Implicit cognition and depression: A meta-analysis

Clinical Psychology Review 30 (2010) 691–709

Contents lists available at ScienceDirect

Clinical Psychology Review

Implicit cognition and depression: A meta-analysis Wendy J. Phillips ⁎, Donald W. Hine, Einar B. Thorsteinsson School of Behavioural, Cognitive and Social Sciences, University of New England, Armidale, NSW, 2351, Australia

a r t i c l e

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Article history: Received 18 November 2009 Received in revised form 10 May 2010 Accepted 14 May 2010 Keywords: Depression Cognitive bias Implicit Dual-process Meta-analysis Self-reference

a b s t r a c t This study examined the relationship between negative self-referential implicit cognition and depression. A meta-analysis of 89 effect sizes from a pooled sample of 7032 produced a weighted average effect size of r = .23. Moderator analyses, using an expanded set of 202 effect sizes, indicated that effect sizes relating to all facets of cognition, study designs and sample types significantly predicted depression. Significant heterogeneity was observed in effect sizes across facets of cognition, cognitive manipulations and measurement strategies. Studies that assessed interpretation and self-beliefs, utilized mood and cognitive load manipulations, and employed the Self-Descriptiveness Judgement Task produced the largest effect sizes. The transfer-appropriate processing view of implicit memory was supported and significant biases were observed at both early and late stages of attention. Overall, results support cognitive models of depression and suggest that implicit cognition reliably predicts past, current, and future depression. Consequently, treatment efficacy may be improved by incorporating strategies that target implicit processes. © 2010 Elsevier Ltd. All rights reserved.

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Introduction . . . . . . . . . . . . . . . . . . . . . 1.1. Facets of cognition and measurement strategies . 1.1.1. Attention . . . . . . . . . . . . . . . 1.1.2. Memory . . . . . . . . . . . . . . . 1.1.3. Interpretation and self-beliefs . . . . . 1.1.4. Self-esteem . . . . . . . . . . . . . . 1.2. Study design . . . . . . . . . . . . . . . . . 1.3. Cognitive reactivity and control . . . . . . . . 1.4. Rationale for this meta-analysis . . . . . . . . Method . . . . . . . . . . . . . . . . . . . . . . . 2.1. Literature search . . . . . . . . . . . . . . . 2.2. Inclusion criteria . . . . . . . . . . . . . . . 2.2.1. Stimuli . . . . . . . . . . . . . . . . 2.2.2. Samples and depressive status . . . . . 2.2.3. Implicitness . . . . . . . . . . . . . 2.2.4. Statistics . . . . . . . . . . . . . . . 2.3. Moderator coding . . . . . . . . . . . . . . . 2.3.1. Aspect of cognition . . . . . . . . . . 2.3.2. Cognitive reactivity and control . . . . 2.3.3. Sample type . . . . . . . . . . . . . 2.3.4. Measurement strategy . . . . . . . . 2.3.5. Priming threshold. . . . . . . . . . . 2.3.6. Processing level. . . . . . . . . . . .

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⁎ Corresponding author. Tel.: +61 2 6773 3606; fax: +61 2 6773 3820. E-mail address: [email protected] (W.J. Phillips). 0272-7358/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.cpr.2010.05.002

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2.4. Inter-coder reliability. . . . 2.5. Statistical analyses . . . . . 3. Results . . . . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . 4.1. Theoretical implications . . 4.2. Methodological implications 4.3. Treatment implications . . . 4.4. Limitations . . . . . . . . 4.5. Future directions. . . . . . 4.6. Conclusion. . . . . . . . . References . . . . . . . . . . . . . .

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1. Introduction Depression is believed to occur when negative self-beliefs and processing biases impede an individual's ability to regulate emotional responses to adverse experiences. According to cognitive theories of depression, depressed individuals possess representations of selfreferential information involving themes of loss, failure, worthlessness, rejection and hopelessness (Abramson, Metalsky, & Alloy, 1989; Beck, 1967; Ingram, Miranda, & Segal, 1998). When activated by an environmental trigger, self-schemas are thought to generate automatic and systematic biases in information processing. Patterns of activation are presumed to reflect existing cognitive structures, where one activated negative memory or emotion node activates all other nodes in an individual's associative network (Beck, 2008; Bower, 1981; Ingram et al., 1998; Teasdale, 1988). Therefore, activated schemas increase the likelihood of depressive episodes, and the presence of a negative self-schema represents a relatively stable vulnerability factor for future depression. Although most theoretical approaches to depression agree that negative self-views play an important role in depression, the hypothesized manner in which they exert their influence is more contentious. Some researchers believe that individuals are at greatest risk for depression when they consciously possess negative selfreferential attitudes and engage in self-defeating reasoning and thinking styles (e.g., Alloy et al., 2000; Nolen-Hoeksema, 2000). Other researchers consider that negative self-schemas precipitate depression by influencing automatic, often preconscious, cognitive responses to experiences (for reviews, see Ingram et al., 1998; Scher, Ingram, & Segal, 2005). Considerable bodies of research have addressed both views. Recently, conscious versus automatic perspectives have been integrated within a dual-processing framework. Dual process theories advocate that people possess two distinct information processing systems: 1) an implicit system that involves automatic processing, requires little cognitive effort, and is guided by slow-forming associative memory constructs, and 2) an explicit system that employs deliberate processing, involves motivated effort, and is directed by rapidly-acquired rule-based learning (Evans, 2008). Regarded as output from the two systems, implicit and explicit cognitions are presumed to possess the characteristics of their system of origin. Deemed to represent conscious evaluations, explicit cognitions are widely assessed by measures that require participants' deliberate consideration (e.g., self-report questionnaires). In contrast, the automatic nature of implicit cognitions requires assessment under indirect, unconscious or uncontrolled conditions (e.g., reaction times or memory associations). According to Forgas (2000), healthy mood regulation involves an interaction between the two systems. Implicit processing is posited to maintain current mood by gathering mood-congruent information until an affective threshold is reached. At that time, explicit processing is triggered to restore homeostasis by seeking mood-incongruent information. In line with this model, Beevers (2005) proposed that depression occurs when negatively biased implicit processing

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remains uncorrected by explicit processing. In this conceptualization, an initial response to a negative stimulus represents the activation of negative implicit schemas. Corrective explicit processing may reinterpret the stimulus and override the negative implicit response, which relieves negative affect. However, vulnerability may be exposed when negatively biased implicit cognitions remain uncorrected by explicit processing; resulting in negative explicit cognitions, increased dysphoria, depleted cognitive resources and a downward spiral into depression. Thus, implicit cognitions are hypothesized to represent the origin of depression. Haeffel et al. (2007) proposed a similar model, but expressed the converse view that explicit cognitions are most critical because they represent the more proximal cognitive determinants of depression. In accordance with dual process formulations, empirical evidence suggests that depression interferes with effortful processing but only minimally disrupts automatic processing (e.g., Bargh & Tota, 1988; Hammar, Lund, & Hugdahl, 2003; Hartlage, Alloy, Vázquez, & Dykman, 1993). For example, several studies have observed biases amongst depression-vulnerable individuals only under conditions in which their cognitive resources have been experimentally depleted and automatic processes exposed (e.g., Wenzlaff & Bates, 1998; Wenzlaff & Eisenberg, 2001). Similarly, neurophysiological research suggests that depressed individuals experience increased activity in limbic brain regions associated with emotional responses, and decreased activity in frontal regions that regulate limbic activity (e.g., Johnstone, van Reekum, Urry, Kalin, & Davidson, 2007; Siegle, Thompson, Carter, Steinhauer, & Thase, 2007). In summary, dysregulated top-down processing appears to be coupled with accentuated bottom-up processing, resulting in the dominance of the latter. The hypothesized interplay between processing systems in depression suggests that corrective explicit processes would not be required, and negatively biased explicit cognitions would not occur, if implicit self-referential cognitions were predominantly positive. Thus, increasing our understanding of biased implicit cognitions in depression represents an important research goal. 1.1. Facets of cognition and measurement strategies Cognitive models of depression predict that depressed individuals will exhibit negative biases in implicit attitudes toward the self, and in all aspects of information processing. Particularly relevant facets of cognition include attention, memory, interpretation, self-beliefs and self-esteem. A summary of these facets is provided in the sections that follow, along with descriptions of the primary measures used to assess them. 1.1.1. Attention Automatic attention allocation reflects an individual's goals, emotions, moods, and task demands, and is greatly influenced by prior experience (Hertel, 2002). Therefore, individuals with negative self-views may preferentially respond to negative self-referential

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environmental cues. In a cyclical fashion, such selective attendance may maintain depressed mood and serve to substantiate and consolidate detrimental self-related cognitions. Depression research has assessed biases in three identified subsystems of attention: shifting, engagement and disengagement (Posner, Inhoff, Friedrich, & Cohen, 1987). Hypothesized biases in orienting attention toward negative selfrelated stimuli have been assessed by a variety of tasks. Probe tasks provide relatively direct measures of shifts in attention towards the location of emotional stimuli. Stimuli usually comprise negative and neutral (and/or positive) trait and/or state self-descriptive adjectives. Pairs of stimuli are presented (e.g., one negative and one neutral) and participants are required to respond rapidly to a probe stimulus (e.g., a dot or symbol) that replaces one cue. Negative attentional biases are indicated by faster reaction times (RTs) to negative than to neutral cues. Short stimulus durations (e.g., up to 500 ms) have been used to measure early stage orienting processes, whereas longer stimulus durations (e.g., 1000 ms) have been employed to assess sustained attentional processing. In the deployment-of-attention task (DOAT), a negative and a neutral (or positive) word are simultaneously replaced with bars of different colors. Participants are misinformed that the bars appear sequentially and asked to report which color appears first. Perception of the color bar that replaces the previously-attended word will precede perception of the other bar. Thus, negative biases are observed when colors that replace negative words are identified more often than colors that replace neutral words. This task's forcedchoice format avoids possible confounds of motivational and response speed factors that may differentiate groups in RT tasks (McCabe, Gotlib, & Martin, 2000). This is particularly relevant to depression research, where affected individuals typically exhibit symptomatic cognitive impairment (Pelosi, Slade, Blumhardt, & Sharma, 2000). Other tasks have used interference effects to measure automatic attentional processes. In the Dichotic Listening Task, participants are asked to repeat information presented to one ear and ignore information presented to the other ear. Negative self-referential biases are hypothesized to interfere with the ability to ignore the unattended channel when negative stimuli are presented. Thus, attentional biases are observed when negative words presented to the unattended ear are associated with slower RTs or with greater numbers of shadowing errors than contrast words. Similarly, the Emotional-Stroop task requires participants to identify the color of presented words while ignoring their meaning. Attentional biases are inferred from slower color-naming latencies for negative compared to neutral words, with the assumption that the word's meaning has interfered with the participants' ability to color-name. However, the Stroop has been criticized as a measure of attentional bias because differences in interference may be due to either input (attentional) or output (response) processes (Gotlib, Neubauer Yue, & Joormann, 2005). Reviewers of the attentional bias literature have noted that depressive biases for negative stimuli tend to be found at longer, rather than shorter, stimulus presentations (Gotlib & Joormann, 2010; Mogg & Bradley, 2005; Wisco, 2009). For example, Mogg, Bradley, and Williams (1995) conducted a dot-probe task involving pairs of negative (or positive) and neutral words at subliminal (14 ms) and supraliminal (1000 ms) stimulus durations. Compared to controls, depressed participants were significantly faster to detect probes that replaced negative words only in the supraliminal condition. Other dot probe studies have reported similar results (e.g., Bradley, Mogg, & Lee, 1997; Donaldson, Lam, & Mathews, 2007; Mathews, Ridgeway, & Williamson, 1996), and subliminal presentations of stimuli in Emotional Stroop tests have also failed to reveal depressive biases (Lim & Kim, 2005; McNeely, Lau, Christensen, & Alain, 2008; Yovel & Mineka, 2005). However, it should be noted that some studies have found depression-related biases after short stimulus exposures on

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visual tasks (e.g., Siegle, Ingram, & Matt, 2002) and others have failed to detect biases after long exposures (e.g., Yovel & Mineka, 2004). The tendency to observe biases at longer stimulus durations suggests that depression may not be associated with preferential attentional shifting toward negative self-referential stimuli, but with sustained attention to it (Mogg & Bradley, 2005; Wisco, 2009) that may reflect an inability to disengage from it (Gotlib & Joormann, 2010; Wisco, 2009). Probe tasks are ill-equipped to assess this possibility because they provide only a broad measure of attentional bias (Koster, De Raedt, Goeleven, Franck, & Crombez, 2005). The Exogenous Cuing Task (Posner, 1980) addresses this limitation by varying cue-target intervals to provide separate indicators for engagement and inhibition aspects of attention. A valenced target stimulus appears in either the same or opposite spatial location as a preceding prime stimulus. Participants respond faster when the valence of prime and target coincide at short intervals (i.e., cue validity), whereas the effect disappears or reverses at longer intervals because attention is inhibited in favour of new locations. The emotional modification of this task involves comparing responses to emotional versus neutral words. Negative biases may be observed by extended cue validity effects for negative words, faster engagement to negative words on valid trials and/or slower disengagement from them on invalid trials. Using the exogenous cuing paradigm, Koster et al. (2005) found that dysphoric undergraduate participants were significantly slower to disengage attention from negative self-referential words than nondysphoric participants, but only at long stimulus presentations. Research using other methods has also suggested that depression is associated with impaired disengagement from negative information (Gotlib & Joormann, 2010). For example, Rinck and Becker (2005) found that depressed participants were not characterized by enhanced detection of negative words in a word matrix but were more easily distracted by them. Difficulty disengaging from negative stimuli may sustain depressed mood by increasing the availability of negative information and the likelihood of rumination (Donaldson et al., 2007). Researchers have hypothesized that depression-related difficulties in disengaging attention from negative information may reflect deficits in cognitive inhibition. Inhibition aspects of attention have been investigated using negative affective priming (NAP; Joormann, 2004). In this task, two consecutive trials are presented, each of which contains a target word and a distractor word (e.g., positive or negative). For each display, participants are required to name the target and ignore the distractor. A negative bias is indicated by faster response latencies to negative targets that follow negative distractors on previous trials. Using NAP, Joormann (2004) found that nondysphoric participants were slower to identify the valence of positive and negative adjectives following similarly-valenced distractors, whereas dysphoric participants inhibited only positive distractors and demonstrated faster responses to negative adjectives presented after negative distractors (i.e., indicating ineffective inhibition). Similar methods have facilitated the detection of deficits in other aspects of inhibition in depression, such as removing negative information from working memory (e.g., modified Sternberg Task; Joormann & Gotlib, 2008). 1.1.2. Memory Implicit memory is usually defined as an unintentional process in which “performance on a task is facilitated in the absence of conscious recollection” (Graf & Schacter, 1985, p. 501). Associative memory networks are believed to operate in a bidirectional fashion (Bower, 1981); where affective states influence memory processes and memories influence affect. Thus, activated connections between negative emotions, arousal, and episodic information may affirm negative self-schema and perpetuate dysphoria. A recall bias for positive self-referential information represents the normative baseline

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exhibited by non-depressed groups, whereas depressed individuals exhibit biases toward negative information and/or away from positive information (Matt, Vázquez, & Campbell, 1992). Depression researchers have sought to identify similar depression-related biases in implicit memory by assessing the recognition of valenced environmental stimuli. Implicit recognition biases are typically measured by priming, where responses indicate exposure to previously encoded material. Word completion tasks involve the initial presentation of a list of negative and positive words under an encoding condition. Later, participants are given a list of word stems or fragments and asked to complete each one with the first word that comes to mind. No reference is made to the encoding session. Half can be completed to form words from the encoded list (primed words), and half can form words from a new list (unprimed words). The difference between the number of primed and unprimed negative words completed provides an index of negative bias. Similarly, the difference between primed and unprimed positive words completed indicates degree of positive bias. Alternative retrieval paradigms have included providing word associations to experimental cues (Watkins, Martin, & Stern, 2000), creating words from anagrams (Ellwart, Rinck, & Becker, 2003), or naming positive or negative words aloud (i.e., word identification). Priming also features in the most commonly used RT measure of implicit memory; the Lexical Decision Task. In this task, lexical stimuli (comprising words and non-words) are briefly presented, masked, and then presented again. Upon the second presentation, participants are required to identify whether each stimulus is a legitimate (e.g., English) word by indicating “yes” or “no” as quickly as possible. Faster responses to positive than to neutral words indicate a positive implicit memory bias and faster responses to negative than to neutral words indicate a negative bias. Despite the importance of the hypothesized role of implicit memory in depression, literature reviewers have reported inconsistent observations of depression-related biases (e.g., Gotlib & Joormann, 2010; Watkins, 2002; Wisco, 2009). Several cross-sectional studies of depressed and non-depressed participants have found strong negative biases amongst depressed participants (e.g., Bradley, Mogg, & Williams, 1994; Bradley, Mogg, & Millar, 1996; Ruiz-Caballero & González, 1994;1997). For example, Ruiz-Caballero and González (1997) found that, compared to non-depressed participants, depressed participants completed more word-stems with primed than with unprimed negative words and fewer word-stems with primed than with unprimed positive words. However, a similar number of studies have failed to observe significant group differences (e.g., Danion, Kauffmann-Muller, Grangé, Zimmerman, & Greth, 1995; Denny & Hunt, 1992; Ilsley, Moffoot, & O'Carroll, 1995; Watkins, Mathews, Williamson, & Fuller, 1992). Two explanations for the inconsistencies in the implicit memory literature have been proposed. Barry, Naus and Rehm (2004) suggested that implicit memory biases will only appear if the same level of processing is employed during both encoding and retrieval tasks. Referred to as transfer-appropriate processing (TAP), this view predicts that either perceptual processing or conceptual processing must be activated at both times. Perceptual processing refers to datadriven processing that may occur without awareness of semantic content (e.g., counting letters in words), whereas conceptual processing involves the effortful analysis of stimulus meaning (e.g., rating pleasantness of words). Barry et al.’s (2004) review of the implicit memory literature found that almost all studies conformed to the TAP framework. Depression-related negative biases were observed in most studies that used perceptual (e.g., Ruiz-Caballero & González, 1997) or conceptual (e.g., Watkins, Vache, Verney, Muller, & Mathews, 1996) encoding and retrieval strategies, whereas biases were not found in most studies that used a combination of perceptual and conceptual tasks (e.g., Denny & Hunt, 1992). Other theorists have challenged the TAP account (e.g., Watkins, 2002; Wisco, 2009). They contend that evidence supporting TAP may

be confounded by inclusion of several Lexical Decision Task studies in the perceptual category, which may be inappropriate because semantic content has been shown to influence task performance (Neely, 1977). Conversely, these theorists have argued that conscious elaboration underlies implicit memory (Williams, Watts, MacLeod, & Mathews, 1997) and that depressive biases will only emerge if conceptual processing is employed at the time of encoding and retrieval (Watkins, 2002; Wisco, 2009). Thus, controversy continues to surround the level of processing required to demonstrate implicit memory biases in priming tasks. At least one study has investigated existence of biases in implicit autobiographical memory in depression (e.g., MacLeod, Tata, Kentish, & Jacobsen, 1997). Whereas implicit recognition tests involve unconscious processes, autobiographical memory tasks may be considered implicit if the measurement outcome involves rapid processing. For example, participants may be required to report as many positive or negative memories as they can within a limited timeframe. 1.1.3. Interpretation and self-beliefs Depression is associated with a set of maladaptive beliefs, including perfectionistic self-standards, personal inadequacy, rejection by others, self-blame for negative outcomes, and pessimistic personal future-event predictions (Wisco, 2009). Our interpretations of experiences, interactions, and stimuli are largely determined by our belief system. Thus negative self-beliefs may underlie an interpretive bias in depression, in which ambiguous information is processed in an unrealistically negative self-referential manner. In turn, negative interpretations may reinforce self-beliefs and amplify negative mood. Very few methods have been developed to assess implicit selfbeliefs and interpretive biases. The most widely used measure is the Scrambled Sentences Test (SST; Wenzlaff, 1993), which evaluates participants' tendencies to interpret ambiguous information (e.g., “winner born I am loser a”) as a positive (“I am a born winner”) or a negative (“I am a born loser”) self-belief. Participants are presented with a series of scrambled sentences and asked to use five of the six words to create the first grammatically correct sentence that comes to mind. The task is completed under time pressure and a neutral condition may be included to obscure the purpose of the task. Percentage of negative solutions provides an index of negative interpretive bias. Wenzlaff, Rude, Taylor, Stultz, and Sweatt (2001) expressed concern over the implicitness of the SST due to its relative transparency and the potential for participants to consciously control their responses. However, the SST has been included in this metaanalysis because it meets two alternative implicitness criteria: fast and efficient (cf. Inclusion criteria; see De Houwer & Moors, 2010). Other interpretive bias measures have included asking individuals to write down verbally-presented homophones that could be interpreted as negative or neutral words (Wenzlaff & Eisenberg, 2001); to unknowingly convey personal interpretations of stories that could be either positive or negative (i.e., disambiguated stories, Halberstadt et al., 2008); or to indicate whether they recognise disambiguated positive or negative sentences that they previously viewed as unvalenced sentences (Wenzlaff, Meier, & Salas, 2002). Generally, reviewers have reported that implicit interpretation research in depression is scarce and the results equivocal (e.g., Gotlib & Joormann, 2010; Wisco, 2009). However, studies that have failed to observe implicit interpretive biases in depression have tended to use ambiguous stimuli that was not self-related (e.g., Lawson & MacLeod, 1999). The specificity of negativity toward the self in depression, as opposed to general or other-related negativity, has been demonstrated empirically (Wisco, 2009). Accordingly, most interpretation studies that have employed self-referential stimuli have found reliable and strong negative biases amongst depressed individuals. For example, high scores on the SST have discriminated depressed and formerly depressed groups from never depressed groups in cross-

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sectional studies (Hedlund & Rude, 1995; Rude, Covich, Jarrold, Hedlund, & Zentner, 2001), and prospectively predicted subsequent depressive symptoms (Rude, Valdez, Odom, & Ebrahimi, 2003) and depression diagnosis (Rude, Wenzlaff, Gibbs, Vane, & Whitney, 2002) of undergraduates. Pessimistic personal future-event beliefs have also been reliably observed in depression, in the form of a surplus of expectancies for negative events and/or a shortage of positive expectancies (e.g., Lavender & Watkins, 2004; MacLeod & Salaminiou, 2001). These beliefs may become automated through habitual thinking styles (Andersen & Limpert, 2001; Andersen, Spielman, & Bargh, 1992). Specifically, well rehearsed ruminative thought patterns may create associative structures that facilitate the effortless production of negative predictions and an unwavering certainty of their inevitability. In contrast, non-depressed individuals make more positive and realistically uncertain future predictions. Future-event schemas have been assessed by tasks involving time pressure. For example, the Personal-Future Task (MacLeod, Tata, et al., 1997) requires participants to envisage positive and/or negative experiences that may happen to them in the future, and to generate as many as possible within a brief time (e.g., 30 s). Response latencies to identify examples, or predict the likelihood, of specific future events have also been utilized (Andersen et al., 1992; MacLeod & Cropley, 1995). 1.1.4. Self-esteem Explicit and implicit self-esteem are believed to represent two distinct self-attitudes. Whereas explicit self-esteem comprises consciously-held attitudes toward the self, implicit self-esteem is posited to reflect automatic associations between self-concept and positivity or negativity that developed during early experiential learning (Koole, Dijksterhuis & van Knippenberg, 2001; Rudman, 2004). Of particular relevance to depression, implicit self-esteem has predicted selfreported levels of daily negative affect over and above the influence of explicit self-esteem (Conner & Feldman Barrett, 2005). Implicit self-esteem is often assessed by the Implicit Association Test (Self-worth IAT; Greenwald & Farnham, 2000). The Self-worth IAT measures the relative strength of associations between four stimulus categories: “me” (e.g., own name), “not-me” (e.g., other name), “negative” (e.g., sad), and “positive” (e.g., happy). Words from the four categories are presented individually on a computer screen, and participants assign each item to its target category. In one condition, pressing one key categorizes “me” and “positive” items and another key classifies “not-me” and “negative” items. In a comparison condition, one key is pressed for “me” and “negative” pairings and the other key classifies “not-me” and “positive” attributes. Responses are faster when the pairings of target and attribute items match an individual's automatic associations. Consequently, negative implicit self-esteem is indicated by shorter response latencies to categorize “me” and “negative” than “me” and “positive” attributes. The Extrinsic Affective Simon Task (De Houwer, 2003) involves the classification of colored adjectives. In self-esteem studies, white descriptive adjectives are categorised according to their valence (e.g., “L” if positive and “S” if negative) and colored words relating to self or other are classified according to their color (e.g., “L” if blue and “S” if green). The EAST is based on the assumption that performance is facilitated when items associated in memory share a response key. However, evidence suggests that this facilitative effect is moderated by affective state (Vermeulen, Corneille, & Luminet, 2007). Specifically, the EAST effect appears to be positively correlated with positive affective states and negatively correlated with negative affective states. Consequently the EAST may lack validity when used to assess the attitudes of depressed individuals. Compared to the IAT and EAST, the Self-Descriptiveness Judgement Task (SDJT) provides a relatively simple RT estimate of implicit self-esteem. Participants are asked to indicate whether trait adjectives displayed on a screen describe their personality by pressing a “yes” or

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“no” key. Lower self-esteem is indicated by shorter latencies to make “yes” judgements to negative attributes and “no” judgements to positive attributes. An alternative mode of implicit self-esteem estimation is offered by the Name Letter Preference Task (NLPT; Nuttin, 1985). The NLPT is based on the finding that people with high self-esteem tend to prefer the letters in their own name more than people with low self-esteem like their name-letters (Nuttin, 1987). Preference for initials can be considered an implicit index of self-esteem because people are generally unaware that they possess or display such a preference (Greenwald & Banaji, 1995; Koole et al., 2001). In the task, each letter of the alphabet is briefly presented in the centre of a computer screen and participants are required to rate their immediate response to the letter (e.g., not at all attractive to very attractive). Lower self-ratings for one's own name-letters (or initials) compared to others' ratings of those letters indicates negative self-esteem. Investigations into the relationship between depression and implicit self-esteem are relatively recent and have not yet been evaluated by literature reviewers. As predicted by cognitive theories of depression, several cross-sectional studies have identified low implicit self-esteem amongst depressed participants (e.g., Franck, De Raedt, Dereu, & Van den Abbeele, 2007; Segal, Truchon, Gemar, Guirguis, & Horowitz, 1995). For example, Gilboa, Roberts and Gotlib (1997) found that dysphoric undergraduate participants were faster to affirm negative self-descriptors and slower to reject positive selfdescriptors on the SDJT than non-dysphoric participants; including those who were experiencing an experimentally-induced sad mood that was equivalent to that of dysphoric participants. Low scores on the Self-worth IAT have also predicted subsequent levels of depressive symptoms of undergraduates (Haeffel et al., 2007). However, the results of other studies have led some researchers to suggest that depression is associated with paradoxically positive implicit selfesteem (De Raedt, Schacht, Franck, & De Houwer, 2006). De Raedt et al. (2006) found that clinically depressed participants' scores on the IAT and NLPT revealed similarly positive self-esteem to controls, and their scores on the EAST indicated significantly higher self-esteem than controls.

1.2. Study design Most research on depression-related cognitions has involved cross-sectional designs to identify differences between depressed and non-depressed individuals (Abramson et al., 2002; Ingram et al., 1998; Scher et al., 2005). However, cross-sectional designs cannot fully address vulnerability assumptions of cognitive models of depression. To provide empirical support for a hypothesized cognitive vulnerability, a study design should also demonstrate that the cognition temporally precedes the initial onset or recurrence of depression (Ingram et al., 1998) and is not a temporary consequence or symptom of the disorder (Riskind & Alloy, 2006). In this respect, designs that compare formerly- and non-depressed groups (i.e., Remitted designs) are more informative because they can establish independence of vulnerabilities from symptoms. But, like cross-sectional studies, remitted designs cannot determine whether scores on cognitive measures reflect consequences or scars of depression, rather than risk factors or causes (Lewinsohn, Steinmetz, Larson, & Franklin, 1981). Prospective designs that measure hypothesized cognitive vulnerability factors prior to depression onset are often considered most suitable for assessing vulnerability hypotheses because they can establish both temporal precedence and independence from symptoms. To date, prospective research on implicit cognition in depression is sparse. Some high-risk designs perform the same function: These designs assess whether hypothesised vulnerability factors (e.g., familial, social or cognitive) predict subsequent depression.

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If negative self-referential implicit cognitions represent vulnerability factors for depression, they should (theoretically) predict past, current and future depression. If so, this meta-analysis should find significant relationships between implicit cognitions and depression, on average, in studies that have used remitted, cross-sectional and prospective designs. Another methodological issue surrounds the validity of research using undergraduate samples, which reflects ongoing debate about whether depression operates in a categorical or dimensional fashion (Ruscio, Brown, & Ruscio, 2009; Ruscio & Ruscio, 2000). From a dimensional perspective, depression occurs on a continuum and schematic processing is a normative process that differs only in valence and intensity between individuals. From a categorical viewpoint, clinically depressed individuals possess qualitatively different cognitions from non-depressed individuals. In relation to this meta-analysis, the categorical perspective suggests that different effects will be found in clinical compared to non-clinical studies whereas the dimensional account predicts that similar effects will be observed in all sample types. 1.3. Cognitive reactivity and control Cognitive theories of depression are essentially diathesis-stress models, in which negative self-referential biases are posited to remain dormant until activated by relevant environmental cues, such as stress or sad mood (e.g., Beck, Rush, Shaw, & Emery, 1979; Beevers, 2005). Cognitive reactivity refers to the relative ease with which maladaptive cognitions are activated in vulnerable individuals. Consequently, researchers have utilized priming procedures (e.g., sad mood inductions) to activate and identify latent biases. Supporting theoretical predictions, several studies have revealed negative self-schemas amongst formerly depressed individuals after, but not before, a sad mood induction (Scher et al., 2005). From a dual process perspective, a sad mood induction may expose latent self-schema by increasing maladaptive bottom-up processing or by undermining regulatory functions of top-down processing (Hartlage et al., 1993). Vulnerable individuals may successfully override implicit negative biases when sufficient cognitive resources are available, but their ability to self-regulate may be impaired when cognitive resources are depleted (e.g., by life stress). For example, evidence suggests that the dispositional tendency to suppress unwanted thoughts may perform a protective function at low levels of life stress, but may present an increased risk for depression as life stress increases (Beevers & Meyer, 2004). Consequently, in order to uncover negative implicit cognitions, researchers have employed dual-task (e.g., cognitive load) procedures to deplete participants' cognitive resources and disrupt their mental control efforts during task completion. For example, participants may be required to memorise and retain a six digit number while simultaneously completing the task of interest. According to schema and dual process models of depression, the relationship between negative implicit self-referential cognitions and depression should be strongest, on average, under conditions that maximize the potential activation of implicit processing and/or inhibit the potential for corrective explicit processing. One of the aims of the current meta-analysis is to assess the validity of this premise. 1.4. Rationale for this meta-analysis According to most cognitive theories (e.g., Beck, 1967; Beevers, 2005), implicit cognitive biases resulting from activated negative self-schemas should be evident across all cognitive domains (e.g., attention or memory) and measurement paradigms. However, literature reviewers have indicated tenuous support for this prediction, by noting apparently inconsistent relationships between depression and implicit biases both within (e.g., Barry et al., 2004;

Mogg & Bradley, 2005) and across (e.g., Joormann, 2009; Wisco, 2009) cognitive domains. Existing narrative reviews have not quantified the predictive ability of implicit cognition across studies. Thus, their conclusions reflect a preponderant weighing of results from studies that used different methodologies, populations, and researchers. Consequently, differences between studies and across cognitive domains may reflect superficial methodological aspects rather than the presence or absence of the bias of interest. Additionally, these reviews relied on statistical significance tests where results were reported as either supporting or failing to support a hypothesized relationship. However, statistical significance is influenced by sample size and does not indicate the strength or magnitude of observed effects. Although meta-analytic studies take sample size into account and can provide effect size estimates, only one previous meta-analysis has assessed implicit depression-related cognition; specifically, attention biases observed in a subsample of Lexical Decision Task studies (Siegle, 1996). Several other meta-analyses have focussed on related issues such as explicit biases (e.g., Matt et al., 1992) and emotional reactivity (Blysma, Morris, & Rottenberg, 2008) in depression. The current meta-analysis reviewed empirical studies that have assessed the relationship between negatively biased self-referential implicit cognitions and depression. We aimed to determine: (1) whether a reliable relationship exists between negative self-referential implicit cognition and depression, and (2) if that relationship is moderated by four factors: facet of cognition, reactivity and control, sample type, and measurement strategies. We also aimed to assess whether priming thresholds moderate the relationship between attentional bias and depression, and if level of processing at encoding and retrieval moderates effect sizes across implicit memory studies. Results of these analyses may elucidate the nature of the relationship between implicit cognitive biases and depression. From a theoretical perspective, these results will indicate the degree of support for dual-process and diathesis-stress models of depression. Specifically, these models will be supported if: 1) all facets of cognition are equally strong predictors of depression, 2) implicit negative biases predict depression in cross-sectional, remitted and prospective studies, and 3) experimental cognitive manipulations strengthen the relationship between implicit biases and depression. From a methodological perspective, these results may assist future researchers and practitioners to utilize appropriate samples and reliable measures. From an applied perspective, these results may provide information to guide the development of more effective treatments. 2. Method 2.1. Literature search Studies were primarily sourced from the PsychINFO and PubMed databases using two categories of keywords. The first category of search terms included depression, depressive, depressed, dysphoria and dysphoric. Search terms in the second category related to implicit measures and cognitive biases: implicit, automatic, indirect, schema, attention, attentional bias, memory, memory bias, interpretation, interpretive bias, beliefs, self-esteem, self, future, cognitive bias, dot probe, visual probe, dichotic listening, Stroop, word stem, word fragment, word completion, homophones, lexical decision, priming, scrambled sentences, Implicit Association Test, name letter preference, Extrinsic Affective Simon, and Go/No Go. The search was limited to contemporary methodologies reported in articles published from 1984 to the present. We limited the literature search to 25 years to keep the scope of the study manageable and because of difficulties acquiring data for older studies. Articles that contained one search term from each category were sourced for potential inclusion, and their references were checked to identify additional relevant studies.

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

2.2. Inclusion criteria To be included, studies were required to involve an assessment of an implicit cognitive bias in attention, memory, self-esteem, interpretation or self-beliefs and a measure of naturally-occurring depressive symptoms and/or clinical status. 2.2.1. Stimuli In accordance with cognitive theories, studies were required to utilize self-referential experimental stimuli, including affective state or trait self-descriptors, perceptions of self-worth, and beliefs about one's past, present and future life. Accordingly, studies that used facial, physical, or general threat stimuli were excluded (e.g., horror and accident). We also excluded neuro-imaging and neurophysiological studies to confine this study within manageable boundaries. 2.2.2. Samples and depressive status While no age-groups were deliberately excluded, all retained studies used adult samples except for one (Mean age = 15.37; Dalgleish et al., 2003). To be included, studies were required to involve samples that included individuals who exhibited or reported at least mild levels of current or previous depression (e.g., BDI-II≥ 14, Beck, Steer, & Brown, 1996) as well as individuals who reported low levels of depressive symptoms. Mildly depressed groups were included because around 50% of depression research has involved undergraduate samples that rarely comprise severely depressed individuals. Comparisons with other clinical groups were excluded. 2.2.3. Implicitness De Houwer and colleagues (De Houwer & Moors, 2007; De Houwer, Tiege-Mocigemba, Spruyt, & Moors, 2009) defined implicit measures as measurement outcomes produced by psychological attributes via automatic processes; where automatic processes are processes that can operate under uncontrolled, goal independent, unconscious, efficient or fast conditions. A measure may be considered uncontrolled if the attribute still causes the measurement effect even when participants intend to prevent, alter, or avoid expressing the attribute via the measure. A process is goal-independent when its occurrence does not causally depend on participants adopting any type of methodological goal. Unconscious measures assess an attribute when participants are unaware of the activating stimuli, the attribute itself, or that (or how) the attribute influences performance. Efficient measures consume little or no processing resources or attentional capacity, as evidenced by an effect when resources are restricted by time or cognitive load. Finally, processes are fast if the effect occurs when stimulus input and/or process duration are relatively brief (as determined by the specific task). For a comprehensive account, see Moors, De Houwer and colleagues (De Houwer & Moors, 2010; Moors & De Houwer, 2006; Moors, Spruyt, & De Houwer, 2010). From this decompositional view, implicit measures can be implicit in different ways and individual automaticity features may not necessarily co-exist within a particular measure (De Houwer et al., 2009). For example, in relation to the IAT, participants have minimal control over the measurement outcome (e.g., Steffens, 2004) but may be aware of the attribute being assessed (e.g., Monteith, Voils, & Ashburn-Nardo, 2001). Thus, an IAT can be considered implicit in the sense of uncontrolled but not unconscious (De Houwer, 2009). Conversely, the goal of the SST is relatively transparent and responses may be controlled, but it is efficient because its effect increases when completed under cognitive load (Wenzlaff & Bates, 1998). Studies selected for this meta-analysis used measures that were judged to possess at least one automatic feature, as indicated by empirical evidence or deduction (Moors et al., 2010). 2.2.4. Statistics Appropriate statistics (e.g., means and standard deviations) were required to be reported in the article or available from the author upon

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request. Several studies were excluded because this information could not be obtained.1 Three studies were excluded because their datasets were utilized in subsequent studies that were included in this metaanalysis (i.e., Neshat-Doost, Taghavi, Moradi, Yule, & Dalgleish, 1997; Neshat-Doost, Moradi, Taghavi, Yule, & Dalgleish, 2000; Rude et al., 2003). When a study reported a non-significant result but did not provide sufficient information to determine an effect size, we entered zero as the effect size (see Table 1). Two hundred and two effect sizes were acquired from 89 samples, resulting in a pooled sample size of 7032 with a mean age of 32. See Table 1 for a summary of included studies. 2.3. Moderator coding Effect sizes were coded by four potential primary moderators: 2.3.1. Aspect of cognition Effect sizes were classified into four groups according to the facet of cognition they assessed: attention, memory, interpretation/beliefs, and self-esteem. Effect sizes were calculated to represent observed relationships between depression and negative cognitive biases; where positive effect sizes represented relationships between higher levels of depression and larger attentional biases toward negative stimuli, greater memory for negative stimuli, lower self-esteem, or higher levels of negative interpretive biases and self-beliefs. Following theory and convention, negative biases for negative and positive stimuli were included in the memory and interpretation/beliefs categories if both statistics were available. Efforts were made to extract statistics that assessed conceptually equivalent constructs within each facet of cognition. When a study provided separate statistics for positive and negative components of a self-esteem measure, we averaged the two resulting effect sizes to estimate the measure's typical single measurement outcome (see Table 1). For all studies, when ideal figures were not available and alternative calculation methods could be applied (e.g., frequencies or chi square), we chose the method that produced the lower effect size estimate. 2.3.2. Cognitive reactivity and control Studies were classified into four categories according to their use of conditions that manipulated participants' cognitive reactivity and control: none, sad mood induction, cognitive load, and sad mood induction and cognitive load. Rumination and self-focus manipulations were classified as mood inductions because evidence suggests that rumination inductions lower participants' moods (Lavender & Watkins, 2004; Nolen-Hoeksema & Morrow, 1993) and self-focus inductions enhance accessibility of self-related constructs in longterm memory (Hedlund & Rude, 1995). 2.3.3. Sample type Categories of samples included clinical, undergraduate, and community. 2.3.4. Measurement strategy To increase power, some tasks were grouped (as indicated) according to methodological similarities. Effect sizes within these groups were homogenous. Twenty-three measurement strategy categories were coded: Deployment of Attention Task, dichotic listening, Extrinsic Affective Simon Task, Exogenous Cuing Task, negative affective priming/Sternberg Task, probe tasks (dot probe and visual probe), Stroop, valence identification, visual search, Imbedded Word Task, Implicit Association test, Name Letter Preference Task, Self-Descriptiveness Judgement Task, Breadth-based Adjective Rating Task, Lexical Decision Task, word completion (word stems and word 1

A list of excluded studies and conditions is available from the authors.

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Table 1 Summary of included studies. N

Sample type

M age

Study design

Aspect of cognition

Andersen and Limpert (2001) Andersen and Limpert (2001) Andersen et al. (1992) Andersen et al. (1992)b Andersen et al. (1992)be Andersen et al. (1992)e Baños, Medina and Pascual (2001)

37 37 53 32 32 53 40

Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Clinical

19 19 – – – – –

Cross Cross Cross Cross Cross Cross Cross

Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Memory (neg)

Baños et al. (2001)

40

Clinical

–

Cross sectional

Memory (pos)

41 41 26 26 30 53 37 37 37 37 39

Undergraduate Undergraduate Undergraduate Undergraduate Clinical Undergraduate Clinical Clinical Clinical Clinical Clinical

21 21 19 19 39 – 37 37 37 37 73

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional

e

sectional sectional sectional sectional sectional sectional sectional

Measurement strategy

Priming and processing

Cognitive reactivity and control

Implicitness of measure

– – – – – – –

Cog Load Cog Load Cog Load Cog Load Cog Load Cog Load None

EF EF EF EF EF EF –

0.40 0.10 0.29 0.35 0.00 0.00 0.19

0.17 0.17 0.14 0.19 0.19 0.14 0.16

–

None

–

0.08

0.16

Attention Attention Memory (neg) Memory (neg) Memory (neg) Memory (neg) Memory (neg) Memory (pos) Memory (neg) Memory (pos) Attention

Personal-future Personal-future Yes/no future Yes/no future Yes/no future Yes/no future Word stems (combined encoding conditions) Word stems (combined encoding conditions) Dot probe (14 ms) Dot probe (1000 ms) Lexical decision (28 ms) Lexical decision (7 s) Lexical decision (28 ms & 7 s) Lexical decision (28 ms) Lexical decision (7 s) Lexical decision (7 s) Lexical decision (14 ms) Lexical decision (14 ms) Emotional Stroop

≤400 ms ≥500 ms P/P P/P P/P P/P P/P P/P P/P P/P ≥500 ms

None None None None None None None None None None None

UCF UCF UGCEF UGCE UGCE UGCEF UGCE UGCE UGCEF UGCEF UCEF

0.00 0.32 0.38 − 0.04 0.34 0.28 0.33 − 0.06 0.24 0.04 0.32

0.16 0.16 0.21 0.21 0.19 0.14 0.17 0.17 0.17 0.17 0.17

(pos) (neg) (neg) (neg) (pos) (pos)

r

SE

Bradley et al. (1997) Study 2 Bradley et al. (1997) Study 2 Bradley et al. (1996) Experiment 1 Bradley et al. (1996) Experiment 1 Bradley et al. (1996) Experiment 2 Bradley et al. (1994) Bradley, Mogg and Williams (1995) Bradley et al. (1995) Bradley et al. (1995) Bradley et al. (1995) Broomfield, Davies, MacMahon, Ali and Cross (2007) Dalgleish et al. (2003) Dalgleish et al. (2003) Danion et al. (1995) Danion et al. (1995) Denny and Hunt (1992)f De Raedt et al. (2006) Study 1a De Raedt et al. (2006) Study 2 De Raedt et al. (2006) Study 3 Dudley, O'Brien, Barnett, McGuckin and Britton (2002) Ellwart et al. (2003) Ellwart et al. (2003) Franck, De Raedt, Dereu, et al. (2007) Franck, De Raedt, Dereu, et al. (2007) Franck, De Raedt and De Houwer (2007) Franck, De Raedt and De Houwer (2007) Franck, De Raedt and De Houwer (2007) Garlipp (2004)a Gemar et al. (2001)a Gemar et al. (2001)a Gilboa et al. (1997) Naturally Dysphoric Gilboa et al. (1997) Naturally Dysphoric Gotlib et al. (2004)

45 45 60 60 32 30 32 26 24

Clinical Clinical Clinical Clinical Clinical (women) Clinical Clinical Clinical Clinical

15 15 41 41 27 44 39 39 74

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional

Attention Attention Memory (neg) Memory (pos) Memory (neg) Self-esteem Self-esteem Self-esteem Attention

Emotional Stroop Dot probe Word stems Word stems Word fragments IAT NLPT EAST Emotional Stroop

≥500 ms ≥500 ms C/P C/P C/P – – – ≥500 ms

None None None None None None None None None

UCEF UCF CE CE CE UEF UGCEF UEF UCEF

0.00 − 0.04 − 0.05 − 0.08 0.04 − 0.01 0.14 − 0.41 0.42

0.15 0.15 0.13 0.13 0.19 0.19 0.19 0.21 0.22

72 72 29 30 61 67 40 30 50 50 76 76 143

Clinical Clinical Clinical Clinical (suicidal) Clinical Clinical Clinical Undergraduate Clinical Clinical Undergraduate Undergraduate Clinical

42 42 43 41 42 45 – 41 38 38 – – 34

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Remitted Prospective Cross sectional Remitted Remitted Cross sectional Cross sectional Cross sectional

Memory (neg) Memory (pos) Self-esteem Self-esteem Self-esteem Self-esteem Self-esteem Self-esteem Self-esteem Self-esteem Self-esteem Self-esteem Attention

C/C C/C – – – – – – – – – – ≥500 ms

None None None None None None None None None Mood None None None

CEF CEF UEF UEF UGCEF UGCEF UGCEF UEF UEF UEF UEF UEF UCEF

0.17 0.08 0.59 0.14 0.04 0.00 − 0.21 0.26 − 0.10 0.07 0.59 0.55 − 0.05

0.12 0.12 0.16 0.13 0.13 0.20 0.19 0.19 0.15 0.15 0.12 0.12 0.09

Gotlib et al. (2004)

143

Clinical

34

Cross sectional

Attention

≥500 ms

None

UCEF

0.09

0.09

Gotlib, McLachlan and Katz (1988) Gotlib et al. (2005) Gotlib et al. (2005) Greenberg and Alloy (1989) Haeffel et al. (2007) Study 1 Haeffel et al. (2007) Study 2

24 36 36 24 237 251

Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate

32 – – – 19 19

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional

Attention Attention Attention Self-esteem Self-esteem Self-esteem

Anagrams Anagrams IAT IAT NLPT NLPT NLPT IAT IAT IAT SDJT (RT affirm) SDJT (RT reject) Emotional Stroop (sad word condition) Emotional Stroop (social threat condition) DOAT (730 ms) NAP (interference, 100 ms) NAP (inhibition, 100 ms) SDJT (RT only) IAT IAT

≥500 ms ≤400 ms ≤400 ms – – –

None None None None None None

UCEF GCEF GCEF EF UEF UEF

0.36 0.47 0.35 0.32 0.07 0.08

0.22 0.17 0.17 0.22 0.06 0.06

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

Author

Prospective Cross sectional Cross sectional

Self-esteem Interpretation/beliefs (neg) Interpretation/beliefs (neg)

IAT Disambiguated stories Disambiguated stories

– – –

None None None

UEF CE CE

0.20 0.24 0.07

0.07 0.03 0.03

– –

Cross sectional Cross sectional

Interpretation/beliefs (pos) Interpretation/beliefs (pos)

Disambiguated stories Disambiguated stories

– –

None None

CE CE

0.33 0.05

0.03 0.03

– – 33 31 33 31 – 46 46 – – 20 20 25 25 28 28 36

Cross sectional Cross sectional Cross sectional Remitted Cross sectional Remitted Cross sectional Cross sectional Cross sectional Remitted Remitted Remitted Remitted Cross sectional Cross sectional Remitted Remitted Cross sectional

Interpretation/beliefs (pos) Interpretation/beliefs (neg) Interpretation/beliefs Interpretation/beliefs Attention Attention Attention Memory (neg) Memory (pos) Attention Attention Attention Attention Attention Attention Attention Attention Attention

Disambiguated stories Disambiguated stories SST SST Emotional Stroop Emotional Stroop Emotional Stroop Word stems Word stems Dichotic listening Dichotic listening Dichotic listening Dichotic listening NAP NAP NAP (self reference) NAP (evaluation) Sternberg Task

– – – – ≥500 ms ≥500 ms ≥500 ms C/P C/P – – – – ≥500 ms ≥500 ms ≥500 ms ≥500 ms ≥500 ms

None None Mood Mood Mood Mood None None None None Mood None Mood None None None None None

CE CE EF EF UCEF UCEF UCEF CE CE CF CF CF CF GCEF GCEF GCEF GCEF UCEF

0.29 0.18 0.57 0.12 0.00 0.00 0.00 − 0.07 − 0.13 0.00 0.49 0.03 0.12 0.26 0.29 0.34 0.44 0.34

0.07 0.07 0.17 0.18 0.17 0.18 0.22 0.19 0.19 0.17 0.18 0.11 0.11 0.16 0.12 0.16 0.21 0.21

Undergraduate

–

Cross sectional

Self-esteem

IAT (self-other)

–

None

UEF

− 0.01

0.09

134

Undergraduate

–

Cross sectional

Self-esteem

–

None

C

0.27

0.09

Kinderman (1994)a Koster et al. (2005) Experiment 1

32 57

Clinical Undergraduate

33 19

Cross sectional Cross sectional

Self-esteem Attention

– ≥500 ms

None None

UCEF CF

0.11 0.35

0.19 0.14

Koster et al. (2005) Experiment 1

57

Undergraduate

19

Cross sectional

Attention

≥500 ms

None

CF

0.12

0.14

Koster et al. (2005) Experiment 2

40

Undergraduate

22

Cross sectional

Attention

≤400 ms

None

CF

0.07

0.16

Koster et al. (2005) Experiment 2

40

Undergraduate

22

Cross sectional

Attention

≤400 ms

None

CF

0.10

0.16

Koster et al. (2005) Experiment 2

40

Undergraduate

22

Cross sectional

Attention

≥500 ms

None

CF

0.38

0.16

Koster et al. (2005) Experiment 2

40

Undergraduate

22

Cross sectional

Attention

≥500 ms

None

CF

0.00

0.16

Koster et al. (2005) Experiment 2

40

Undergraduate

22

Cross sectional

Attention

≥500 ms

None

CF

0.30

0.16

Koster et al. (2005) Experiment 2

40

Undergraduate

22

Cross sectional

Attention

≥500 ms

None

CF

0.32

0.16

Koster et al. (2005) Experiment 2

40

Undergraduate

22

Cross sectional

Attention

≥500 ms

None

CF

0.24

0.16

Koster et al. (2005) Experiment 2

40

Undergraduate

22

Cross sectional

Attention

≥500 ms

None

CF

0.36

0.16

Lavender and Watkins (2004) Rumination condition Lavender and Watkins (2004) Rumination condition Lim and Kim (2005) Negative words condition Lim and Kim (2005) Negative words condition

45

Clinical

40

Cross sectional

Interpretation/beliefs (pos)

Breadth-based Adjective Rating Task Stroop (Self Worth) Exogenous cuing (1500 ms, disengagement) Exogenous cuing (1500 ms, engagement) Exogenous cuing (250 ms, disengagement) Exogenous cuing (250 ms, engagement) Exogenous cuing (500 ms, disengagement) Exogenous cuing (500 ms, engagement) Exogenous cuing (500 ms, cue validity) Exogenous cuing (1500 ms, disengagement) Exogenous cuing (1500 ms, engagement) Exogenous cuing (1500 ms, cue validity) Personal-future

–

Mood

EF

0.33

0.15

45

Clinical

40

Cross sectional

Interpretation/beliefs (neg)

Personal-future

–

Mood

EF

0.38

0.15

63

Clinical

35

Cross sectional

Attention

Stroop (subliminal)

≤400 ms

None

UCEF

0.08

0.13

63

Clinical

35

Cross sectional

Attention

Stroop (supraliminal)

≥500 ms

None

UCEF

0.35

0.13

251 1006 1111

Halberstadt et al. (2008) Halberstadt et al. (2008)

1006 1111

Halberstadt et al. (2008)b Halberstadt et al. (2008)b Hedlund and Rude (1995) Hedlund and Rude (1995) Hedlund and Rude (1995)e Hedlund and Rude (1995)e Hill and Knowles (1991)e Ilsley et al. (1995) Ilsley et al. (1995) Ingram and Ritter (2000) Ingram and Ritter (2000) Ingram et al. (1994)d Ingram et al. (1994)d Joormann (2004) Study 1 Joormann (2004)Study 2 Joormann (2004)Study 3 Joormann (2004)Study 3 Joormann and Gotlib (2008) No sad induction control Karpinski, Steinberg, Versek and Alloy (2007) Study 2 Karpinski et al. (2007) Study 2

229 229 38 33 38 33 24 30 30 34 39 89 89 74 44 26 26 44

Undergraduate Undergraduate Undergraduate (dysphoric) Undergraduate Undergraduate (dysphoric) Undergraduate Undergraduate Clinical Clinical Clinical Clinical – Clinical Clinical Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Clinical

134

699

(continued on next page)

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

19 – –

Haeffel et al. (2007) Study 2 Halberstadt et al. (2008) Halberstadt et al. (2008)

700

Table 1 (continued) Author

Sample type

M age

Study design

Aspect of cognition

Measurement strategy

Priming and processing

Cognitive reactivity and control

Implicitness of measure

Lim and Kim (2005)c

56

Clinical

35

Cross sectional

Memory (neg)

None

UCEF

− 0.01

0.14

Lim and Kim (2005)

56

Clinical

35

Cross sectional

Memory (pos)

P/P

None

UCEF

0.00

0.14

Lim and Kim (2005)

c

56

Clinical

35

Cross sectional

Memory (neg)

P/P

None

UCEF

− 0.01

0.14

Lim and Kim (2005)c

56

Clinical

35

Cross sectional

Memory (pos)

P/P

None

UCEF

0.00

0.14

MacLeod and Cropley (1995) MacLeod and Cropley (1995) MacLeod and Salaminiou (2001) MacLeod, Pankhania, Lee and Mitchell (1997) MacLeod, Pankhania, et al. (1997) MacLeod, Tata, et al. (1997)df MacLeod, Tata, et al. (1997)df McCabe and Gotlib (1995) McCabe and Gotlib (1995) McCabe and Toman (2000) McCabe et al. (2000)d Trait words McCabe et al. (2000)d State words McCabe et al. (2000)d Trait words McCabe et al. (2000)d State words McNeely et al. (2008)e Meites et al. (2008) Meites et al. (2008) Meites et al. (2008) Meites et al. (2008) Mogg et al. (1995)i Mogg et al. (1995)i Phillips (2009) Phillips (2009) Phillips (2009) Phillips (2009) Phillips (2009) Phillips (2009) Phillips (2009) Phillips (2009) Rinck and Becker (2005) Rinck and Becker (2005) Rude et al. (2001)h Rude et al. (2001) Rude et al. (2002) Rude et al. (2002) Rude et al. (2002) Rude et al. (2002) Ruiz-Caballero and González (1994) Experiment 1 Ruiz-Caballero and González (1994) Experiment 1 Ruiz-Caballero and González (1994) Experiment 2 Ruiz-Caballero and González (1994) Experiment 2 Ruiz-Caballero and González (1994) Experiment 2

54 54 44 61

Undergraduate Undergraduate Clinical Clinical (suicidal)

19 19 57 35

Cross Cross Cross Cross

Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs

Word identification (subliminal) Word identification (subliminal) Word identification (supraliminal) Word identification (supraliminal) Future generation Future generation Personal-future Personal-future

P/P

c

– – – –

None None None None

EF EF EF EF

0.16 0.17 0.76 0.57

0.14 0.14 0.16 0.13

Clinical (suicidal) Clinical Clinical Clinical (women) Clinical (women) Undergraduate Community (women) Community (women) Community (women) Community (women) Clinical Community Community Community Community Community Community Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Clinical Clinical Community Community Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate

35 40 40 – – – 33 33 34 34 37 27 27 27 27 37 37 31 33 31 33 31 31 33 33 22 22 34 34 18 18 18 18 –

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Remitted Remitted Remitted Remitted Cross sectional Remitted Remitted Remitted Remitted Cross sectional Cross sectional Cross sectional Prospective Cross sectional Prospective Cross sectional Cross sectional Prospective Prospective Cross sectional Cross sectional Remitted Remitted Cross sectional Cross sectional Prospective Prospective Cross sectional

Interpretation/beliefs (neg) Interpretation/beliefs (neg) Memory (neg) Attention Attention Attention Attention Attention Attention Attention Attention Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Attention Attention Interpretation/beliefs Interpretation/beliefs Self-esteem Self-esteem Memory (neg) Memory (pos) Memory (neg) Memory (pos) Attention Memory (neg) Interpretation/beliefs Memory Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Memory (neg)

Personal-future Personal-future Autobiographical memory DOAT (750 ms) DOAT (750 ms) DOAT (3 durations) DOAT (750 ms) DOAT (750 ms) DOAT (750 ms) DOAT (750 ms) Emotional Stroop (400 ms) IAT (depression) IAT (depression) IAT (hopelessness) IAT (hopelessness) Dot Probe (14 ms) Dot Probe (1000 ms) SST SST NLPT NLPT Word stems Word stems Word stems Word stems Visual search Anagrams SST Word intrusions SST SST SST SST Word stems

– – – ≥ 500 ms ≥ 500 ms – ≥ 500 ms ≥ 500 ms ≥ 500 ms ≥ 500 ms ≤ 400 ms – – – – ≤ 400 ms ≥ 500 ms – – – – P/P P/P P/P P/P ≥ 500 ms C/C – – – – – – P/P

None None None None None None None None Mood Mood None None Mood None Mood None None Mood & Cog Load Mood & Cog Load Mood Mood Mood Mood Mood Mood None None Mood Mood None Cog Load None Cog Load None

EF EF EF UCF UCF UCF UCF UCF UCF UCF UCEF UEF UEF UEF UEF UGCF UCF EF EF UGCEF UGCEF CE CE CE CE CEF CEF EF UCE EF EF EF EF E

0.26 0.08 0.16 0.40 0.49 0.31 − 0.04 0.07 0.07 0.00 0.00 0.53 0.23 0.20 0.20 0.14 0.47 0.52 0.44 0.10 0.16 0.17 0.10 0.19 0.11 0.23 0.22 0.39 0.33 0.55 0.51 0.46 0.48 0.21

0.13 0.18 0.18 0.16 0.16 0.13 0.16 0.16 0.16 0.16 0.20 0.16 0.16 0.16 0.16 0.19 0.19 0.06 0.08 0.06 0.08 0.06 0.06 0.08 0.08 0.11 0.11 0.19 0.16 0.06 0.06 0.06 0.06 0.16

40

Undergraduate

–

Cross sectional

Memory (pos)

Word stems

P/P

None

E

0.33

0.16

26

Undergraduate

–

Cross sectional

Memory (neg)

P/P

None

CE

0.43

0.21

26

Undergraduate

–

Cross sectional

Memory (pos)

P/P

None

CE

0.25

0.21

26

Undergraduate

–

Cross sectional

Memory (neg)

Word stems (incidental encoding) Word stems (incidental encoding) Word stems (intentional encoding)

C/P

None

E

0.13

0.21

61 33 33 40 40 59 40 40 40 40 29 42 42 42 42 32 32 322 171 322 171 306 306 160 160 82 82 31 41 339 339 339 339 40

sectional sectional sectional sectional

(neg) (pos) (pos) (pos)

r

SE

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

N

26

Undergraduate

–

Cross sectional

Memory (pos)

28 28 28 28 42

Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate

– – – – 19

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional

Memory Memory Memory Memory Memory

42

Undergraduate

19

Cross sectional

Memory (pos)

Scott et al. (2001) Experiment 1

42

Undergraduate

19

Cross sectional

Memory (neg)

Scott et al. (2001) Experiment 1

42

Undergraduate

19

Cross sectional

Memory (pos)

Scott et al. (2001) Experiment 2

42

Undergraduate

21

Cross sectional

Memory (neg)

Scott et al. (2001) Experiment 2

42

Undergraduate

21

Cross sectional

Memory (pos)

Scott et al. (2001) Experiment 2

42

Undergraduate

21

Cross sectional

Memory (neg)

Scott et al. (2001) Experiment 2

42

Undergraduate

21

Cross sectional

Memory (pos)

Clinical

30

Cross sectional

Self-esteem

Shane and Peterson (2007) Study 2

66

Undergraduate

20

Cross sectional

Attention

Sheppes, Meiran, Gilboa-Schechtman and Shahar (2008) Sheppes et al. (2008)a Siegle et al. (2002) Siegle et al. (2002) ⁎Smallwood (2004) Smallwood (2004) Tarsia, Power and Sanavio (2003)c Tarsia et al. (2003)c Taylor and John (2004) Taylor and John (2004) Van der Does (2005) No valence condition Van der Does (2005) No valence condition Watkins and Moulds (2007) Comparison 1 Watkins and Moulds (2007) Comparison 2 Watkins and Moulds (2007) Comparison 3 Watkins and Moulds (2007) Comparison 4 Watkins et al. (1992)di Watkins et al. (1992)di Watkins et al. (1996) Watkins et al. (1996) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000)

63

Undergraduate

–

Cross sectional

Self-esteem

Word stems (intentional encoding) Word stems Word stems Word stems Word stems Lexical decision (repetition priming, 56 ms) Lexical decision (repetition priming, 56 ms) Lexical decision (semantic priming, 56 ms) Lexical decision (semantic priming, 56 ms) Lexical decision (semantic priming, 56 ms) Lexical decision (semantic priming, 56 ms) Lexical decision (semantic priming, 2 s) Lexical decision (semantic priming, 2 s) Modified Stroop (unmasked, 2 s) Dot Probe (combined durations) IAT (mental set operation)

63 76 76 40 40 32

Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Clinical

– – – 24 24 41

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional

Self-esteem Attention Attention Memory (neg) Memory (pos) Memory (neg)

IAT (mental set maintenance) Lexical decision (150 ms) Valence Identification (150 ms) Word fragments Word fragments Word identification

– ≤400 ms ≤400 ms – – C/P

None None None None None None

UCEF UGCEF EF CE CE UCEF

32 24 24 46

Clinical Clinical Clinical Undergraduate

41 43 43 26

Cross sectional Cross sectional Cross sectional Remitted

Memory (pos) Memory (neg) Memory (pos) Interpretation/beliefs

Word identification Word stems Word stems SST

C/P P/P P/P –

None None None Mood

UCEF CE CE EF

46

Undergraduate

26

Remitted

Interpretation/beliefs

SST

–

Mood & Cog Load

EF

0.24

0.15

19

Clinical

39

Remitted

Interpretation/beliefs

SST

–

None

EF

0.31

0.25

21

Clinical

41

Cross sectional

Interpretation/beliefs

SST

–

None

EF

0.88

0.24

21

Clinical

31

Remitted

Interpretation/beliefs

SST

–

Cog Load

EF

0.55

0.24

19

Clinical

37

Cross sectional

Interpretation/beliefs

SST

–

Cog Load

EF

0.63

0.25

Clinical Clinical Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate

– – 19 19 – – – – –

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional

Memory Memory Memory Memory Memory Memory Memory Memory Memory

Word stems Word stems Word association Word association Word stem Word stem Word stem Word stem Word identification

C/P C/P C/C C/C P/P P/P C/P C/P P/P

None None None None None None None None None

CE CE CF CF CE CE CE CE UCEF

0.06 0.04 0.41 0.34 0.26 0.10 0.14 0.10 −0.03

0.18 0.18 0.21 0.21 0.09 0.09 0.09 0.09 0.09

Segal et al. (1995)

102

34 34 26 26 134 134 134 134 134

(neg) (pos) (neg) (pos) (neg)

(neg) (pos) (neg) (pos) (neg) (pos) (neg) (pos) (neg)

C/P

None

E

0.33

0.21

P/P P/P C/P C/P P/P

None None None None None

CE CE CE CE UGCEF

0.39 0.23 0.43 0.34 0.04

0.20 0.20 0.20 0.20 0.16

P/P

None

UGCEF

0.10

0.16

C/P

None

UGCEF

0.22

0.16

C/P

None

UGCEF

0.05

0.16

C/P

None

UGCEF

0.27

0.16

C/P

None

UGCEF

0.11

0.16

C/P

None

UGCE

− 0.12

0.16

C/P

None

UGCE

0.23

0.16

–

None

UCEF

0.26

0.10

–

None

UCF

0.40

0.13

–

None

UEF

0.09

0.13

0.10 0.30 0.29 0.45 0.36 0.05

0.13 0.12 0.12 0.16 0.16 0.19

0.35 0.30 − 0.04 − 0.10

0.19 0.22 0.22 0.15

701

(continued on next page)

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

Ruiz-Caballero and González (1994) Experiment 2 Ruiz-Caballero and González (1997) Ruiz-Caballero and González (1997) Ruiz-Caballero and González (1997) Ruiz-Caballero and González (1997) Scott, Mogg and Bradley (2001) Experiment 1 Scott et al. (2001) Experiment 1

702

Table 1 (continued) Author

Sample type

M age

Study design

Aspect of cognition

Measurement strategy

Priming and processing

Cognitive reactivity and control

Implicitness of measure

134 134 134 134 134 134 134 134 134 134 134 68

Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate

– – – – – – – – – – – 20

Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional Cross sectional

Memory (pos) Memory (neg) Memory (pos) Memory (neg) Memory (pos) Memory (neg) Memory (pos) Memory (neg) Memory (pos) Memory (neg) Memory (pos) Interpretation/beliefs

Word identification Word identification Word identification Word association Word association Word association Word association Word retrieval Word retrieval Word retrieval Word retrieval SST

P/P C/P C/P P/C P/C C/C C/C P/C P/C C/C C/C –

None None None None None None None None None None None None

UCEF UCEF UCEF CF CF CF CF CF CF CF CF EF

68

Undergraduate

20

Cross sectional

Interpretation/beliefs

SST

–

Cog Load

EF

Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Undergraduate Community (worriers) Community (worriers) Undergraduate Undergraduate Undergraduate

20 20 20 20 21 21 21 21 21 21 49 49 – – –

Cross sectional Cross sectional Remitted Remitted Remitted Cross sectional Remitted Cross sectional Cross sectional Remitted Cross sectional Remitted Cross sectional Cross sectional Cross sectional

Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Interpretation/beliefs Attention Attention Attention Attention Interpretation/beliefs Interpretation/beliefs Attention Attention Attention Attention Attention

Homophones Homophones (time pressure) Homophones Homophones (time pressure) Imbedded word task Imbedded word task Imbedded word task Imbedded word task Disambiguated events Disambiguated events Emotional Stroop Emotional Stroop Emotional Stroop Emotional Stroop (subliminal) Emotional Stroop (supraliminal)

– – – – ≥500 ms ≥500 ms ≥500 ms ≥500 ms – – ≥500 ms ≥500 ms ≥500 ms ≤400 ms ≥500 ms

None None None None Cog Load Cog Load None None None None None None None None None

CE CEF CE CEF EF EF EF EF CEF CEF UCEF UCEF UCEF UCEF UCEF

69 69 64 64 111 111 117 117 146 91 31 21 122 72 72

r

SE

0.26 − 0.19 0.10 0.25 − 0.30 − 0.08 0.04 − 0.02 0.02 0.07 0.39 0.29

0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.12

0.49

0.12

0.53 0.68 0.16 0.59 0.03 0.16 0.21 0.21 0.36 0.28 0.52 0.28 0.02 0.14 − 0.06

0.12 0.12 0.13 0.13 0.10 0.10 0.09 0.09 0.08 0.11 0.19 0.24 0.09 0.12 0.12

Notes. DOAT = Deployment of Attention Task, IAT = Implicit Association Test, EAST = Extrinsic Affective Simon Task, NAP = Negative Affective Priming, NLPT = Name Letter Preference Test, SDJT = Self-Descriptiveness Judgement Task, SST = Scrambled Sentences Task, P/P = perceptual encoding and perceptual retrieval, P/C = perceptual encoding and conceptual retrieval, C/P = conceptual encoding and perceptual retrieval, C/C = conceptual encoding and conceptual retrieval, U = Uncontrolled, G = Goal Independent, C = Unconscious, E = Efficient, F = Fast. a Effect size calculated by averaging positive and negative self-esteem indices. b Effect size based on comparison between depressed and dysphoric groups. c Effect size based on chi-square calculation. d Effect size based on frequency calculation. e Effect size of zero entered because a non-significant result was reported but no statistics were available. f Effect size based on comparison between negative and positive stimuli. g Effect size based on partial correlation. h Refers to formerly depressed group with N 4 previous episodes. i Stimuli included depression and anxiety/threat words.

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Watkins et al. (2000) Wenzlaff and Bates (1998)d No valence condition Wenzlaff and Bates (1998)d No valence condition Wenzlaff and Eisenberg (2001) Wenzlaff and Eisenberg (2001) Wenzlaff and Eisenberg (2001) Wenzlaff and Eisenberg (2001) Wenzlaff et al. (2001) Wenzlaff et al. (2001) Wenzlaff et al. (2001) Wenzlaff et al. (2001) Wenzlaff et al. (2002) Wenzlaff et al. (2002) Williams and Nulty (1986) Williams and Nulty (1986) Yovel and Mineka (2004)g Yovel and Mineka (2005) Yovel and Mineka (2005)

N

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

fragments), other priming retrieval tasks (anagrams, word association, word retrieval and word identification), Autobiographical Memory Task, Personal Future Task, yes/no future events, Scrambled Sentences Task, homophones, and other interpretation tasks (disambiguated stories and disambiguated events).

Table 3 Moderator analysis for aspect of cognition. Cognition

a

Two potential second order moderators were also coded: 2.3.5. Priming threshold Attention studies were categorised according to their use of short (≤400 ms) or long (≥500 ms) stimulus presentation duration. 2.3.6. Processing level Implicit memory priming studies were classified into four categories according to the level of processing required during encoding and retrieval: perceptual encoding and retrieval, perceptual encoding and conceptual retrieval, conceptual encoding and perceptual retrieval, and conceptual encoding and retrieval. 2.4. Inter-coder reliability Effect sizes were calculated and coded by two independent researchers. Coder 1 calculated, coded and double-checked all articles and Coder 2 calculated and coded a random selection of 20% of the articles. Inter-coder consistency for the double-coded articles was high (N99%), which indicated that the remaining articles were appropriately calculated. 2.5. Statistical analyses More than one effect size could be calculated for most studies because they used multiple measures or designs. To avoid problems associated with data dependencies, an average effect size for each of these studies was entered into the overall analysis (N = 89). However, all effect sizes from all studies were entered into the moderator analyses (N = 202). Pearson's r was used in this meta-analysis. Effect sizes were calculated according to the guidelines provided by Lipsey and Wilson (2001). Effect sizes for between-group differences were calculated from means and standard deviations, F values, or t values. If these statistics were not available, alternative methods were used; including frequencies and chi-square (see Table 1). Inverse variance weighting was applied to effect sizes (w= 1/SE). Fisher's transformation of r (zr) was used in the analyses and the r and CI values back-transformed from zr. One low- and two high-scoring univariate outliers were observed and recoded to .01 below and above the nearest score on the distribution, respectively. Homogeneity analyses were conducted using the Q statistic (see Table 2). 3. Results An initial omnibus analysis indicated that effect sizes were heterogeneous. Consequently we used a random effects model for all analyses, which generates relatively conservative estimates (Lipsey & Wilson, 2001). The omnibus analysis revealed a significant overall weighted effect size of r = .23. According to Cohen’s (1988)

Table 2 Overall effect size and homogeneity analysis. Model

Random effect size model

r

.23

CI95%

p

Lower

Upper

.19

.28

b.001

Fail-safe N

Homogeneity analysis Q

df

p

117

229.31

88

b.001

Note. Homogeneity analysis based on Fisher's r, r values based on inverse transformation of Fisher's r.

703

Attention Interpretation/beliefsb Memorya Self-esteema

n

r

58 47 72 25

p

CI95%

.20 .37 .13 .15

Lower

Upper

.15 .31 .09 .07

.26 .42 .18 .24

Homogeneity analysis

b.001 b.001 b.001 b.001

Q

df

p

38.48 58.64 45.88 28.80

57 46 71 24

.97 .10 .99 .23

Notes. Qbetween(3) = 38.43, p b .001. Homogeneity analysis based on Fisher's r, r values based on inverse transformation of Fisher's r. Categories with different superscripts differ significantly. When one effect size relating to the EAST was removed from the analysis, the relationship between self-esteem and depression increased to r = .17, p b .001.

convention, this effect size indicates a small to medium relationship between depression and negatively biased implicit self-referential cognition. An indication of the stability of this association is provided by the fail-safe N statistic, which addresses the problem of unpublished studies in “file drawers” (Rosenthal, 1979). Specifically, this statistic showed that an additional 117 studies with effect sizes of zero would be required to reduce the overall effect size to r = .10. Moderator analyses were conducted to determine whether aspect of cognition, cognitive reactivity and control, study design and sample type influenced the relationship between depression and implicit cognition. As shown in Table 3, the association varied significantly according to aspect of cognition. All facets of cognition significantly predicted depression, which corresponds with cognitive theories of depression. Studies that assessed negative interpretive biases and beliefs produced a significantly larger effect size (r = .37) than studies that assessed attention, memory or self-esteem. Second order moderator analyses were conducted to assess the roles of priming threshold in attention studies and level of processing in memory studies. Stimulus presentation duration (≤400 ms versus ≥500 ms) did not moderate the relationship between depression and negative attentional bias, Qbetween(1) = .002, p = .97. Contrary to prevailing consensus, significant associations were observed in studies that assessed both early (n = 11, r = .19, p b .001) and late (n = 41, r = .19, p b .001) stage attentional processing. As shown in Table 4, the relationship between implicit memory biases and depression was moderated to a near significant degree (p = .08) by the level of processing required by encoding and retrieval strategies utilized by the studies. Supporting the prediction of transfer-appropriate processing, studies that used perceptual encoding and retrieval strategies (r = .15) or conceptual encoding and retrieval strategies (r = .15) produced significantly larger effect sizes than studies that employed perceptual encoding and conceptual retrieval strategies. As predicted, cognitive reactivity and control moderated the relationship between depression and implicit biases (see Table 5). Studies that utilized a mood induction and a cognitive load produced Table 4 Moderator analysis for level of processing across implicit memory priming studies. Processing level at encoding/retrieval

n

r

Perceptual/perceptualb Perceptual/conceptuala Conceptual/perceptualab Conceptual/conceptualb

30 4 23 9

.15 −.01 .08 .15

p

CI95% Lower

Upper

. 09 −.14 .01 .06

.21 .12 .16 .25

b.001 .84 .03 .002

Homogeneity analysis Q

df

p

19.62 9.39 18.89 10.95

29 3 22 8

.90 .03 .65 .21

Notes. Qbetween(3) = 6.63, p = .08. Homogeneity analysis based on Fisher's r, r values based on inverse transformation of Fisher's r. Categories with different superscripts differ significantly. Similar results emerged when effect sizes relating to the Lexical Decision Task were removed from the analysis: P/P, r = .15; P/C, r = −.01; C/P, r = .07; C/C, r = .16; P/P and C/C N P/C, ps = .06.

704

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

Table 5 Moderator analysis for cognitive reactivity and control. Manipulation

n

a

None Mood induction Cognitive loadb Mood induction and cognitive load

r

164 22 13 3

Table 7 Moderator analysis for sample type. p

CI95%

.20 .18 .33 .46

Lower

Upper

.17 .09 .21 .25

.24 .27 .44 .67

b.001 b.001 b.001 b.001

Homogeneity analysis

Sample

Q

df

p

164.90 13.92 15.82 1.41

163 21 12 2

.44 .87 .20 .49

Notes. Qbetween(3) = 9.94, p = .02. Homogeneity analysis based on Fisher's r, r values based on inverse transformation of Fisher's r. Categories with different superscripts differ significantly.

the largest average effect size (r = .46). The average effect size for studies that applied a cognitive load manipulation (r = .33) was significantly larger than the mean effect size for studies that did not use a cognitive manipulation. Table 6 presents the results of the study design moderator analysis. A non-significant homogeneity analysis indicated that average effect size did not differ significantly between designs. As predicted by cognitive vulnerability and dual process models of depression, negative implicit self-referential biases significantly predicted depression in crosssectional, remitted and prospective research designs. All design categories exhibited small to medium sized mean effects, with prospective designs achieving the strongest relationship at r = .27. A dimensional view of depression was supported by the moderator analysis for sample type. As shown in Table 7, the non-significant homogeneity analysis indicated that average effect size did not differ between studies that used clinical, undergraduate and community samples. In all three sample types, significant relationships were observed between negative implicit cognition and depression. Measures used in more than one study were included in the measurement strategy moderator analysis (see Table 8). The relationship between negative implicit self-referential cognition and depression varied significantly according to the measurement strategy used. Most measures significantly predicted depression. The SDJT produced the strongest effect size (r = .59), which was significantly larger than the average effects for studies that employed the NLPT, word completion measures, other priming retrieval strategies, IAT, Stroop, dichotic listening, yes/no future, imbedded word, and lexical decision tasks. The NLPT, Dichotic Listening Task, and yes/no future task did not predict depression.

Clinical Undergraduate Community

n

65 122 14

r

CI

.17 .23 .25

Lower

Upper

.11 .19 .12

.23 .27 .38

Table 6 Moderator analysis for study design. Study design

Cross-sectional Remitted Prospective

n

163 31 8

r

.21 .19 .27

p

CI95% Lower

Upper

.18 .11 .14

.25 .28 .40

b.001 b.001 b.001

Homogeneity analysis Q

df

p

153.83 24.60 10.15

162 30 7

.66 .74 .18

Notes. Qbetween(2) = 0.96, p = .62. Homogeneity analysis based on Fisher's r, r values based on inverse transformation of Fisher's r.

Homogeneity analysis

b.001 b.001 b.001

Q

df

p

65.81 107.95 9.24

64 121 13

.41 .80 .76

Notes. Qbetween(2) = 3.33, p = .19. Homogeneity analysis based on Fisher's r, r values based on inverse transformation of Fisher's r.

4.1. Theoretical implications Models of cognitive vulnerability to depression are based on the premise that depression-vulnerable individuals possess relatively stable negative self-referential implicit cognitions that remain latent until triggered by environmental stress (Beck, 1967; Beevers, 2005). When activated, these cognitions are posited to influence all aspects of information processing (Beck, 2008; Bower, 1981; Ingram et al., 1998; Teasdale, 1988). Dual-process theories propose that depression occurs when activated implicit cognitions are not adequately regulated by explicit processing (Beevers, 2005; Haeffel et al., 2007). Accordingly, we predicted that implicit biases would be observed across all study designs and facets of cognition, and that cognitive mood and load manipulations would moderate the relationship between implicit cognition and depression. Supporting cognitive vulnerability models of depression, negative implicit self-referential cognition significantly predicted depression in studies that used cross-sectional, remitted and prospective research designs. Prospective designs achieved the strongest relationship at r = .27. These results support the view that negative implicit cognitions are not merely symptoms of depression or temporary consequences of the disorder, but also precede its onset. Further theoretical support was provided by the facet of cognition moderator analysis, which showed that biases in attention, memory, interpretation/beliefs and self-esteem all significantly predicted depression. The magnitude of this association differed significantly according to

Table 8 Moderator analysis for measurement strategy Measure

n

r

Other priminga DOATabc Dichotic listeningab Probe tasksabc Stroopab Personal futureabc Homophonesc IATab Lexical Decisionab NLPTa Other interpretationabc SDJTc SSTbc NAP/Sternbergabc Imbedded wordab Word completionab Exogenous cuingabc Yes/no futureab

23 8 4 6 18 10 4 16 17 6 8 3 17 7 4 31 10 4

.07 .22 .15 .23 .12 .35 .57 .15 .17 .07 .22 .59 .48 .36 .16 .17 .23 .16

4. Discussion Acknowledging cognitive models of depression, this meta-analysis aimed to analyse the relationship between negative self-referential implicit cognition and depression. An overall correlation of .23 was observed, which indicated that implicit cognitive biases explained a significant 5.3% of the variance in depression in a pooled sample of 7032. Additionally, results of several moderator analyses may assist the further development of depression theory, research methods and treatment strategies.

p

95%

p

CI95% Lower

Upper

.01 .08 −.02 .08 .04 .23 .40 .06 .07 −.07 .13 .39 .40 .21 .01 .10 .11 −.03

.14 .36 .32 .39 .21 .47 .73 .23 .26 .19 .31 .79 .56 .51 .30 .23 .35 .36

.03 .002 .09 .004 .004 b.001 b.001 b.001 b.001 .35 b.001 b.001 b.001 b.001 .03 b.001 b.001 .10

Homogeneity analysis Q

df

p

30.13 8.42 4.85 6.82 15.55 15.15 8.66 16.09 9.87 2.84 6.96 1.44 21.80 1.91 1.05 20.54 5.11 2.83

22 7 3 5 17 9 3 15 16 5 7 2 16 6 3 30 9 3

.12 .30 .18 .23 .56 .09 .03 .38 .87 .72 .43 .49 .15 .98 .80 .90 .83 .42

Notes. Qbetween(17) = 120.54, p b .001. Homogeneity analysis based on Fisher's r, r values based on inverse transformation of Fisher's r. Categories with different superscripts differ significantly. DOAT = Deployment of Attention Task, IAT = Implicit Association Test, NAP/Sternberg = Negative affective priming & Sternberg task, NLPT = Name Letter Preference Test, SDJT = Self-Descriptiveness Judgement Task, SST = Scrambled Sentences Task.

W.J. Phillips et al. / Clinical Psychology Review 30 (2010) 691–709

the aspect of cognition assessed. Studies that assessed negative interpretive biases and beliefs produced a significantly larger average effect size than attentional biases, memory biases and self-esteem. Notably, a significant relationship was revealed between depression and low implicit self-esteem. This finding corresponds with theoretical accounts of depression but contradicts recent associations observed between depression and high implicit self-esteem (De Raedt et al., 2006; Franck, De Raedt, & De Houwer, 2007; Gemar, Segal, Sagrati, & Kennedy, 2001). Indeed, only three notable negative effect sizes (indicating high self-esteem) were observed in the 25 selfesteem studies in this meta-analysis. The largest positive self-esteem result was obtained in a study that used the EAST (De Raedt et al., 2006). As previously noted, recent evidence suggests that the EAST effect is reduced or reversed in the presence of negative affective states and therefore may lack validity when used to assess the attitudes of depressed individuals (Vermeulen et al., 2007). When this effect size was removed from the analysis, the relationship between depression and negatively-biased self-esteem increased to r = .17. Negative biases in implicit attention and memory also predicted depression. A second order analysis indicated that the relationship between depression and attentional bias was not influenced by stimulus presentation duration (≤400 ms versus ≥500 ms), with significant biases observed in studies that assessed earlier and later stage attentional processes. This finding runs counter to prevailing research consensus that depressive biases are confined to later stages of attention. Another second order analysis showed that the association between implicit memory biases and depression was moderated (at p = .08) by the level of processing required by encoding and retrieval strategies utilized during testing. In line with Barry et al.’s (2004) transfer-appropriate processing perspective, the largest effect sizes were associated with studies that used matching levels of processing in their encoding and retrieval tasks (i.e., perceptual/perceptual and conceptual/conceptual). This pattern did not change when Lexical Decision Tasks were removed from the analysis (see Table 4). The opposing view that observation of implicit memory biases in depression depends upon elaboration and conceptual processing was not well-supported (e.g., Watkins, 2002; Williams et al., 1997; Wisco, 2009). The moderator analysis for cognitive reactivity and control revealed that the association between depression and implicit cognition was influenced by cognitive manipulations applied during experimental tasks. All manipulation categories significantly predicted depression. Studies that utilized a negative mood induction and a cognitive load produced the largest average effect size, which was closely followed by studies that applied a cognitive load manipulation. Studies that used a cognitive load procedure generated a significantly larger mean effect size than studies that did not use any manipulation. This finding corresponds with dual-process theories and neurophysiological evidence of disrupted top-down processing in depression (e.g., Beevers, 2005; Haeffel et al., 2007; Johnstone et al., 2007). However, studies that employed a negative mood induction did not achieve a larger average effect size than studies that used no manipulation. This finding contradicts the conclusion drawn by several reviewers that sad mood facilitates the emergence of negative cognitions amongst formerly depressed individuals (e.g., Scher et al., 2005). It is also incompatible with evidence indicating a relationship between depressive cognitive biases and hyperactive limbic activity (e.g., Siegle, Steinhauer, Thase, Stenger, & Carter, 2002). Several studies have directly assessed the effect of sad mood inductions on formerly depressed groups, five of which were included in this metaanalysis. Three of these studies found increased biases following a mood induction (Gemar et al., 2001; Ingram, Bernet, & McLaughlin, 1994; Ingram & Ritter, 2000) and two studies did not (McCabe et al., 2000; Meites, Deveney, Steele, Holmes, & Pizzagalli, 2008). Thus, the current results suggest that latent negative implicit biases are not reliably activated by sad mood.

705

The significantly stronger result for studies that applied a cognitive load suggests that dysregulated top-down processing may influence the emergence of depressive implicit cognitions to a greater extent than accentuated bottom-up processing. A recent fMRI study of responses to emotional stimuli supports this interpretation (Siegle et al., 2007). In that study, most depressed participants exhibited hyperactive amygdalar and hypoactive prefrontal activity but a subset exhibited only hypoactive prefrontal functioning. That is, not all depressed participants exhibited accentuated schematic processing. The current result is consistent with Haeffel et al.’s (2007) dual process model which posits dysfunctional explicit processing as the primary determinant of depression. However, most studies that applied cognitive load manipulations assessed interpretive biases and beliefs, which produced larger effect sizes than the other aspects of cognition. Thus, the larger average effect size for studies that applied a cognitive load may reflect the type of cognition assessed. 4.2. Methodological implications Considerable variability in effect size was observed according to the measurement strategy used. Fifteen measures employed in the studies produced significant effect sizes; however, the Dichotic Listening Task, NLPT, and yes/no future task did not. The latencybased SDJT produced the strongest average effect size. This task measures individuals' automatic associations between self-concept and positive or negative attributes. As such, it directly assesses implicit self-esteem as defined by Koole et al. (2001) and Rudman (2004). In contrast, the weakest effect size was produced by the most indirect measure of self-esteem included in this meta-analysis: the NLPT. In this task, participants do not directly assess their self-esteem. Instead, they directly self-assess attitudes toward letters and selfesteem estimates are based on researchers' interpretations of participants' attitudes toward letters (De Houwer & Moors, 2010). The current results suggest that direct implicit measures of selfesteem may produce more reliable associations with depression than indirect measures. After the SDJT, three of the next four strongest effect sizes related to measures of interpretation and beliefs: homophones, the SST, and personal future task. Both the SST and the personal future task were included in this meta-analysis because they were considered to be implicit in the sense of efficient and fast. De Houwer and Moors (2007) note that responses under time pressure are logically associated with other implicitness features such as uncontrolled. However, the SST and personal future tasks may allow sufficient conscious awareness to produce an overlap with measures of explicit processing. As an index of implicit cognition, some applications of the SST may be more appropriate than others (e.g., inclusion of a neutral condition). Studies with undergraduate samples are sometimes criticised as being “analogue” investigations with little generalizability to clinical samples. Contrary to this position, our analysis of sample types in this meta-analysis revealed similar relationships between negative implicit self-referential biases and depression in clinical, undergraduate and community samples. Surprisingly, clinical samples produced the smallest correlation, although not significantly so. Evidence of depressive biases in diverse samples supports a dimensional view of depression (Ruscio & Ruscio, 2000), and is in line with prevailing neurophysiological evidence which suggests that “dysphoria is associated with activation of the amygdala and related limbic structures, with a progressive reduction of the DLPFC that is associated with increasing syndromal severity” (Thase, 2009, p.208). 4.3. Treatment implications Current psychotherapies for depression focus on changing negative beliefs, interpersonal functioning, coping skills, and emotional engagement (for review, see Hollon, Thase, & Markowitz, 2002). The most

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widely used intervention, Cognitive Therapy (CT), is based on the premise that modifying explicit negative beliefs will break habitual cognitive cycles associated with depression (Beck et al., 1979). This general approach is supported by the current finding that negative interpretation and self-beliefs were stronger predictors of depression than negative biases in attention, memory or self-esteem. However, two year relapse/recurrence rates following CT can be as high as 73% for certain patient groups (Bockting et al., 2005; Tang, DeRubeis, Hollon, Shelton, & Amsterdam, 2007). Results of this meta-analysis indicate that negatively-biased implicit cognitions are associated with depression and may represent vulnerability factors for its onset, relapse or recurrence. Consequently, treatment efficacy may be improved by incorporating strategies that address implicit processes. From a dual-process perspective, implicit processing could be targeted in several ways. Therapies could aim to change conscious expectancies which would trigger corrective explicit processing in response to negative implicit output (Beevers, 2005). Evidence suggests that the efficacy of CT is determined by how much it (indirectly) increases an individual's metacognitive awareness of their implicit responses; and that low levels of awareness predict susceptibility to relapse (Teasdale et al., 2002). To this end, Mindfulness Based Cognitive Therapy (MBCT, Segal, Williams, & Teasdale, 2002) not only assists recovered depressed individuals to maintain positive conscious expectancies, but also aims to directly increase metacognitive awareness by training individuals to monitor their implicit responses. When vulnerable individuals become consciously aware of implicit responses that are incompatible with their explicit goals, that awareness should trigger corrective explicit processing. Alternatively, strategies associated with longer-term success could target patterns of activation determined by associative network structures (Beevers, 2005). Repeatedly engaging in corrective explicit processing may change biased implicit cognitions through a process of consolidation (McClelland, McNaughton, & O'Reilly, 1995). For example, consistent efforts to prevent memories from entering awareness have been shown to impair their subsequent deliberate recollection (Anderson & Green, 2001). Accordingly, Joormann et al. (2009) recently developed a procedure that successfully trained depressed individuals to forget negative material. Researchers have also used a dot-probe task to train individuals' attentional biases away from negative cues, which reduced negative emotional responses to a subsequent stressful task (MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002). New associations could also be created by exposing vulnerable individuals to repeated positive experiences (Beevers, 2005). For example, repeated exposure to new “success” experiences may begin to alter a “failure” oriented associative network. Given that associations develop slowly over time through repetition, longer-term therapies would be required to create new cognitive structures. However, the greatest promise for permanent recovery would involve reducing negative self-referential implicit biases using both affective and cognitive strategies to target implicit and explicit systems, respectively (Beevers, 2005). Such an approach would involve relearning implicit associations that underlie schematic biases. For example, exposure and experiential therapies are hypothesized to take effect via emotional processing. Emotion-Focused Therapy (Greenberg & Watson, 2005) aims to generate new cognitive structures by focussing on emotional experiences and the meanings attributed to them. Similarly, Exposure-Based Cognitive Therapy incorporates elements of mindfulness meditation, affective engagement, activation of negative selfviews, cognitive analysis and interpretation (Hayes, Beevers, Feldman, Laurenceau, & Perlman, 2005; Hayes et al., 2007). Both approaches have reported successful therapeutic outcomes. 4.4. Limitations Several limitations should be considered when interpreting the results of this meta-analysis. A relatively large number of studies or

conditions could not be included because relevant statistics were not available. Excluded studies appeared to comprise similar numbers of significant and non-significant effects, which may minimise potential bias resulting from their exclusion. Also, several categories within the moderator analyses contained very few effect sizes, which may decrease the reliability of the results. Importantly, confounds may have impacted the moderator analyses. Notably, effect sizes for interpretive biases and self-beliefs were associated with studies that used cognitive load manipulations. This may explain the higher average effect size obtained for the cognitive load category in the reactivity and control analysis. Finally, the correlational nature of data from cross-sectional and remitted studies included in this metaanalysis prevents conclusions from being drawn about causal associations between implicit cognition and depression. 4.5. Future directions Following extensive work, De Houwer et al. (2007, 2009; Moors & De Houwer, 2006; Moors et al., 2010) recently developed criteria which enables measures to be classified as implicit if they possess one or more specific features of automaticity. For this meta-analysis, we attempted to utilize these implicitness criteria but found insufficient empirical evidence to confidently classify measures. Classification of implicit features of measures would facilitate further investigations into the nature of implicit depressive cognitions. Specifically, type of implicitness could be examined as a moderator of the relationship between implicit biases and depression (De Houwer & Moors, 2010). For example, it would be possible to assess the relative predictive power of unconscious versus fast processes involved in depressive implicit cognition across studies. In turn, this knowledge could inform treatment strategies. Thus, further research is needed to assess the implicitness of measures used in depression research (see De Houwer et al., 2009). Cognitive theories of depression suggest that implicit cognitions associated with depression represent elements of a unified selfschematic construct (e.g., Beck, 1967; Beevers, 2005) and that depression-related explicit cognitions comprise a separate system of self-referent thinking (e.g., Abramson et al., 1989; Beck, 1967). However, inconsistent relationships observed across and between implicit and explicit cognitions (e.g., Phillips, 2009) place some doubt over the viability of this implied two-factor structure. A formal factor analytic study of several implicit and explicit depressive cognitions is needed to reveal how these variables covary. Given the multifarious nature of implicitness, many and various points of overlap may exist between implicit and explicit cognitions. Consequently, the structure of negatively-biased implicit and explicit cognitions may be best examined using a person-level approach. For example, it may be possible to identify vulnerability profiles based on patterns of implicit and explicit cognitions and to investigate how each profile may be uniquely associated with depression. Identification of high-risk profiles could potentially inform the development of more individually tailored treatment programs. Further research is also needed to investigate conditions under which implicit cognitive vulnerability takes effect. To date, only two studies have investigated cognitive reactivity in a dual process context. Haeffel et al. (2007) found that, when assessed separately, both explicit and implicit self-worth interacted with life stress to predict depression five weeks later. But when both interaction terms were entered simultaneously into a hierarchical regression, only explicit self-worth interacted with stress to predict subsequent depression. Conversely, Steinberg, Karpinski and Alloy (2007) assessed implicit and explicit self-esteem separately, and found that only implicit self-esteem moderated the effects of life stress on subsequent depression for high risk individuals, and only life stress predicted depression for low risk participants. Steinberg et al. classified participants into risk groups according to their levels of

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self-reported dysfunctional beliefs and attributions. Future implicit cognition research may profit from classifying high risk groups according to alternative criteria (e.g., neurophysiological attributes or attentional bias); or by examining life stress in interaction with other implicit cognitive biases.

4.6. Conclusion This meta-analysis revealed a significant relationship between negatively biased self-referential implicit cognition and depressive status. Moderator analyses supported several tenets of theoretical models of cognitive vulnerability to depression. Although the relative roles of implicit and explicit processes in depression have not yet been determined, the current study suggests that implicit cognitive biases represent significant predictors of past, current and future depression. Consequently, treatment efficacy may be improved by incorporating therapeutic strategies that target implicit processes.

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