Elsevier

Clinical Psychology Review

Volume 58, December 2017, Pages 33-48
Clinical Psychology Review

Review
A comprehensive meta-analysis of interpretation biases in depression

https://doi.org/10.1016/j.cpr.2017.09.005Get rights and content

Highlights

  • A meta-analysis on interpretation biases in depression was conducted

  • Results revealed a medium overall effect size (ES)

  • Clinical status or mental imagery instructions were not significant moderators

  • Self-reference of stimuli and measurement method were significant moderators

  • Theoretical and clinical implications with future directions are provided

Abstract

Interpretation biases have long been theorized to play a central role in depression. Yet, the strength of the empirical evidence for this bias remains a topic of debate. This meta-analysis aimed to estimate the overall effect size and to identify moderators relevant to theory and methodology. PsycINFO, Embase, Web of Science, Scopus, PubMed, and dissertation databases were searched. A random-effects meta-analysis was performed on 87 studies (N = 9443). Results revealed a medium overall effect size (g = 0.72, 95%-CI:[0.62;0.82]). Equivalent effect sizes were observed for patients diagnosed with clinical depression (g = 0.60, 95%-CI:[0.37;0.75]), patients remitted from depression (g = 0.59, 95%-CI:[0.33;0.86]), and undiagnosed individuals reporting elevated depressive symptoms (g = 0.66, 95%-CI:[0.47;0.84]). The effect size was larger for self-referential stimuli (g = 0.90, 95%-CI[0.78;1.01]), but was not modified by the presence (g = 0.74, 95%-CI[0.59;0.90]) or absence (g = 0.72, 95%-CI[0.58;0.85]) of mental imagery instructions. Similar effect sizes were observed for a negative interpretation bias (g = 0.58, 95%-CI:[0.40;0.75]) and lack of a positive interpretation bias (g = 0.60, 95%-CI:[0.36;0.85]). The effect size was only significant when interpretation bias was measured directly (g = 0.88, 95%-CI[0.77;0.99]), but not when measured indirectly (g = 0.04, 95%-CI[− 0.14;0.22]). It is concluded that depression is associated with interpretation biases, but caution is necessary because methodological factors shape conclusions. Implications and recommendations for future research are outlined.

Introduction

Depression is one of the most common psychiatric disorders causing a severe personal and societal burden. The global point prevalence rate of depression is estimated at 4.7% and the annual incidence at 3.0% (Ferrari et al., 2013), with about 350 million people currently suffering from this disorder worldwide (WHO, 2012). In addition to its high prevalence, depression is marked by severe symptomatic suffering, impaired social and professional functioning, substantial loss of quality of life, as well as increased risk of suicide (Kessler & Bromet, 2013). The economic costs to society are estimated at a total of $83.1 billion a year in medical costs, suicide-related mortality costs, and workplace costs (Greenberg et al., 2015). These facts clearly demonstrate that depression represents a major public health concern. Efforts to identify the factors involved in the onset and maintenance of depression are therefore particularly important to better understand and treat this devastating disorder.

The past several decades have witnessed burgeoning research on cognitive factors involved in depression (Gotlib & Joormann, 2010; Mathews & Macleod, 2005). This line of research represents one of the most important and direct translations of cognitive science to uncover emotionally distorted cognitive processes that put people at risk to develop and/or maintain depressive symptoms. In particular, one important line of research has investigated depression-linked abnormalities in how people interpret ambiguous emotional information.

Ambiguity is commonly encountered in everyday life. Imagine, for example, a person in the audience frowning his eyebrows while you are giving a speech or imagine that you were not invited to a party while several of your friends are going. These events are ambiguous because they can be understood in multiple ways (e.g., think about how these situations may turn out for you or how others may think of you). People need to interpret such ambiguous information to make sense of what is happening to them. Interpretation is a semantic process through which people construct mental representations that resolve the ambiguity (Blanchette & Richards, 2010; Hirsch et al., 2016). In depression, the process of interpretation is theorized to be marked by systematic emotional biases known as interpretation biases. Specifically, cognitive theorists have hypothesized that individuals with elevated depression levels have a tendency to create more negative and fewer positive meanings to explain ambiguous information (Clark et al., 1999; Ingram et al., 1999). Importantly, interpretation biases are typically regarded as proximal cognitive causes of depression and not as some mood-dependent correlates (Beck & Haigh, 2014; Ingram et al., 1999). Therefore, interpretation biases are a primary target in many psychological treatments for depression (Berking et al., 2013; Clark et al., 1999) and cognitive training methodologies (Cristea et al., 2015; Menne-Lothmann et al., 2014).

The theoretical prediction that depression is linked to interpretation biases has generated a wealth of empirical scrutiny in the past four decades. Cross-sectional and longitudinal studies have employed a wide variety of interpretation bias paradigms in diverse adult populations including patients diagnosed with major depression, undiagnosed individuals with elevated self-reported depressive symptoms, and patients remitted from depression (Blanchette & Richards, 2010; Foland-Ross & Gotlib, 2012; Gotlib & Joormann, 2010; Mathews & Macleod, 2005; Wisco, 2009). Although the theoretical predictions are straightforward, the empirical data has been far less clear-cut. In interpreting the available data, some narrative reviews have concluded that a considerable number of studies have yielded evidence for interpretation biases in depression and provide substantial support for predictions by cognitive models (Mathews & Macleod, 2005; Wisco, 2009). In contrast, other reviews have emphasized inconsistencies in research findings and pointed out that much of the evidence base is plagued by confounding factors even to the extent that there is limited direct support for the hypotheses (Blanchette & Richards, 2010; Foland-Ross & Gotlib, 2012; Gotlib & Joormann, 2010). In light of these different opinions and recent research that has produced mixed findings (e.g., Beard et al., 2017; Cowden Hindash and Rottenberg, 2017; Käse et al., 2013; Moser et al., 2012; Normansell & Wisco, 2016; Sears et al., 2011), the strength of support for interpretation biases in depression remains a topic of scientific debate.

To date, it has yet to be quantified which factors derived from theory and research methodology contribute to the variability in research findings. Interpretation biases in depression have only been subjected to meta-analysis indirectly, as part of a meta-analysis on implicit processes in depression (Phillips et al., 2010). While this meta-analysis offered some support for the relation between interpretation biases and depression, the study focused on a small subset of relevant work in this area and did not aim to explore methodological or theoretical factors that may moderate this relationship. The field clearly lacks a comprehensive meta-analysis that synthesizes prior research findings on interpretation biases in depression. In an attempt to address this gap in the literature, this study aims (a) to estimate the magnitude of the overall effect size and (b) to explore whether effect sizes vary as a function of several key theoretical or methodological factors.

To contextualize the variables of interest to this meta-analysis, this section briefly outlines several influential cognitive models of depression that have guided research on interpretation biases.

One of the most prevailing cognitive models is Beck's schema theory (Beck & Haigh, 2014; Clark et al., 1999). This theory asserts that individuals who are vulnerable to depression hold latent negative schemas or memory representations that contain negative self-referent beliefs on themes of loss and failure. Negative schemas develop through interactions between cognitive processes and adverse environmental factors. When activated by stressful life events, negative schemas guide how individuals process and interpret information. Vulnerable and depressed individuals are theorized to selectively attend to negative cues in their environment and recall related negative experiences. These negative biases in attention and memory skew the integration of information and produce a stream of more negative and fewer positive interpretations. Such interpretation biases reinforce negative schemas and memories, instigating a vicious cycle of negative thinking and worsening of depressive mood.

Drawing on Bower's network theory (Bower, 1981), Ingram (1984) proposed an information-processing analysis in which interpretations play a central role in self-perpetuating circles of negative thought in depression. The theory assumes that interpretations of life events activate memory networks. These memory networks consist of a number of interconnected nodes containing specific sets of cognitions. It is assumed that the activation of specific nodes spreads to all other connected nodes within a certain memory network and all its related networks. This causes individuals to elaborate upon cognitions that are congruent with their initial interpretations, which in turn activate other related negative cognitions. The process of recycling negative interpretations through various memory networks is thought to heighten vulnerability to depression. Once negative interpretations are more frequently made, they can more easily trigger extensive elaboration on related negative topics and themes. Ingram's theory asserts that the biased elaboration on negative interpretation is a vulnerability factor that endures beyond the depressive episode.

Recent models of depression increasingly emphasize the role of mental imagery during emotional information-processing. While ambiguity can be resolved verbally, it can also be resolved through mental imagery. Holmes and colleagues (Holmes et al., 2009) formulated a model of depression that focuses on the interaction between mental imagery and interpretation bias. The model proposes that interpretation biases and mental imagery have particularly toxic effects on depressed mood when these factors interact. It is hypothesized that making a negative interpretation and subsequently mentally simulating it amplify depressed mood more than verbal interpretations of the same situation. The mental images constructed may in turn be interpreted negatively and increase depressive mood. In addition to negative mental images, the lack of positive mental imagery in depression may further contribute to the depressed mood. This is because it prevents formation of more positive images that can motivate goal-directed behaviour. Mental imagery may thus be a critical factor that aggravates the impact of interpretation biases on depressive symptoms.

Theoretical models converge on the hypothesis that depression is marked by interpretation biases. These models also propose a number of factors that influence the magnitude of the emotional bias in interpretation. Below, we focus on the clinical status of depression, the centrality of self-relevant stimuli, and the use of mental imagery.1

Theories of depression predict that the magnitude of interpretation biases differs across depression groups (Beck & Haigh, 2014; Clark et al., 1999; Ingram, 1984). In researching this hypothesis, studies have recruited samples of patients with diagnosed major depression, undiagnosed individuals with self-reported elevated levels of depressive symptoms, and patients remitted from major depression (Blanchette & Richards, 2010; Foland-Ross & Gotlib, 2012; Gotlib & Joormann, 2010; Mathews & Macleod, 2005; Wisco, 2009). In the face of more severe and impairing symptoms, theorists have argued that clinical depression is qualitatively different from subclinical symptom levels of depression (Ingram & Siegle, 2009). Hence, patients with diagnosed major depression are expected to display more severe interpretation biases than undiagnosed individuals with elevated depressive symptoms. Moreover, theoretical models predict that biased elaboration of negative interpretations is a vulnerability factor that endures beyond the depressive episode (Ingram, 1984). Therefore, individuals remitted from depression are expected to display an interpretation bias. However, this bias in remitted depression may represent either a stable process that operates continuously or a latent process that is activated by stress or negative mood (Just et al., 2001). To date, it is unclear whether there are differences in the magnitude of interpretation bias between depression groups with a different clinical status.

Depression-linked interpretation biases are assumed to be triggered by self-relevant information related to depressed people's negative schemas (Clark et al., 1999) or depressive memory networks (Ingram, 1984). While a direct test of this theoretical prediction requires ideographically relevant experimental stimuli, studies have utilized standardized self-referent information as a proxy of idiographic relevance. Self-referential stimuli make reference to the respondent's own character and his/her experience. Though prior work has suggested that interpretation biases mainly occur for self-referent stimuli (Wisco, 2009), research is inconclusive as to whether interpretation biases are elicited only in response to this type of stimuli. For example, some investigators have observed an interpretation bias for specific other-referent stimuli (e.g., Wisco & Nolen-Hoeksema, 2010), whereas other researchers reported no such evidence (Cowden Hindash & Rottenberg, 2017). Given the centrality of self-relevance in cognitive theories, it is important to determine whether self-referential stimuli elicit an interpretation bias and if this bias can be elicited by stimuli that are not self-referent.

In line with theories stipulating that mental imagery exacerbates interpretation bias in depression (Holmes et al., 2009), studies on interpretation biases have sometimes explicitly instructed participants to use mental imagery to resolve the ambiguous stimuli. The task instructions typically prompt participants to imagine the described situation and its outcome or to elaborate on images evoked by the presented stimulus materials including themselves as a central actor, that is a first-person perspective. To date, it remains unclear to what extent mental imagery instructions affect the relation between interpretation biases and depression. Prior research has generally produced mixed findings. For example, even when identical paradigms were employed with instructions to use mental imagery, some studies did observe evidence for depression-linked interpretation biases (Lawson et al., 2002) whereas others did not (Käse et al., 2013). For both theoretical and methodological reasons, it is important to clarify whether instructing participants to create mental images to disambiguate the emotional information alters the strength of the relation between interpretation biases and depression.

In addition to moderators related to theory, investigators have repeatedly argued that the diversity in methods contributes to the variability in research findings (Blanchette & Richards, 2010; Foland-Ross & Gotlib, 2012). Indeed, studies on interpretation biases differ in how interpretation bias is measured and quantified in bias scores.2

Investigators have used direct and indirect measurement methods to study interpretation biases. Direct measurement involves methods that require respondents to report the emotional tone of their interpretation(s) in response to the stimuli presented. Examples of direct methods are interpretation tasks that ask respondents to report the interpretations they have constructed (Berna et al., 2011; Wisco & Nolen-Hoeksema, 2010) or to rank interpretations according to their plausibility (Butler & Mathews, 1983). Importantly, direct methods are not restricted to questionnaires. Direct measurement of the emotional content of interpretations is an important feature of many cognitive-experimental paradigms. For example, in the homograph task (Holmes et al., 2008) or scrambled sentences task (Wenzlaff & Bates, 1998), respondents describe the first mental image or thought that comes to mind. The advantage of direct methods is that they have higher face validity because they assess the content of emotional interpretations. This is information only respondents know. The major weakness is that direct methods are prone to response biases and demand characteristics (Blanchette & Richards, 2010). This means that depressed individuals may preferentially endorse more negative interpretations, but that other factors than interpretation biases drive these effects (Blanchette & Richards, 2010; Gotlib & Joormann, 2010).

To minimize problems inherent to direct measurement methods, researchers have developed indirect measures. Indirect measures do not require respondents to describe or evaluate the content of their emotional interpretations. Instead, these measures rely on differential behavioral or psychophysiological responses to emotional interpretations. Examples of indirect measures include reaction times (Mogg et al., 2006; Sears et al., 2011), startle responses (Käse et al., 2013; Lawson et al., 2002), and event-related potentials (Moser et al., 2012). Indirect methods are mostly employed by cognitive-experimental paradigms. For instance, in the word-sentence association paradigm (Cowden Hindash & Rottenberg, 2017), respondents judge the relatedness between pairs of words (e.g., ‘embarrassing’, ‘funny’) and ambiguous sentences (e.g., ‘people laugh after something you said’) that are presented for short durations (e.g., 500 ms). The task records reaction times (RT) to endorse or reject the matched word-sentence pairs to infer interpretation bias. Evidence for an interpretation bias is inferred when faster RTs are found for endorsed negative word-sentence pairs compared to positive word-sentence pairs. The advantage of indirect measures is that they reduce the influence of potential response strategies on interpretation bias. The disadvantage is that they have lower face validity.

To date, many studies in support of depression-linked interpretation biases have employed methods that directly measure interpretations, whereas the findings from studies with indirect measures have been mixed (Blanchette & Richards, 2010; Foland-Ross & Gotlib, 2012; Gotlib & Joormann, 2010). Thus, examining the role of the measurement method as a moderator is of critical importance to determine whether methodological factors shape the conclusions regarding the relation between interpretation bias and depression.

Studies have computed both absolute and relative bias scores to represent depression-linked biases in interpretation. Absolute bias scores are based on the recorded responses for each of the possible interpretations. For example, absolute bias scores may include the number of negative or positive interpretations reported (e.g., Halberstadt et al., 2008) or the reaction times on trials indexing positive, neutral, or negative interpretations (e.g., Bisson & Sears, 2007). Other studies have calculated relative bias scores that directly compare negative and positive interpretations by calculating proportions or ratios (e.g., Moser et al., 2012). The advantage of absolute over relative bias scores is that they allow disentangling valence-specific biases, namely decreased positive versus increased negative interpretations. Yet, relative bias scores may better quantify the depression-linked distortion in emotional information-processing (Shane & Peterson, 2007). Indeed, toxic effects of a negative bias could be dependent on how this bias operates relative to the presence of positive interpretations. Comparing negative with positive interpretations may reveal larger effect sizes than absolute indexes. Examining the moderating role of the interpretation bias score is important to identify the specific interpretation biases in depression as hypothesized by theoretical models (Clark et al., 1999; Holmes et al., 2009; Ingram, 1984) as well as to gain insight in to how best to quantify the severity of distortions in the interpretation of ambiguous emotional information.

The purpose of this meta-analysis is to provide the first comprehensive and objective summary of a research on interpretation biases related to depression. The first objective is to assess the overall effect size of the relation between interpretation biases and depressive symptoms. The second objective is to examine theoretical and methodological factors that may moderate this relationship. As moderators, this meta-analysis examines the clinical status of depression, the role of self-referent stimuli, the use of mental imagery instructions, the measurement method, and the interpretation bias score. An exhaustive examination of interpretation biases in depression is necessary to be able to draw empirically-informed conclusions about the strength of the relation between depression and interpretation bias. This study may accordingly inform theory, research, and treatment.

Section snippets

Literature search strategies

Complementary approaches were used to identify relevant articles. First, studies were collected through comprehensive searches of electronic databases PsycINFO, Embase, ISI Web of Science, Scopus, and PubMed through September 2016. To maximize coverage of the relevant studies, the following comprehensive search string was entered into the databases: (depress* OR dysphor*) AND (interpret* OR apprais* OR schema* OR process* OR cognitive OR affective) AND bias*. In addition, the databases were

Characteristics of the studies

Sample sizes ranged from 7 to 1173 totaling 9443 participants. The mean age ranged from 18 to 44.81 years and the proportion of female participants ranged from 55.1% to 100%. The number of studies with a dimensional design (k = 41) was comparable to the number of studies with a categorical design (k = 46). With regard to task properties, most studies relied on direct measurement methods (k = 67), computed absolute bias scores (k = 51), presented self-referent stimuli (k = 62), and did not provide mental

Discussion

Intense research during the past four decades has generated a wealth of data on the relation between interpretation biases and depression. Yet, empirical findings have been mixed and investigators have drawn diverging conclusions regarding the strength of the available evidence. This study aimed to provide the first comprehensive meta-analysis to assess the current state of research in this field of interest. In line with theoretical predictions (Clark et al., 1999; Holmes et al., 2009; Ingram,

General conclusion

The following conclusions are supported by this meta-analysis. First, there is evidence that depression is associated with interpretation biases, but caution is necessary because the evidence draws on studies using direct methods. Second, there is no evidence that the magnitude of interpretation differs across individuals with undiagnosed elevated depressive symptoms, clinical, or remitted depression. Third, interpretation biases are stronger for self-referent stimuli, but also occur for

Role of funding sources

This research was supported by a grant from the Belgian American Educational Foundation awarded to Jonas Everaert, and the Romanian National Authority for Scientific Research and Innovation (CNCS-UEFISCDI, PN II-RU-TE-2014-4-2481, 293/01/10/2015) awarded to Ioana R. Podina.

Contributors

JE, IRP, and EHWK designed the study and wrote the meta-analytic protocol. JE and IRP conducted literature searches, provided summaries of previous research, and coded the study characteristics under supervision of EHWK. IRP conducted the analyses in consultation with JE and EHWK. JE wrote the first draft of the manuscript and critical feedback was provided by IRP and EHWK. All authors have approved the final manuscript.

Conflict of interest

All authors declare that they have no conflicts of interest.

Acknowledgements

The authors thank Jan De Houwer and Alishia D. Williams for their thoughtful comments on earlier drafts of this manuscript. The authors thank Mirela Mohan for the language proofing and Nolan Sheridan for his assistance with the literature search.

Jonas Everaert is a postdoctoral research fellow at Yale University, USA.

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    Jonas Everaert is a postdoctoral research fellow at Yale University, USA.

    Ioana R. Podina is an assistant professor at the University of Bucharest, Romania.

    Ernst H.W. Koster is an associate professor at Ghent University, Belgium.

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