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The interplay among attention, interpretation, and memory biases in depression:

Revisiting the combined cognitive biases hypothesis

Jonas Everaert and Ernst H.W. Koster

Ghent University

Introduction

Depression is a prevalent and recurrent mental disorder causing a severe personal and societal burden (Kessler & Bromet, 2013). Identifying the mechanisms involved in depression is an integral part of efforts to improve prevention and treatment strategies for this burdensome disorder. Cognitive theories posit that depression symptoms are caused in part by negative biases in the processing of emotional information (Clark, Beck, & Alford, 1999; Ingram, 1984; Williams et al., 1997). Consistent with this hypothesis, extensive research has linked depression to negative biases in cognitive processes such as attention, interpretation, and memory. Specifically, empirical studies have found that (sub)clinically depressed individuals may exhibit an attention bias toward negative self-relevant information (Armstrong & Olatunji, 2012; Peckham, McHugh, & Otto,

2010; Winer & Salem, 2016; but see also Rodebaugh et al., 2016), an interpretation bias favoring negative explanations for ambiguous situations (Everaert, Podina, & Koster, 2017), and a memory bias featuring improved recollection of negative self-referential information (Gaddy & Ingram,

2014; Matt, Vázquez, & Campbell, 1992). Importantly, research suggests that biases of attention, interpretation, and memory may influence symptoms of depression (Hallion & Ruscio, 2011;

Menne-Lothmann et al., 2014; Mogoaşe, David, & Koster, 2014; Vrijsen, Hertel, & Becker, 2016) and predict their longitudinal course (Johnson, Joormann, & Gotlib, 2007; Price et al., 2016; Rude,

Durham-Fowler, Baum, Rooney, & Maestas, 2010). Together, current research findings indicate that attention, interpretation, and memory biases are not merely a by-product of depressed mood but may confer risk to experiencing depression. 2

While attention, interpretation, and memory biases have been investigated extensively in isolation, the interplay among these presumed risk factors has received only modest consideration.

However, uncovering how these cognitive biases work together seems crucial to gain a comprehensive understanding of the cognitive mechanisms of maladaptation involved in depression. Indeed, researchers have repeatedly argued that it is unlikely that the heterogeneous symptoms of depression (e.g., sustained negative mood, anhedonia) would be caused or maintained by cognitive biases that operate in isolation (e.g., Hankin, 2012; Kraemer, Stice, Kazdin, Offord,

& Kupfer, 2001; Wittenborn, Rahmandad, Rick, & Hosseinichimeh, 2016). Instead, theorists have proposed that it is much more likely that depression involves multiple causal chains involving cognitive biases that reinforce each other through mutual influences. This notion has been referred to as the Combined Cognitive Biases Hypothesis (CCBH; Hirsch, Clark, & Mathews, 2006). The

CCBH, as originally formulated by Hirsch and colleagues, specifically states: “cognitive biases do not operate in isolation, but rather can influence each other and/or can interact so that the impact of each on another variable is influenced by the other. Via both these mechanisms we argue that combinations of biases have a greater impact on disorders than if individual cognitive processes acted in isolation” (p. 224; Hirsch et al., 2006).

Although the CCBH was formulated in the context of social anxiety disorder, the hypothesis can be applied to other forms of psychopathology. Elaborating on the CCBH in the context of depression in a prior review (Everaert, Koster, & Derakshan, 2012), we outlined three broad categories of open research questions that required empirical consideration to further our understanding of how cognitive biases operate in the etiology, maintenance, and relapse depression. Specifically, we distinguished association, causal, and predictive magnitude questions.

With association questions, we referred to research questions focusing on how biases of attention, interpretation, and memory are correlated across different stages of information-processing (e.g. 3 during encoding or retrieval of emotional information). Example association questions may concern whether negative attention bias during encoding is associated with improved memory for previously presented negative material or whether negative memory bias is related to attention biases toward matching emotional material. The second category of CCBH questions, the causal questions, focuses on the direction of the hypothesized influence of one cognitive bias on another bias. These questions concern unidirectional and bidirectional influences that may exist between two given cognitive biases. An example of a causal question is whether attention biases cause a congruent bias in interpretation and whether an interpretation bias in turn influences attention allocation toward stimuli that are congruent with the emotional interpretations. Finally, the third category of questions, the predictive magnitude questions, focuses on how multiple cognitive biases in concert influence the symptom course of depression over longer periods of time.

Extending association and causal questions, predictive magnitude questions focus on the utility of a single cognitive bias vs. multiple cognitive biases in combination in predicting prospective changes in depression. For example, predictive magnitude questions may address whether cognitive biases have additive effects on depressive symptoms that extend beyond the isolated effect of each bias (see below).

Recent years have witnessed an important upsurge of empirical studies addressing different aspects of the different CCBH questions in various forms of psychopathology. In light of the advances in research on the CCBH in depression, the purpose of this chapter is to review recent findings as well as both theoretical and methodological innovations since our review article

(Everaert et al., 2012). We think that an updated review of theory and research on the CCBH in depression is both timely and necessary given the increasingly complex picture that is arising from the empirical research examining interactions among cognitive biases in depression. 4

Below, we start by describing theoretical contributions that can inform upon the interplay among cognitive biases in depression. Then, we discuss the major methods that have been used to investigate the association, causal, and predictive magnitude questions that originate from the

CCBH. Next, we review findings from recent empirical work with respect to the different CCBH questions. Finally, we discuss limitations of current research on this topic and propose several ways in which this exciting area of research can be taken forward.

Major theories in the field

Influential theoretical models of depression such as Beck’s schema theory (Beck & Haigh,

2014; Clark et al., 1999), enhanced elaboration accounts (Ingram, 1984; Williams et al., 1997), and cognitive control accounts (Hertel, 1997; Joormann, Yoon, & Zetsche, 2007) have attributed a crucial role to cognitive biases in the etiology and maintenance of depressive symptoms. These dominant theoretical models have guided seminal research and led to important discoveries regarding the role of cognitive biases as (causal) risk factors for depression (for reviews, see Gotlib

& Joormann, 2010; Mathews & Macleod, 2005). However, contemporary theoretical accounts often propose a number of cognitive biases without providing a detailed account of how these processes may influence one another and in concert influence the course of depression (for a review, see Everaert et al., 2012). It is only recently that specific ideas and hypotheses regarding the interplay between cognitive biases have begun to emerge in clinical research (Aue & Okon-

Singer, 2015; Everaert et al., 2012; Hertel, 2004; Hertel & Brozovich, 2010; Hirsch et al., 2006;

Wittenborn et al., 2016). In this section, we discuss novel conceptual contributions that have attempted to describe the interplay among cognitive biases in depression in a comprehensive manner. A discussion of the shared and unique predictions by traditional cognitive models with respect to the different CCBH questions can be found elsewhere (see Everaert et al., 2012). 5

The causal loop diagram of depression dynamics

Wittenborn and colleagues recently proposed a causal loop diagram integrating cognitive, social, and environmental factors that may explain the etiology of depression (Wittenborn et al.,

2016). Of particular relevance to the CCBH, this model specifies a reinforcing feedback loop involving attention, interpretation, and memory biases in the consolidation of negative cognitions.

The model proposes that negative cognitive representations that are stored in long-term memory direct attention toward relevant information. Specifically, negative memory representations are hypothesized to both orient and maintain attention on negative material in the environment that matches the content of the memory representations. The resulting negative bias of attention is expected to increase one’s perceived stress level and produce negatively biased interpretations of the situation. This enhanced processing of negative material through biases of attention and interpretation is in turn expected to set the stage for increased negative affect and improved encoding of negative material into memory. This further consolidates the initial negative memory representations, which may in turn guide attention toward congruent information, etc. By defining a pathway with memory bias causing biases of attention and interpretation that in turn fuel memory bias, this model advocates the view that cognitive biases are highly interactive and interdependent processes that cannot be fully understood when studied in isolation.

The Attention-Memory-Bias-Interaction research framework

In an attempt to organize existing research and guide future empirical inquiry, we recently formulated a conceptual framework of interactions between attention and memory biases in psychopathology (Everaert, Bernstein, Joormann, & Koster, 2018). Grounded in basic cognitive research on interactions between attention and memory (for excellent review articles, see Awh,

Vogel, & Oh, 2006; Chun, Golomb, & Turk-Browne, 2011; Hutchinson & Turk-Browne, 2012;

Todd & Manaligod, 2017), the framework proposes several theoretical predictions about causal 6 pathways between attention and memory biases at different stages of information-processing.

Specifically, the framework proposes that attention bias improves memory for negative material by influencing both encoding and retrieval of emotional material. That is, attentional bias may skew the processing of emotional material in favor of negative information, which increases the probability that negative material is encoded into memory and alters what is available for later recollection. In addition, attentional biases may also enhance memory biases after encoding by altering the retrieval of stored emotional items. In remembering emotional experiences, attention bias may influence which cues are used to guide memory search to retrieve details of a past event.

This attention bias during memory search is expected to increase the likelihood of remembering negative memories. In addition to the role of attention bias in influencing emotional memory, the framework proposes that memory biases may guide attention biases toward matching emotional material. Such a memory-guided attention bias may occur because someone’s emotional learning history alters the attentional priority of certain cues in the environment. Alternatively, emotional memories can also be retrieved consciously when searching for relevant emotional information.

Thus, the framework hypothesizes that attention bias influences and is influenced by memory bias.

One important prediction of the framework is that the interactions between attention and memory biases unfold dynamically over time. Indeed, the model considers attention and memory biases as dynamic processes with mutually reinforcing influences. That is, memory biases may shape biases of attention, which may in turn influence what is processed and stored in memory, which again guides attention toward matching emotional information, and so on. Through mutual influences, attention and memory biases are hypothesized to instigate pathogenic cycles of increasingly negative information processing in depression. Critically, the framework argues that cognitive biases may not operate in a stable manner but are better conceptualized as processes that fluctuate over time and across contexts (e.g., before, during, after stressful episodes). This implies 7 that also the mutual influences between cognitive biases may change over time (e.g., stronger connections during stress). Therefore, the framework advocates the view that quantifying the dynamic nature of cognitive biases is key to understanding the intricate interplay between attention and memory biases.

Conceptualizing the combined influence of cognitive biases on depression over time

While recent conceptual contributions predominantly focus on how cognitive biases may interact (i.e., association and causal questions), they do not elaborate extensively on how cognitive biases may combine to influence the course of depression symptoms (i.e., predictive magnitude questions). In this respect, frameworks from research on risk factors in psychiatry (Kraemer et al.,

2001) and cognitive content factors in depression (Abela & Hankin, 2008) may help to understand the combined impact of cognitive biases on depression.

In their seminal article, Kraemer et al. (2001) elaborated on five conceptually different ways in which risk factors may work together to influence an outcome. Putative risk factors may operate as proxy, overlapping, or independent risk factors as well as mediators or moderators. When applied to cognitive biases, several hypotheses can be formulated regarding how attention, interpretation, and memory biases may work together to influence symptoms of depression. A first possibility is that attention, interpretation, and memory biases are independent risk factors. This means that each cognitive bias has a unique relationship with depression symptoms and exerts an influence on depression independent from other cognitive biases. To qualify as independent risk factors, there should be no temporal precedence of different cognitive biases and cognitive biases should not be correlated. A second hypothesis is that a cognitive bias may operate as a proxy risk factor for another cognitive bias. This may occur when one cognitive bias turns out to be a relatively stronger risk factor for depressive symptoms and any of its correlating cognitive biases also appear to be a risk factor for depression (through the strong bias that is correlated with both). For example, 8 through its correlation with memory bias, attention bias may be a proxy for memory bias as a risk factor for depression. The third hypothesis concerns a mediation model in which a cognitive bias intervenes in the relation between another cognitive bias and depressive symptoms. This means that the mediating cognitive bias explains how another cognitive bias influences depression. For example, it is possible that attention bias impacts depression symptoms such as sadness through its influence on interpretation bias. A fourth hypothesis about the combined impact of cognitive biases concerns a moderation model. It is possible that one cognitive bias may moderate effects of another cognitive bias on depression. A moderating cognitive bias determines under what conditions another cognitive bias operates to produce depression symptoms. For example, attention bias may affect the relationship between memory bias and depression, such that greater negative attention bias increases the potency of memory bias to generate depressive symptoms. A final hypothesis is that multiple cognitive biases could be overlapping risk factors in predicting depression. This possibility is likely when cognitive biases are correlated without temporal precedence and have unique relationships with depression symptoms. For example, interpretation and memory bias may be overlapping risk factors through the shared content of negative cognitions resulting from the bias. Given the mutual relations among attention, interpretation, and memory biases (see below for an overview of empirical research), several of these hypothetical interactions (e.g., overlapping risk factor, mediation effect) could be appropriate to characterize how cognitive biases work together to influence depression.

In addition to examining how cognitive biases work together in influencing depressive symptoms, it is of importance to determine which (combination of) biases yields the greatest potency in predicting the symptom course over time. In this respect, research examining the predictive power of multiple depression-linked distortions in cognitive content factors may inform how to conceptualize and quantify the combined impact of cognitive biases. This research has 9 frequently used additive and weakest link models to integrate multiple indices of risk factors (Abela

& Hankin, 2008; Reilly, Ciesla, Felton, Weitlauf, & Anderson, 2012). The additive approach assumes that the severity of distorted cognitive factors has a cumulative effect, such that risk to develop depressive symptoms increases with each additional factor. Applied to cognitive biases, the model predicts that individuals with more severe negative biases in multiple processes are at greater risk to develop depressive symptoms compared to individuals with fewer negative biases.

Alternatively, the weakest link approach predicts that the course of depressive symptoms depends on the most pathogenic cognitive factor and not on the number of factors. The best marker of future increases in depressive symptoms would then be the cognitive process that is dominantly biased toward negative material. While these models may not specify how biases work together, they provide useful integrative indices to conceptualize their combined predictive magnitude.

Methods used to investigate the CCBH

Association questions

In studies examining association questions, investigators have utilized two different methodological approaches. A first approach consists of combining experimental paradigms to measure multiple cognitive biases in a single study. For example, in a study by Gotlib et al. (Gotlib et al., 2004) participants started with an encoding task of emotional and neutral words which was followed by a free recall task to measure memory bias for emotional words. Next, to measure attention biases, participants completed a dot probe task in which emotional-neutral face pairs were presented. Specific to this first approach to study CCBH association questions is that the utilized experimental tasks are unrelated in that they present their own unique stimulus materials. Given this independence of the experimental tasks, this type of studies can address questions regarding the correlations among cognitive biases. For example, these studies may shed light on whether negative biases occur in one or multiple cognitive processes independently (Vrijsen, Van Oostrom, 10

Isaac, Becker, & Speckens, 2014). Yet, a limitation of this type of CCBH studies is that the study design does not provide insight into specific pathways of how cognitive biases may work together.

While hypothesized relations among attention, interpretation, and memory biases can be modeled statistically (for an example, see Sanchez, Duque, Romero, & Vazquez, 2017), it cannot be tested formally how emotional material is modulated by the different biases across different stages of information processing. To answer such association questions, researchers have adopted a second methodological approach.

The second approach also combines multiple experimental tasks or measures of cognitive biases in a single study, but modifies the tasks so that they use the same stimulus materials. These studies typically present the experimental tasks in a fixed temporal order to examine how different cognitive biases during encoding and/or retrieval of the emotional material is related to later processing of this stimulus material. For example, by presenting an attentional bias task followed by a memory task probing recall of the presented stimuli, it is possible to examine whether attention bias skews encoding in favor of negative information and how this is associated with a negative memory bias. By using the same stimulus materials across tasks, these studies enable the investigation of specific aspects of the potential interplay among cognitive biases in a controlled experimental context. For example, we recently examined whether attention bias modulates the interpretation of emotional material and subsequently alters emotional memory (Everaert, Duyck,

& Koster, 2014). To this end, participants unscrambled emotional sentences in either a positive or negative manner (e.g., “born winner am loser a I” into either “I am a born loser” or “I am a born winner”) to measure interpretation bias. When unscrambling the sentences, biases of attention towards negative (e.g., “loser”) vs. positive (e.g., “winner”) words were measured using eye tracking. A subsequent free recall task prompted participants to recollect the constructed interpretations and served as a measure of memory bias. Figure 1 depicts the task procedure. 11

Indeed, the designed dependency among the different cognitive bias and measures allowed this study to track how the same emotional material was treated by the different biases. It is then possible to gain insight into potential pathways from attention bias to memory bias.

At present, several promising paradigms have been developed to study the dependency among cognitive biases at different stages of information processing (see Everaert & Koster, 2015;

Salem, Winer, & Nadorff, 2017; Wells, Beevers, Robison, & Ellis, 2010). However, we think that this field of research could benefit from the development of novel research paradigms to address open research questions. Indeed, many aspects of the interplay among cognitive biases have yet to be investigated such as the role of memory bias in guiding attention toward congruent information.

In this respect, paradigms from basic cognitive science may provide ways forward (see Everaert et al., 2018).

Causal questions

Though certain features of some cross-sectional study designs optimize conditions to examine the effect of one cognitive bias on another process (e.g., temporal order of tasks, similar stimulus materials across tasks), third variables could account for the observed relations. Direct proof of causality requires experimental manipulation of one cognitive bias to track effects on other process. Cognitive bias modification methods provide the tools to test the causal influences among cognitive biases. These procedures encourage acquisition or attenuation of an emotional bias via exposure to experimentally established contingencies within specific task settings (Koster, Fox, &

MacLeod, 2009).

Studies that have tested the assumed influences among cognitive biases have typically employed cognitive bias modification procedures in combination with a transfer cognitive task.

For example, in a study examining the influence of attention bias on emotional memory (Blaut et al., 2013), participants were trained to orient attention away from negative words. The attention 12 manipulation involved a training variant of the dot-probe task in which a probe (the target) consistently replaced the neutral stimulus, instead of the equal replacement probability in the standard task design. The impact on memory bias was measured by presenting participants with a memory task in which novel neutral, negative, and positive words were presented. In a study testing whether interpretation bias influences memory (Tran, Hertel, & Joormann, 2011), interpretation bias was first modified by presenting participants with a series of ambiguous stories each ending with a word fragment for participants to complete. This word fragment imposed either a positive meaning (in the positive training condition) or negative meaning (in the negative training condition) on the ambiguous story. After the positive or negative interpretation bias training, participants completed an ambiguous stories interpretation task and a recall task testing memory for the ambiguous scenarios from the previous task. Such study protocols clearly enable the investigation of the effects of the manipulated cognitive bias on other cognitive processes.

Though several studies have examined causal relations among cognitive biases, a potential threat to CCBH research is the availability of effective procedures to modify cognitive biases.

While various novel approaches have been developed to modify attention and interpretation biases

(Bernstein & Zvielli, 2014; Sanchez, Everaert, & Koster, 2016), there is some doubt about whether current approaches to modify cognitive biases such as attention bias using traditional dot probe training tasks are successful (Koster & Bernstein, 2015; Okon-Singer, 2018). Moreover, cognitive bias modification procedures to target biases of memory are largely lacking (but see, Vrijsen,

Becker, et al., 2014). Given the scarcity of appropriate methods, there is currently no research testing how memory bias may causally contribute to biases in attention or interpretation. The absence of reliable cognitive bias tasks and modification procedures may jeopardize progress in research on individual cognitive biases and integrative research testing the CCBH. Therefore, we 13 think that the development of novel cognitive bias modification methods represents an important challenge for future research examining causal relations among cognitive biases.

Predictive magnitude questions

Longitudinal research designs are required to address predictive magnitude questions. To date, prospective studies exploring how multiple cognitive biases work together to influence depression are scarce. Existing research utilized a longitudinal design measuring cognitive biases at baseline and depression symptom levels at a later time point (Everaert, Duyck, & Koster, 2015).

Though valuable as a first step, such study designs have important limitations that need to be addressed in future research. First, longitudinal designs with only two time points have a low temporal resolution. This restricts data-analysis to tests of linear change models. While linear models might be appropriate to describe the change in depression symptoms and/or cognitive biases, it is plausible that the rate of change is not constant over time and may take a nonlinear shape. For example, the shape of change might be related to contextual factors such as the occurrence and chronicity of stressful events. By conducting at least four waves of data collection, studies can explore richer models of nonlinear change (e.g., quadratic growth) in depressive symptoms as well as how cognitive biases are related to different change parameters. Second, previous research has measured cognitive biases only at baseline. As such, these studies cannot examine whether there are changes in multiple cognitive biases over time and if such changes covary with changes in depressive symptoms or other contextual factors (e.g., perceived stress).

Therefore, it is recommended that future longitudinal studies administer the same test battery of cognitive biases and symptom measures at each wave of data collection. Indeed, this again requires reliable cognitive tasks that also accurately conceptualize cognitive biases (Rodebaugh et al.,

2016). 14

Empirical research on the CCBH

Association questions

Since the seminal study by Gotlib et al. (Gotlib et al., 2004), several studies have investigated correlations among cognitive biases using unrelated cognitive tasks. One study presented formerly depressed patients and never-disordered control individuals with a dot probe task followed by a self-referential encoding and free recall task after a mood induction (Vrijsen,

Van Oostrom, et al., 2014). Similar to Gotlib et al.’s study in a clinical sample, no statistically significant correlations were found between attention and memory biases in formerly depressed individuals. By contrast, a recent study employing a different battery of experimental tasks reported some evidence for correlations among different cognitive biases (Sanchez et al., 2017). This study presented participants with varying depressive symptom levels with an engagement-disengagement task (measure of attention bias), scrambled sentences task (measure of interpretation bias), and a free-recall autobiographical memory task (measure of memory bias). The results of that study showed that attentional biases for sad faces were positively correlated with negative interpretation biases as well as negative memory biases. Taken together, research utilizing unrelated cognitive bias tasks has produced mixed findings, rendering conclusions regarding correlations among cognitive biases difficult.

Important advances have been made by research that has attempted to model specific aspects of the interplay between cognitive biases. Building on initial work suggesting that depression-linked attention biases during encoding enhance later memory for negative material and impair memory for positive material (Ellis, Beevers, & Wells, 2011; Koster, De Raedt, Leyman, &

De Lissnyder, 2010; LeMoult & Joormann, 2012; Wells et al., 2010), researchers studied the role of interpretation bias in the relation between attention and memory bias. In one recent study

(Everaert et al., 2014), participants with varying depression levels completed an interpretation task 15 requiring them to unscramble emotional sentences in either a positive or negative manner (e.g.,

“born winner am loser a I” into either “I am a born loser” or “I am a born winner”). When participants were unscrambling the sentences, biases in attention towards negative (e.g., “loser”) vs. positive (e.g., “winner”) words were measured using eye tracking. A subsequent free recall task prompted participants to recollect the constructed self-referent meanings. This procedure is also depicted by Figure 1. The results suggested that a bias in attentional selection toward negative words was related to a higher proportion of negative interpretations, which was in turn related to better memory for negative interpretations. Thus, interpretation bias may mediate the relation between attention selection bias and memory bias. Moreover, sustained attention toward negative words was directly related to a negative memory bias, without interpretation bias intervening in this relationship. This pattern of findings suggests that attention bias is both directly and indirectly related to memory bias via interpretation bias. Interestingly, the mediation model in which interpretation intervenes in the relation between attention bias and memory bias was also found by another study using an exogenous cueing task in combination with a scrambled sentences test and subsequent free recall task (Everaert, Tierens, Uzieblo, & Koster, 2013).

Extending this work on explicit memory (i.e., storage systems that represent knowledge in a consciously accessible manner), one recent study examined whether symptoms of anhedonia are related to negative biases in attention and implicit memory (Salem et al., 2017). In that study, participants completed the attentional dot probe task followed by a two-alternative forced-choice recognition task to measure implicit memory for stimuli that were presented during the dot probe task. In the two-alternative forced-choice recognition task, participants were briefly presented with a stimulus word from the dot probe task, which was then masked and replaced by two response choices (including the target word and a foil). Participants were instructed to select the word that was presented during the attention task. The results showed that implicit memory bias moderated 16 the relation between attention bias and symptoms of anhedonia. Negative attention bias was associated with anhedonia only at high levels of implicit memory bias. However, the effect sizes were relatively small in this study. This suggests that attention biases do not influence implicit memory as they may influence explicit memory bias.

Finally, there is some evidence suggesting that emotional memory may guide attention bias.

Studies testing this hypothesis involve a learning phase followed by an attention task. The learning phase typically involves repeated pairings of neutral stimuli and emotional outcomes while participants execute a cover task. The attention task subsequently presents only the neutral stimuli to examine whether the learned associations with emotional content may capture attention. Studies on depression have shown that individuals with higher depression levels do not orient attention toward stimuli associated with reward in an attentional cueing task (Brailean, Koster, Hoorelbeke,

& De Raedt, 2014) or visual search task (Anderson, Leal, Hall, Yassa, & Yantis, 2014).

In sum, research modeling specific hypothesized interactions among cognitive biases by using related cognitive tasks seems to produce more consistent findings compared to studies testing correlations among cognitive biases with independent tasks. The available research evidence suggests that attentional biases may be related to a subsequent congruent bias in memory, and that emotional memory may modulate attention allocation.

Causal questions

In researching causal relationships among cognitive biases, a fruitful line of studies has attempted to understand the causes of memory bias. Initial research has focused on the role of interpretation bias in explaining emotional biases in memory. Findings from studies in nonclinical samples suggest that memory recall is affected by interpretation biases operating during encoding of emotional material as well as by interpretation biases acquired after emotional material has been encoded (Salemink, Hertel, & Mackintosh, 2010; Tran et al., 2011). Recently, a study sought to 17 extend this work by recruiting a sample of clinically depressed individuals (Joormann, Waugh, &

Gotlib, 2015). In this study, participants completed an interpretation bias training inducing a positive interpretation bias. Next, they completed an ambiguous stories interpretation task followed by a recall task testing memory for the ambiguous scenarios from the previous task. The results showed that only induced positive interpretation bias had training-congruent effects on the recall for endings (i.e., interpretations) of ambiguous scenarios. This finding substantiates the role of

(positive) interpretation bias in influencing memory bias.

In addition to interpretation biases, attention biases have also been hypothesized to influence emotional memory. In a recent study by Blaut et al. (2013), participants allocated to the training condition were trained to orient attention away from negative words to test the influence of attention bias on memory. Participants in the control condition were not trained to orient attention to specific emotional stimuli. The attention manipulation involved a training variant of the dot-probe task in which a probe (the target) consistently replaces the neutral stimulus, instead of the equal replacement probability in the standard dot-probe task. It was found that individuals with higher depression levels did not exhibit a memory bias for negative words when they were trained to orient attention away from negative words. A typical memory bias occurred in the no- training control group. These findings suggest that attention bias may causally influence memory bias.

Another line of research addressing causal CCBH questions examined causal links between biases of attention and interpretation in samples of individuals reporting varying depressive symptom levels. To test the hypothesized influence of attention bias on interpretation, two recent studies have trained participants to orient attention toward either positive or negative words using a dot probe training task. Transfer of attention bias training to interpretation bias was examined by the scrambled sentences test following the training procedure (Everaert, Mogoaşe, David, & 18

Koster, 2015). Across both studies, the training procedure was not successful at effectively inducing emotional biases in attention allocation in the training condition. No differences in attention bias were observed between the training and control condition. Instead, there was considerable variability in the extent to which participants acquired an attention bias in the training condition. However, the individual differences in attention bias acquisition were not related to the performance on the interpretation tasks.

To date, there is one study examining whether interpretation bias influences attention bias in a sample of adolescents with major depression (LeMoult et al., 2017). In this study, participants received either six sessions of positive interpretation bias training or neutral training followed by an interpretation and attention bias task. The training was effective in that adolescents receiving positive training also interpreted ambiguous scenarios more positively than did participants who received the neutral training. However, there was no transfer of the training to the dot probe task as a measure of attention bias. Consequently, no empirical support was found for an influence of interpretation bias on attention bias.

Taken together, several studies have provided consistent evidence for the role of interpretation bias in influencing memory bias. Yet, current research has not been able to provide empirical support for mutual influences between attention and interpretation biases related to depression. This is remarkable in light of the empirical support for such mutual influences between attention and interpretation biases coming from studies on anxiety (cf. Amir, Bomyea, & Beard,

2010; White, Suway, Pine, Bar-Haim, & Fox, 2011). In examining the potential causal linkages between attention and interpretation bias in depression, studies could take advantage from novel and promising methods to induce or reduce biases of attention and interpretation (cf. Bernstein &

Zvielli, 2014; Sanchez et al., 2016). 19

Predictive magnitude questions

There is currently only one study that examined how biases in attention, interpretation, and memory combine to predict future depressive symptom levels (Everaert, Duyck, et al., 2015). This study tested the predictive value of two integrative approaches to model the combined impact of multiple cognitive biases, namely the additive (i.e., cognitive biases have a cumulative effect) vs. the weakest link (i.e., the dominant cognitive bias is important) model. To this end, this study organized a one-year follow-up on the cross-sectional study quantifying relations among biases of attention, interpretation, and memory (Everaert et al., 2014). At the follow-up moment, participants’ depressive symptom levels were reassessed. The results revealed that the weakest link model had incremental validity over the additive model in predicting prospective changes in depressive symptoms, though both models explained a significant proportion of variance in the change in depressive symptoms. These initial findings suggest that the best cognitive marker of the evolution in depressive symptoms is the cognitive process that is dominantly biased toward negative material. This finding highlights the importance of considering idiographic cognitive profiles with multiple cognitive processes for understanding cognitive biases in depression.

While this study aimed to conceptualize which (combination of) biases yields the greatest potency in predicting the symptom course over time, it does not cast light on how multiple cognitive biases work together in predicting future depression levels. As noted, addressing such questions requires longitudinal designs involving multiple waves of data collection to measure cognitive biases and depression symptoms at multiple time points. Further research is needed to address this open question.

Limitations and future directions

Though research has made considerable progress in the past six years, several limitations remain and represent important directions for future research. First, various aspects of the interplay 20 among cognitive biases have received only limited empirical attention or have yet to be investigated. To date, most studies have examined the role of attention bias during encoding of emotional material to explain emotional biases in memory. Indeed, much has yet to be discovered about whether and how attention bias modulates memory retrieval as well as how memory bias may shape attention allocation in depression. As outlined, several challenges also remain for research examining the causal direction of the observed associations between cognitive biases.

Also, much remains to be understood about the combined influence of multiple cognitive biases on the course of depression. Advances with respect to these issues require targeted research that can be guided by the theoretical models discussed above.

Second, much remains to be learned about the neural basis of interactions among cognitive biases in depression. This is because current research on the CCBH has solely relied on behavioral tasks. Fortunately, research has identified neural mechanisms that are involved in cognitive biases in depression (Belzung, Willner, & Philippot, 2015; De Raedt & Koster, 2010; Disner, Beevers,

Haigh, & Beck, 2011). For example, biased information processing has been linked to greater and more sustained amygdala reactivity, hypoactivity in the left dorsolateral prefrontal cortex, and hyperactivity in the right dorsolateral prefrontal cortex. Moreover, memory bias has been associated with amygdala hyperactivity, which is positively correlated with activity in the hippocampus, caudate, and putamen (Disner et al., 2011). To gain insight into the interactions among cognitive biases at the neural level, future research may be guided by existing cognitive neuroscience research examining attention – memory interactions (Chun & Turk-Browne, 2007) as well as pathophysiological models of depression (Belzung et al., 2015).

Third, prior research has generally studied cognitive biases at the disorder level and ignored the heterogeneous nature of depression. The dominant focus on the disorder level may be problematic because it overlooks critical differences in the importance of individual symptoms, 21 differential relations between symptoms, as well as differential relations between symptoms and risk factors such as cognitive biases (Fried, 2015; Fried, Nesse, Zivin, Guille, & Sen, 2014). From both a theoretical and clinical stance, knowledge of whether (clusters of) symptoms are more closely related to (combinations of) cognitive biases is urgently required. Therefore, future research on the CCBH needs to adopt a symptom level approach to gain insight into how multiple cognitive biases may maintain clusters of depression symptoms.

Fourth, while most studies testing the CCBH in depression focus on attention, interpretation, and/or memory biases, various relations with other critical cognitive factors await systematic empirical scrutiny. For example, studies have just started to examine the role of different executive control difficulties in the interplay between biases of attention and interpretation during encoding stages (e.g., Everaert, Grahek, & Koster, 2017). Also, research has started to document the role of expectancy biases in guiding attention in the context of anxiety (Aue & Okon-Singer,

2015). This research may inform advances in studies examining interactions among biases of attention, interpretation, and memory in depression. Indeed, broadening the scope of cognitive factors that are studied within a CCBH framework may provide an interesting avenue for future research, and holds potential to facilitate an integrated understanding of the cognitive foundations of depression.

Finally, currently little is known about factors that may modulate the interplay among cognitive biases. Indeed, the interplay among multiple cognitive biases may be influenced by factors that impact the expression of individual cognitive biases, such as stimulus-driven salience

(but also see Niu, Todd, & Anderson, 2012), self-reference (Everaert, Podina, et al., 2017; Gaddy

& Ingram, 2014) and counter-regulation processes (Schwager & Rothermund, 2013). Of interest to understanding how cognitive biases interact within a specific context are an individual’s current goals. Goals have been defined as motivational representations of a desired end state (Dickson, 22

Johnson, Huntley, Peckham, & Taylor, 2017). The nature of an individual’s goals and the specific formulation (e.g., approach vs. avoidance-oriented goals) may shape one’s cognitive set. This cognitive set may then alter which information will become more salient in the external environment and/or is more accessible for retrieval from memory. Thus, goals may influence memory retrieval and attention allocation toward cues in a congruent manner and may override initial negative biases to attain the goal (Vogt, Koster, & De Houwer, 2017). Exploring the role of goals in the interplay among cognitive biases in depression provides an important avenue for future research.

Summary

The past several years have seen important theoretical, methodological, and empirical advances in discovering the interactions among attention, interpretation, and memory biases in depression. This review provides an overview of recent theories, methods, and research on the combined cognitive biases hypothesis in depression. The accumulated research findings provide evidence for interrelations among cognitive biases, suggesting that these processes should be studied in an integrative manner to understand their role in depression. Yet, there is much that remains to be understood about their complex interplay. To this end, guiding frameworks and methodological approaches are discussed to stimulate targeted research with the aim of gaining a comprehensive understanding of how multiple cognitive biases are involved in depression.

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