ReviewSelf-reported cognitive biases in depression: A meta-analysis
Introduction
Depression is considered one of the leading causes of disability in the world and is associated with great social and economic costs (Whiteford et al., 2013). More than 200 million people suffer from major depression (World Health Organization, 2017) and the relapse rate is around 85% (McIntyre & O'Donovan, 2004). Given the impact of this disorder, it is important to understand the variables that influence its development, maintenance and recurrence. Several theories have been proposed to explain the causes of this disorder (Gotlib & Hammen, 2014). Among them, cognitive theories have been very influential both in research and applied fields. These theories point out to dysfunctional thinking as a key causal factor related to the onset and maintenance of these emotional disorders (Barlow et al., 2011; Beck, 1967, Beck, 1976).
Beck's seminal cognitive model of depression (1967, 1976) provided some of the concepts and explanatory heuristics that have been incorporated in most of the current cognitive models of depression. According to this model (see Fig. 1), individuals may develop, early in life, latent cognitive schemas that get activated when facing environmental stressors that are pertinent to the contents of those schemas (e.g., themes of loss). Cognitive schemas are rather abstract representations of the world and determine the way in which information is processed and how events and stimuli are interpreted in a given context (Dalgleish & Power, 2000). When the negative contents of these cognitive schemas are triggered by internal or external events, psychological processes (i.e., memory, interpretation, and attention) operate following negative cognitive biases, like dichotomous thinking or arbitrary inferences (see Table 1). These processes then lead to biased mental products or thoughts about the self, the world and the future (i.e. the cognitive triad). It is important to note the difference between cognitive schemas and cognitive biases. Following Ingram and Kendall's (1986) cognitive taxonomy, cognitive schemas would be structural variables of the system (i.e., broad cognitive frames through which information is filtered, represented and organized), whereas cognitive biases would function as operational variables, or mechanisms, by which cognitive structures work. Although schemas would be the most distal causes of depression (Panzarella, Alloy, & Whitehouse, 2006), biased cognitive operations would also have a critical causal role as they are the action mechanisms used to support and validate the schemas. Overall, this cognitive machinery might be considered as an antecedent of the presence of negative automatic thoughts, negative beliefs (about oneself, the world and/or the future), and ultimately depressive symptoms, which would all be the products (i.e., the tangible outputs) with which clinicians typically work with their clients.
Beck's theoretical account has had a profound impact on both clinical and research fields. For instance, cognitive-behavior therapy (CBT) for depression follows the rationale of the cognitive model (Beck, Rush, Shaw, & Emery, 1979; Greenberg & Padesky, 2015). One of the main aims in CBT is to modify biased thinking (Beck, 1976) as a path to improve symptoms such as negative affect and anhedonia (Joorman & Gotlib, 2010). Also, extensive research on cognition and depression has analysed the relationships between these constructs and depressive symptoms in a variety of samples regarding culture, gender, age and diagnostic severity (e.g. Hu et al., 2015; Cristea et al., 2015; Vîslă, Flückiger, Grosse Holtforth, & David, 2016). Moreover, Beck's proposal has inspired the creation of widely used instruments to measure both symptoms (e.g., the Beck Depression Inventory-II, BDI-II, Beck, Steer, & Brown, 1996) and cognitive constructs like the Cognitive Errors Questionnaire (CEQ, Lefebvre, 1981) or the Cognitive Bias Questionnaire (CBQ, Krantz & Hammen, 1979). In sum, Beck's model has been extraordinarily influential both in research and applied fields in clinical psychology.
Beck's original theory has been further developed during recent decades. Empirical research has supported and refined the model by clarifying the nature of stressors contributing to form early negative schemas (Hammen, 2005), distinguishing different subtypes of schemas (Clark & Beck, 1991), or lately adding new mechanistic processes, like rumination (Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008) or overgeneral autobiographical memory (Williams et al., 2007), that have been incorporated in the depression literature for some decades after Beck's initial formulation. Likewise, there have been efforts to inquire into the neurological basis of Beck's theoretical account (Beck, 2008). For instance, researchers have found that cognitive biases seem to be characterized by a maladaptive bottom-up system at the subcortical level, which is reinforced by an attenuated cortical cognitive control unable to regulate them (Disner, Beevers, Haigh, & Beck, 2011).
Despite the relevance of Beck's cognitive model of depression, the specific cognitive biases proposed in it (see Table 1) have not been subjected to a systematic review, except for causal attribution bias (Hu, Zhang, & Yang, 2015). Although the model is still inspiring research on cognitive processes (Gotlib & Joormann, 2010) and has become one of the best validated and most frequently used therapeutic interventions (Cuijpers, Noma, Karyotaki, Cipriani, & Furukawa, 2019), the validity of the cognitive biases explicitly formulated in the model is relatively unknown.
Whereas some meta-analyses have approached the study of cognitive constructs such as irrational beliefs, dysfunctional thinking, or automatic thoughts (Cristea et al., 2015; Vîslă et al., 2016), the magnitude of most cognitive biases in depressed individuals has not been synthesised in the literature. Also, it remains unclear whether these processes are exclusive for depressed individuals, which makes it important to study their relationship with depressive symptoms itself (dimensional studies) but also to compare different types of populations (categorical studies). Thus, the aim of this study was to conduct a meta-analysis of those cognitive biases of depression, as specified in Beck's cognitive model, that have not been meta-analysed in previous studies (i.e. those presented in Table 1 except for Personalization/Internal causal attributions). Random-effect models were used (since sampling variability was expected, Riley, Higgins, & Deeks, 2011) to meta-analyse each cognitive bias, using the standardized mean difference with Hedge's correction (g) as the effect size (ES). The meta-analysis was pre-registered in PROSPERO (CRD42018115365). The first hypothesis was that there would be significantly larger levels of cognitive biases in depression groups than in other comparison groups, such as healthy participants, or individuals with other psychological symptoms, or subclinical levels of depression. Also, it was expected that several variables could moderate this effect. The year of the study and geographic location of the corresponding author were coded to study potential spatial and temporal effects. Based on previous findings, sample characteristics such as age (Reed, Chan, & Mikels, 2014), gender (Kessler & Bromet, 2013), or type of sample (Clark, Beck, & Alford, 1999) were investigated although the direction of these potential moderation effects was not anticipated. Finally, methodological variables such as sample size, type of measure, and the psychometric characteristics of the instruments were also included as potential moderators given previous meta-analytic results (Everaert, Podina, & Koster, 2017).
Section snippets
Eligibility and search criteria
A systematic search was conducted on the databases of PsycINFO and PubMed, until February 2020, combining terms related to the spectrum of depression (depress* OR dysphor* OR mood OR “affective disorder” OR “sad mood” OR sadness), comparison groups based on DSM-5 (American Psychology Association, 2013) categories and type of population (delirium OR dementia OR “neurodevelopmental disorder” OR schizophrenia OR “psychotic disorder” OR “delusional disorder” OR “bipolar disorder” OR “anxiety
Summary measures
Random-effects meta-analyses for all the included cognitive biases were conducted, in SPSS 20 and R 3.5.0 (metafor package Viechtbauer, 2010), using the standardized mean difference (d= X̅1-X̅2/Spooled) with Hedge's correction (g = c(m)*d)) as the effect size (ES). Positive values reflect a higher level of cognitive bias in the experimental group compared to the control group. Hedge's g values can be categorized as small (0.2–0.5), medium (0.5–0.8), or large (>0.8) (Cohen, 1988). All studies
Study selection
The process of selection and inclusion of studies is shown in Fig. 2. From a total of 4840 records (1320 duplicates), 3131 were excluded based on the screening of the title or abstract, while 461 were excluded after a full-text reading. The main reason for exclusion was the lack of a measure of bias (i.e., many studies were focused on constructs different from cognitive biases, such as cognitive schemas or automatic thoughts -see Fig. 1). Many studies were also excluded because they did not use
Overall effect sizes
Effect sizes for catastrophizing bias and interpretation bias (see Fig. 3 and Fig. 4) were large and moderate (g = 0.95, p < 0.001 and g = 0.78, p < 0.001, respectively). Heterogeneity was significant and high in all cases, with the I2 value around 90% (see Table 2, Table 3). Significant effect sizes were maintained after the removal of 6 outliers for the catastrophising bias analysis, and 13 for interpretation bias. These sensitivity analyses also showed a reduction in heterogeneity. Details
Discussion
The aim of this study was to quantify the evidence of self-reported cognitive biases in depressed individuals as compared to other groups of participants. Based on the relatively scarce amount of empirical studies found in our search (k = 63), analyses only included two categories of bias (catastrophizing and interpretation bias) and comparisons between groups of depressive participants and non-symptomatic groups.
Given that the search covered a large period of time (no limit-2020), it seems
Author Biography
Inés Nieto is a Ph.D. student at Complutense University. She is currently finishing her dissertation on cognitive modification bias in emotional disorders.
Elena Robles is a Ph.D. student at Complutense University. She is currently doing her dissertation on selective attention biases in chronic pain.
Carmelo Vazquez, pH.D. is a Professor of Psychopathology at Complutense University. He has done extensive experimental psychopathology research and has published review papers and meta-analyses on
Declaration of Competing Interest
None.
Acknowledgements
This work was partially supported by MINECO PSI2015-69253-R and Ministry of Science and Innovation PID2019-108711GB-I00 grants to CV and CT17/17-CT18/17 Complutense University predoctoral fellowhip to IN. We also thank Jamie O'Grady for his help in editing the paper and Jonas Everaert for his technical advice when conducting this meta-analysis.
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