitive immunization” against disconfirming information. Experimental research has recently confirmed this hypothesis by indicating that modulating the appraisal of unexpectedly positive performance feedback impacted the update of prior negative performance-related expectations (Kube, Glombiewski, et al., 2019; Kube, Rief, et al.,

2019). In these studies, people with depression worked on a performance test, where it is difficult for participants to evaluate whether they solved the tasks correctly. After the initial establishment of negative expectations, all participants received unexpectedly positive feedback for their performance. In people with depression, it was found that promoting the engagement in a post-hoc devaluation of positive performance feedback (by informing participants that the test they were working on would not have proven to be valid and BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 26 reliable) led to reduced expectation update; in contrast, the inhibition of cognitive immunization strategies (i.e. increasing the value of positive performance feedback) facilitated the update of negative expectations in line with positive feedback (Kube,

Glombiewski, et al., 2019; Kube, Rief, et al., 2019).

Interestingly, another study indicated that the promotion of cognitive immunization strategies in healthy people did not affect their adjustment of initial beliefs; that is, healthy people updated their expectations regardless of a reappraisal manipulation in line with positive performance feedback (Kube & Glombiewski, under review). These findings suggest that negative reappraisal of novel positive information is core to depression, but not typical of healthy people. However, it is well conceivable that healthy people conversely use cognitive immunization strategies to maintain their optimistic beliefs when confronted with bad news (Kube, Schwarting, et al., in press; Sharot & Garrett, 2016).

Synthesis of previous research

Relating belief updating anomalies to different stages of information processing

In the previous section, we compiled evidence of some factors that may contribute to certain anomalies in belief updating. Next, we aim to integrate these factors into the model of belief updating as a process of different stages of information processing. In doing so, we are facing an asymmetry in the depth with which the mechanisms of persistent beliefs vs. hasty changes have been investigated so far: while we were able to invoke a considerable amount of evidence of particular mechanisms underlying the lack of belief updating, little research to date has dealt with factors contributing to hasty changes in beliefs; accordingly, the latter is much less understood. Therefore, we consider these two phenomena separately.

With respect to persisting beliefs despite counterevidence, we identified three potential mechanisms that can be linked to different stages of information processing. First, the idea of BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 27 hyper-precise prior beliefs, resulting in an increased weight given to them in relation to new information, can be linked to the stage of anticipation of new information. In other words, holding a belief with certainty implies that people anticipate to receive confirmatory rather than disconfirmatory information, or even actively search for information that fits into their beliefs, to put in active inference terms. Second, at the level of encoding/interpretation, research into interpretation biases and biased assimilation indicates that beliefs can be resistant to updating if people have a strong bias to re-interpret new information as a confirmation of their prior beliefs. Third, in terms of further processing and appraising new information, people may be inclined to uphold their beliefs if the validity of new information is questioned post hoc, as research on cognitive reappraisal suggests, leading to a disregard of disconfirmatory information so that beliefs are not altered. Figure 3 illustrates how factors underlying the lack of belief updating relate to different stages of information processing

(i.e., anticipation, encoding, appraisal). To facilitate the relation of the psychological terms with their Bayesian homologues, we supplemented the terms “priors”, “weighing evidence”, and “posteriors” in Italics.

Insert Figure 3 here.

From the discussion of hasty changes in beliefs, it emerged that little is known about particular mechanisms underlying this phenomenon. In terms of the different stages of information processing, past research only allows one cautious suggestion with reference to the PPF: Drawing on this framework, we assume that too little precision afforded to prior beliefs leads to the posterior predictions being influenced by new information, whichever it may be, rather than by priors. However, given that little to date is known about mechanisms of hasty changes of beliefs, we refrain from including this into a mechanistic or conceptual model. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 28

An integrative model

As illustrated in Figure 4, we suggest that the degree of precision afforded to prior beliefs construes a lens through which new information is perceived, interpreted, and appraised. In case of strong priors (i.e. prior predictions that are afforded high precision), belief updating is biased towards resistance to disconfirmatory information, as new information may be considered less informative or valid. By contrast, low precision afforded to prior predictions may bias perception toward new sensory information such that posterior beliefs are strongly influenced by new information, formalized by increased weight given to new information

(i.e. prediction error) and pronounced belief updating. Thus, the level of precision afforded to prior beliefs affects the perception of sensory information and its appraisal, resulting in belief updating being tied to the priors (in case of hyper-precise priors) or being influenced strongly by sensory input (in case of weak priors).

Insert Figure 4 here.

This integrative model is – to our knowledge – the first to connect the PPF with the belief updating framework and to synthesize various, formerly unrelated lines of research into one coherent model. Specifically, it bridges neuroscientific research into the role of precision and psychological investigations of several biases in belief updating. Furthermore, this model provides a scaffold to further investigate differences in belief updating between healthy people and people with certain mental health conditions. In particular, we propose that our model is suitable to explain the above-referenced biases of belief updating in healthy people by assuming that, for instance, relatively high precision in optimistic future beliefs results in biased perception and appraisal of new information, hence leading to reduced belief updating in response to bad news. On the other hand, the model can explain the formation of pathologies in belief updating in that it suggests that abnormally high (or low) precision BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 29 given to prior beliefs can result in the persistent lack of belief updating (or hasty updating of beliefs, respectively). Tying in with these ideas, we will next discuss the implications of this account for the understanding of mental health and disorders in more detail.

Implications

Implications for the understanding of mental health vs. disorders

Integrating the above-discussed phenomena, we suggest that belief updating in healthy people it is quite volatile, meaning that it is biased towards the integration of information that fits into people’s pre-existing assumptions in various domains. In particular, according to the literatures on the optimism bias and self-concept stability, we propose that belief updating in healthy people is driven by the valence of new information as well as by the desire for a stable, positive self-perception. In addition, the literature on attitude change has revealed that belief updating in healthy people is biased toward the integration of information that is consistent with core beliefs to make sense of the world, such as core political attitudes. In other words, if (healthy) people’s beliefs reflect an optimistic view of their personal future, their view of themselves, or basic assumptions about the world, beliefs are likely to be resistant to disconfirmatory information and updating. By contrast, in other domains belief updating may be more sensitive to new information, especially if the adjustment of a particular belief is considered socially desirable, as suggested by research on social conformity (Asch, 1956; Bond & Smith, 1996; Larsen, 1974). This means that people are inclined to update their beliefs in line with new information if they consider it socially desirable. Collectively, research suggests that mental health is associated with the preferential integration of positive, self-concept stabilizing information into one’s beliefs, while disregarding information that is inconsistent with an optimistic view of the future, the self-concept, or core beliefs to make sense of the world. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 30

Some of the biases in updating beliefs that are typical of healthy people (i.e. the optimistic bias) have been shown to be absent in people with clinical disorders, particularly depression and autism (Garrett et al., 2014; Korn et al., 2014; Kuzmanovic et al., 2019).

More generally, available evidence suggests that as much as belief updating in healthy people is biased towards the confirmation of core beliefs of them (such as optimistic beliefs about the future and positive beliefs about the self), belief updating in mental disorders is biased towards the integration of information that is consistent with core beliefs of the respective disorder. That is, a belief seems to be particularly immune to updating if it is intertwined with core beliefs of the disorder, such as the view of oneself as being incapable in depression or the overestimation of threat in anxiety disorders. In other words, just as the nature of beliefs differs between people with certain mental disorders and healthy people, so does the nature of the information that is preferentially integrated (vs. disregarded). In both healthy people and people with clinical disorders, the (relative) immunity of core beliefs to disconfirmatory evidence might be accounted for by increased precision afforded to prior beliefs (as illustrated in Figure 4), although the content of the respective beliefs may differ considerably.

In other domains, healthy people and people with mental disorder may not only differ in terms of the contents of their beliefs but also mechanistically in the way new information is integrated. In particular, in cases where prior beliefs are afforded overly low precision, they become highly susceptible to updating, as indicated e.g. for increased (in fact, more rational) belief updating in response to bad news in people with autism. In general, the phenomenon of hasty changes in beliefs as a consequence of weak priors as discussed for various clinical populations has, to our knowledge, not yet been reported for healthy people. Conversely, while in some cases increased precision of prior beliefs may also apply to healthy people, in some clinical conditions prior beliefs are so overly precise that they cause severe perceptual BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 31 distortions, such as hallucinations in psychosis. Specifically, whereas perception in people who are in good mental health is normally influenced by both prior predictions and sensory evidence, it can be almost exclusively dominated by priors in psychotic hallucinations, resulting in a complete neglect of disconfirming sensory information. Similar accounts have been proposed for the experience of flashbacks in people with post-traumatic stress disorder

(Linson & Friston, 2019; Wilkinson et al., 2017) and medically unexplained physical symptoms (Edwards et al., 2012; Henningsen et al., 2018; Kube, Rozenkrantz, et al., in press; Van den Bergh et al., 2017).

Collectively, research into belief updating as reviewed in this article provides a new understanding of cognitive processes in mental health vs. illness in so far as it suggests that healthy people and people with mental disorders differ not only in the nature of their beliefs, but also in the way they integrate new information into their beliefs, depending on the particular disorder. The relative precision of prior beliefs and sensory information is assumed to play a key role in this respect. Importantly, these considerations – and the associated empirical evidence – do not allow any conclusions as to whether belief-updating anomalies are a vulnerability factor of mental disorders or merely a consequence thereof (in terms of reflecting a clinical problem as a result from the disorder). To address this question, researchers would have to carry out longitudinal studies that allow crossed-lagged panel analyses.

Interventions to modify belief updating-anomalies. Besides the contribution to the understanding of the psychopathology of some mental disorders, the above discussed clinical distortions in belief updating also have implications for psychological treatment.

Specifically, since we highlighted in the introduction that dysfunctional beliefs significantly influence the severity and prognosis of various mental disorders, their resistance to updating BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 32 and learning from new experience is particularly important from a clinical point of view.

Accordingly, clinicians may aim to modify dysfunctional patterns of belief updating in their patients through cognitive-behavioral interventions. For example, since cognitive immunization against disconfirmatory evidence is regarded as an important obstacle when attempting to modify dysfunctional beliefs, future clinical research may develop psychological interventions aimed at inhibiting the engagement in cognitive immunization strategies. A first proof-of-concept study in this context has shown that it was possible to prevent people with depression from devaluing expectation-disconfirming positive information through cognitive immunization-inhibiting strategies, thus facilitating the revision of negative expectations (Kube, Glombiewski, et al., 2019). In addition, it has been shown that an optimistic view of the future can be trained in people with depression through optimism-enhancing interventions (Miranda et al., 2017). We encourage researchers to continue this line of research in order to help people with mental health problems use novel information effectively to alter their dysfunctional beliefs.

To the best of our knowledge, there has not yet been any research aimed at modifying the opposite pattern, i.e. hasty changes in beliefs. Given that too little precision afforded to prior beliefs is considered a main candidate of pathology in this respect, it may be worthwhile to develop and test interventions aimed at increasing the confidence of prior beliefs and/or decreasing the precision given to new information.

Societal implications

In our view, the evidence summarized above on the characteristics of belief updating in healthy people also has implications for social and policy issues. For example, whereas the repeatedly referenced optimism bias may be adaptive in terms of mental health, it gets us into trouble at the point where we do not take information that threatens our livelihood BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 33 seriously enough, and fail to take necessary action. Sadly, this can be observed in the context of climate change: Although scientific evidence is accumulating to document the devastating consequences of climate change, many people still deny the presence of man-made climate change and their responsibility to counteract it. Some evidence suggest that this may be, in addition to various other factors, due to the optimistic bias when confronted with bad news.

Specifically, one study using eye tracking found that people with high trait optimism spent significantly less time attending to any information related to climate change, with particularly low fixation times if the arguments about climate change conveyed “bad news”

(Beattie, Marselle, McGuire, & Litchfield, 2017). Additionally, the authors found that people with high dispositional optimism more likely recalled the arguments in terms of a debate between two opposing positions (as opposed to framing it as clear evidence) and were more confident that the consequences of climate change would not affect them personally (Beattie et al., 2017). In line with this, a study applying the belief updating framework to information about climate change demonstrated that people who do not believe in the concept of man- made climate change updated their beliefs more towards good news (suggesting that the global rise of the average temperature may not be as dramatic as predicted) than towards bad news (suggesting that the global rise of the average temperature is even more dramatic than predicted) (Sunstein, Bobadilla-Suarez, Lazzaro, & Sharot, 2017). Thus, it seems that people are fleeing into tragic optimism when confronted with bad news about climate change, hoping that it may not become as horrific as science prognosticates, or even denying man- made climate change fundamentally. Given that this – individually understandable – bias in processing climate change-related information contributes collective inaction, it is a serious problem from a societal point of view.

To return to the examples of current divisive and irreconcilable political controversies as discussed in the beginning of this article, another societal implication of biased belief BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 34 updating might be derived. In particular, the above discussed phenomenon of truth decay must be considered alarming from a political and societal point of view. It is a crucial aspect of democratic systems that people are properly informed before making a political decision

(e.g. in terms of voting for a particular party or person). If, however, there is the increasing tendency that people disregard information that does not fit into their view, this is dangerous for the state of democracy. It is inherent to political decisions that their correctness (in terms of, e.g., being more beneficial than harmful) can be confirmed or falsified by new information. We believe that in a “healthy” democracy, new information should be considered thoroughly to examine past decisions, and if new information questions them, they ought to be revised. Thus, from a political and societal perspective, beliefs should be updated if new information is available that disproves previous assumptions. Accordingly, from this perspective, we view the current developments regarding "truth decay" and

“alternative facts” with great concern. Since “filter bubbles”, reductionism, vulnerability to

“fake news”, and other features of social networks significantly contribute to this development, we believe that societies should discuss this development and the role of social networks in political decision making more critically, particularly since more and more people base their political attitudes on information received in social networks. Future research on belief updating might contribute to this discussion by investigating how information can be provided such that people use it to examine the validity of their (political) beliefs critically.

Critical assessment of past research and future directions

To our knowledge, this is the first review of belief updating that attempted to connect different lines of research and compared characteristics of belief updating in healthy and clinical samples. In our view, the field of belief updating is a fascinating area of research as it provides valuable insights into how people construct and update their subjective reality, BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 35 including their views of themselves, other people, and the world in general. Great merits of past research can be seen in thorough experimental procedures derived from different theoretical backgrounds, and important implications for both clinical work and societal issues. Despite our enthusiasm, we also identified a number of limitations of past research in this relatively nascent field, which we will discuss next in concert with some suggestions for future research.

The continuity of disconfirmatory information

As reviewed in this article, new information can be distinguished by whether it confirms or disconfirms previous assumptions. For the sake of simplicity, many researchers tended to regard confirmatory vs. disconfirmatory information as binary concepts. In fact, however, information can vary greatly in the extent to which it contradicts previous beliefs. Therefore, we encourage researchers to develop experimental paradigms that allow to take the continuity of disconfirmatory information into account. An experimental paradigm that does consider this aspect already is the one from Tali Sharot’s lab, which was used to study the optimism bias in healthy people as discussed above. Interestingly, a study on pain perception has recently found that the relationship between the magnitude of the prediction error

(indicating how large the difference between predicted and actual outcome is) and the extent to which beliefs are updated is not linear, but includes a “tipping point”, meaning that at some point the prediction error is so large that it is considered less reliable and entails thus reduced update (Hird, Charalambous, El-Deredy, Jones, & Talmi, 2019). Continuing this line of research, researchers may examine the relationship between the magnitude of the prediction error and prediction update in domains beyond pain perception. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 36

Belief updating as a dynamic phenomenon

Another limitation of previous research refers to the fact that belief updating is not a stable trait, but varies dynamically based on various factors. For example, Sharot and colleagues, using their optimistic bias paradigm, found that the extent to which belief updating is biased towards the integration of desirable (vs. undesirable) information changes throughout the lifespan (Sharot & Garrett, 2016). Interestingly, whereas the integration of good news remains stable over time, the discard of bad news appears to be associated with younger age: adolescents, to a larger degree than young adults, failed to integrate undesirable information, as revealed in a study by Moutsiana et al. (2013). This may contribute to the understanding of adolescence risk-taking and have implications on educational risk- prevention programs at schools. When examining other age ranges, a U-shape curve is formed, such that the lowest optimistic bias in belief updating is seen in midlife, while younger (adolescents and young adults) and elderly individuals display higher resilience to the integration of bad news. Of note, it remains an open question whether this age effect applies to belief updating in general or only to optimistic updating in particular.

Beyond age, belief updating fluctuates depending on factors such as hormones and neurotransmitters, particularly dopamine. Administrating exogenous dopamine to healthy participants increased the discard of bad news but had no significant effect on the integration of good news (Sharot, Guitart-Masip, et al., 2012), much like the effect seen in adolescents.

Dopamine is involved in many neural processes, including the reward system, where it mediates the process of learning from reward and punishment (Pessiglione, Seymour,

Flandin, Dolan, & Frith, 2006). Relatedly, research on people with Parkinson’s disease demonstrated that dopamine diminished learning from unwanted outcomes when the patients were on dopamine-containing medication (Frank, Seeberger, & O'reilly, 2004), contributing to its effect on belief updating. Providing a different neurotransmitter, oxytocin BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 37

(administered intra-nasally), led not only to the reduced integration of bad news, but also to the increased integration of good news. This was explained by oxytocin’s role in promoting adaptation in social situations by modifying cognitive and emotional responses (Ma et al.,

2016). In view of these various sources of influences on belief updating, we encourage researchers in the field to consider factors such as age, hormones, and neurotransmitters thoroughly in their studies, and test whether this is a by-factor of the optimistic bias, or a genuine belief-updating trait.

The question of adaptivity

In this article, we presented the examples of persistent beliefs and hasty changes in a fairly descriptive manner. In our view, an underexplored aspect in this context is the more normative question of when it is adaptive for people to update their beliefs. So far, researchers have mostly investigated simply whether or not people updated their beliefs, and some patterns in this regard were considered “adaptive” and hence linked to mental health.

This begs the question of what “adaptive” means, and we believe that the answer to this may depend on the (scholarly) perspective of researchers. For instance, from a predictive processing point of view, it is adaptive to update beliefs if there is enough reliable evidence that disconfirms people’s prior beliefs, relatively independent of the nature or content of a belief. By contrast, from a mental health perspective, belief updating would be regarded as adaptive when it helps people sustain their mental health. From this perspective, the content of the particular belief is very important in relation to the question of when it is adaptive to update it: there are some beliefs which can reasonably be considered dysfunctional, such as very negative beliefs about oneself (e.g., “I’m worthless”), whereas other types of beliefs can be assumed to be “healthy”, such as a mildly positive belief in one’s own abilities. By this view, it would be adaptive to update dysfunctional beliefs and to maintain adaptive beliefs, each even on the basis of rather limited evidence. Additional perspectives to consider for the BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 38 question of adaptivity might be an evolutionary psychology perspective and a societal perspective.

Conclusions

To our knowledge, this is the first review of belief updating in mental health and illness.

We pointed out that belief updating in healthy people is biased towards the integration of desirable information; self-concept congruent information; and information that is consistent with a person’s view of the world in general. When examining phenomena related to belief updating in clinical populations, we examined two opposing phenomena: persistent lack of updating disorder-specific beliefs and hasty changes of beliefs. As a main candidate of pathology, we identified aberrant precision given to prior beliefs, which may account for both problematic phenomena, depending on whether prior beliefs are afforded too much or too little precision. Building upon previous research, we provided a synthesis of several lines of research and hope that this review inspires future research into the exciting field of belief updating, as it is of high relevance to several psychological disciplines.

Acknowledgements

We are very grateful to Lukas Haffert who provided valuable suggestions of relevant literature from political sciences. Also, we thank Dorina Winter for her advises regarding borderline personality disorder. Finally, we are very grateful to Anne Suffel, who provided helpful comments on a previous version of the article.

Disclosure statements

The authors declare that they have no conflicts of interest. BELIEF UPDATING IN MENTAL HEALTH AND ILLNESS 39

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Figure legends

Figure 1. Schematic illustration of how new information can be integrated into existing beliefs.

Figure 2. Illustration of the basic experimental paradigm to investigate belief updating.

To examine particular patterns of belief updating, researchers use experimental manipulations that target different stages of information processing, such as anticipation, encoding, and further processing.

Figure 3. Factors contributing to the lack of belief updating at different stages of information processing. The Bayesian homologues of the belief updating terminology are added in Italics.

Figure 4. Schematic illustration of how high vs. low precision afforded to prior beliefs and high vs. low precision of sensory information influence belief updating.