Processing Biases 1

Running Head: FACIAL AFFECT PROCESSING BIASES

This article may not exactly replicate the final version published in the Psychological Bulletin. It

is not the copy of record, which is copyrighted by the American Psychological Association.

© 2011 American Psychological Association

Facial Affect Processing and Depression Susceptibility: Cognitive Biases and Cognitive

Neuroscience

Steven L. Bistricky

University of California, San Francisco

Rick E. Ingram and Ruth Ann Atchley

University of Kansas

Author Note

Steven L. Bistricky, University of California San Francisco; Rick E. Ingram, University

of Kansas; Ruth Ann Atchley, University of Kansas.

We would like to thank Stephen Ilardi for his helpful comments on an early version of

this article.

Correspondence concerning this article should be addressed to Steven L. Bistricky,

Department of Psychiatry, University of California San Francisco, 401 Parnassus Ave. Box 0984

– CPT, San Francisco, CA 94143. Email: steve.bistricky@ucsf.edu

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Abstract

Facial affect processing is essential to social development and functioning, and is particularly

relevant to models of depression. Although cognitive and interpersonal theories have long

described different pathways to depression, cognitive-interpersonal and evolutionary social risk

models of depression focus on the interrelation of interpersonal experience, cognition, and social

behavior. We therefore review the burgeoning depressive facial affect processing literature and

examine its potential for integrating disciplines, theories, and research. In particular, we evaluate

studies that used information processing or cognitive neuroscience paradigms to assess facial

affect processing in depressed and depression-susceptible populations. Most studies have

assessed and supported cognitive models. This research suggests that depressed and depression

vulnerable groups show abnormal facial affect interpretation, attention, and memory, although

findings vary based on depression severity, comorbid anxiety, or length of time faces are viewed.

Facial affect processing biases appear to correspond with distinct neural activity patterns and

increased depressive emotion and thought. Biases typically emerge in depressed moods, but are

occasionally found in the absence of such moods. Indirect evidence suggests that childhood

neglect might cultivate abnormal facial affect processing, which can impede social functioning in

ways consistent with cognitive-interpersonal and interpersonal models. However, reviewed

studies provide mixed support for the social risk model prediction that depressive states prompt

cognitive hypervigilance to social threat information. Based on the current literature, we

recommend prospective interdisciplinary research examining whether facial affect processing

abnormalities promote—or are promoted by—depressogenic attachment experiences, negative

thinking, and social dysfunction.

Keywords: Depression, Vulnerability, Bias, Facial Affect, Neuroscience

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Facial Affect Processing and Depression Susceptibility: Cognitive Biases and Cognitive

Neuroscience

Major depression is a leading cause of disability worldwide (World Health Organization,

2006), and it is associated with impaired interpersonal, cognitive, occupational, and health

functioning. Multiple causal pathways can lead to depression, but etiological mechanisms are not

fully-understood (Hammen, Bistricky, & Ingram, 2009). Nonetheless, evaluating current

evidence for processes theorized to promote depression can increase understanding of the

disorder and inform intervention strategies.

Historically, independent theoretical contingents have proposed that either cognitive

factors or interpersonal factors promote depression (e.g., Beck, 1967; Coyne, 1976; Lewinsohn,

1974). More recently, two noteworthy attempts have been made to integrate these theoretical

literatures. Gotlib and Hammen (1992) proposed a cognitive-interpersonal model, and Allen and

Badcock (2003) forwarded an evolutionary social risk hypothesis. To varying degrees these four

models implicate abnormal social behavior and cognition.

Most studies investigating emotional information processing in depressive populations

have used lexical stimuli. However, a recent wave of studies has shown integrative promise by

examining processing of social emotional information—facial affect. Most of these studies have

been guided by cognitive approaches to depression, which have traditionally laid claim to

emotional information processing. Finding convergence or discrepancy between verbal-semantic

and facial affective processing modalities would help refine cognitive models. However, basic

facial affect processing biases might also influence complex depressotypic cognition, behavior,

and interaction patterns, processes we review with interpersonal, cognitive-interpersonal, and

social risk theories. Because these theories tend to be more complementary than contradictory,

Processing Biases 4

confirming reliable facial affect processing biases in depression susceptible samples would

encourage more direct examination of possible relationships between proposed depressogenic

cognitive and interpersonal factors. This review highlights a conceptual blueprint and the

empirical foundation of a bridge joining theoretical perspectives, which will help frame our

understanding of depression vulnerability and promote interdisciplinary ―bench science‖.

To appreciate the important relationship between facial affect processing and depression,

one must understand the roles of affect perception in normal human functioning. At a

psychological level, facial affect processing is instrumental in social development, emotion

regulation, and social functioning (Cozolino, 2002; Leppӓnen & Hietanen, 2001). At a

neurological level, facial affect is processed by specialized networks within a particular circuit of

brain structures, some of which function abnormally in depression (Surguladze et al., 2005).

Thus, humans appear programmed to make use of facial affect information.

Several additional conceptual factors impel a separate, systematic review of research to

evaluate depressive processing of facial affect. First, facial affect effectively transmits and

evokes emotions (Ruys & Stapel, 2008). That is, facial affect simultaneously indicates the tenor

of the present social situation and influences how one feels. Thus, an attentional bias toward sad

facial expressions might result in a disproportionately depressive mood and mental

representation of the social environment. Second, direct gaze facial affect—which most studies

use—can initiate automatic self-referent processing (i.e., ―he is looking at me‖) and self-relative-

to-other processing (e.g., ―is his response to me unfavorable and dominant?‖). In this way, facial

expressions may tap the looking glass self, which is continually molded by the reflections of

others. Some evidence suggests that for depressed individuals, viewing others’ affect triggers

negative self evaluation and assumptions of others’ harsh judgments (Frewen & Dozois, 2005).

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Third, inasmuch as emotions are preparatory states for action, perceiving facial affect should

prime an immediate, appropriate social reaction. By comparison, language is often more abstract

and detached from present time and place. In line with this conceptual difference, cognitive

neuroscience suggests that facial affect may be a more evocative medium of transmitting

emotion than words (Vanderploeg, Brown, & Marsh, 1987). Fourth, facial affect helps establish

parameters of emotion processing and regulation networks before left-hemisphere-mediated

linguistic and narrative interpretation capabilities are well-developed (Chapman, 2000; Cozolino,

2002; Gazzaniga, 1989). This preverbal foundation of implicit affective memories forms an

enduring basis for intuitions about others and the world even after one can describe, rationalize,

and explicitly remember events linked to pleasant or unpleasant emotional experience.

Fifth, facial affect represents particularly salient information to depression-prone

individuals negotiating social environments. Depressed individuals commonly display impaired

interpersonal functioning, which correlates with facial affect decoding ability (Leppӓnen &

Hietanen, 2001). Lastly, facial affect can be dynamic and involuntary. As a result, it is a more

trusted indicator of a person’s internal emotions or attitudes toward an interaction partner than

more consciously controlled verbal content. Thus, a depressed person hearing supportive

platitudes might instead focus on others’ micro-expressions of ambivalence or frustration. Based

on similar reasoning, commentators have argued that depressive information biases should be

pronounced for affective interpersonal information (Allen & Badcock, 2003; Gotlib,

Krasnoperova, Yue, & Joormann, 2004). For all these reasons, facial affect is not arbitrarily

interchangeable with words as a modality of valence. A separate evaluation of depressive facial

affect processing is needed.

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The growing collection of facial affect information processing studies has not been

reviewed and evaluated based on cognitive, interpersonal, cognitive-interpersonal, and social risk

theories of depression. Additionally, cognitive neuroscience has begun to reveal depressive

attention, interpretation, and memory bias findings for affective facial stimuli. These findings

have also not been reviewed and reconciled with germane theories of depression.

The present review has two broad goals. Our main purpose is to provide a systematic

review of empirical studies that examine facial affect processing in depression susceptible

groups. We focus on the larger collection of studies employing traditional information

processing approaches and incorporate pertinent cognitive neuroscience findings. Cognitive

neuroscience can illuminate neural processes that might underlie depressive information

processing. Our second goal is to evaluate the fit of the empirical literature with cognitive,

interpersonal, cognitive-interpersonal, and social risk frameworks of depression. Cognitive

theory can be more easily evaluated using current empirical evidence than other theories.

Therefore, we focus on cognitive theory but address the potential relevance of the empirical

findings to the other three theories.

To contextualize and justify the goals of the review, we introduce relevant topics and

issues. First, we establish the theoretical importance of studying depression susceptibility and

facial affect processing. We begin by reviewing important conceptual and methodological issues

related to studying depression vulnerability. Next, we describe the cognitive, interpersonal,

cognitive-interpersonal, and social risk models of depression, focusing on aspects most relevant

to facial affect processing. We continue by discussing the importance of facial affect processing

in normal human social development, social function, survival, and evolution. We also review

the neural underpinnings of facial affect processing and evidence of differential processing

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activity in depression. Next, because various tasks can assess different cognitive processing

stages and elicit nuanced findings, we describe information processing tasks and neuroscience

techniques used in the literature. We then explain the method used for the present review.

Finally, we review and critically evaluate empirical studies that have examined facial affect

processing in depressed or depression-susceptible populations. We conclude with a synthesis of

empirical findings and theoretical models, and we identify areas in need of future research.

Important Theoretical and Methodological Issues in the Literature

Studying depression and depression vulnerability. Although individuals are directly

classified as depressed by meeting diagnostic criteria, operationalizing depression vulnerability

is necessarily indirect. Studies operationalize vulnerability according to known risk factors. For

example, vulnerable groups typically have experienced a past major depressive episode, have

parents with a history of depression, or are experiencing stable dysphoria, a subclinical syndrome

that includes depressive and anxious symptoms (Ingram & Hamilton, 1999). Researchers then

compare these high-risk and low-risk groups on factors thought to mediate vulnerability. It has

been noted that when these proposed vulnerability factors are found in currently or formerly

depressed individuals, they might represent internal products (or ―scars‖) from past episodes

(Lewinsohn, Steinmetz, Larson, & Franklin, 1981). Such a factor might not be causally related to

depressive onsets. However, studies that find a proposed mechanism prior to a first onset

circumvent scar interpretations and support a vulnerability factor conceptualization.

Cognitive models of depression. It has long been proposed that negative cognition

initiates and maintains depression (Beck, 1967). From a cognitive diathesis-stress perspective,

vulnerable individuals are thought to possess a depressive cognitive schema that distinguishes

them from nonvulnerable individuals. This schema includes a ―negative triad‖ of core beliefs

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about the self, the world (e.g., indifference or disapproval from others), and the future (Beck,

1970). The depressive schema develops from recurrent, prolonged processing of negative

information. This elaborative processing activates, strengthens, and expands connected networks

of related depressive thoughts and representations of sad experiences. These myriad

interconnections sensitize the depressive schema and make it susceptible to self-sustained

patterns of spreading, reverberating activation (e.g., rumination; Bower, 1981; Ingram, 1984;

Nolen-Hoeksema, Morrow, & Fredrickson, 1993; Teasdale & Barnard, 1993). In essence,

depressed mood represents an affective-motivational state in which a mood-congruent shift in

processing causes depressive information to become more salient and accessible. Thus, cognitive

models predict that when activated, a depressive schema generates mood-congruent cognitive

products, such as negative self-attributions or automatic thoughts, or cognitive processes, such as

negative interpretive, attentional, or memory biases (Beck, Abramson, Seligman, & Teasdale,

1978; Williams, Watts, MacLeod, & Mathews, 1997). Further, these biases should be strongest

for information consistent with themes of sadness and loss, as opposed to fear or anger. This

latter content specificity hypothesis (Beck, 1976; Ingram, Miranda, & Segal, 1998) has

significant empirical support (see Beck & Perkins, 2001 for meta-analysis). When a depressive

schema is not activated, it is characterized as ―latent but reactive‖ to stress, such as a sad mood

induction (Segal & Shaw, 1986). This concept of cognitive reactivity dictates that depressotypic

cognitive processing and products should emerge when a depressive schema is activated, but not

necessarily when it is dormant.

Studies employing mood-priming largely support the idea of cognitive reactivity (see

Scher et al., 2005 for review). That is, formerly depressed individuals in a nondysphoric mood

usually cannot be distinguished from never depressed individuals in affective cognition.

Processing Biases 9

However, typically when these groups experience an affective challenge, such as a sad mood

induction, depressotypic cognitive patterns emerge only in the formerly depressed group. Studies

administering pharmacological challenges, such as acute tryptophan depletion (ATD), have also

evoked twin negativistic shifts in mood and information processing. These depressive effects of

altered central serotonergic function are pronounced in people who have remitted from

depression or who have elevated familial risk of depression (Merens, Booij, Haffmans, & van

der Does, 2008; Munafo, Hayward, & Harmer, 2006). Thus, within a sad mood, the cognition of

formerly depressed groups typically resembles that of currently depressed groups.

Negatively biased selective attention, interpretation, and memory have also distinguished

currently dysphoric groups from nondysphoric groups (e.g., Bradley, Mogg, & Lee, 1997; Gur et

al., 1992; Matt, Vazquez, & Campbell, 1992). Although cognitive models originally conceived

depressive biases to be strictly toward negative information, biases away from positive

information have also been found (Clark, Beck, & Stewart, 1990). Dual-valence biases appear

consistent with major depressive episode presentations, which are often characterized by both

increased negative affect and decreased positive affect (Clark & Watson, 1991). Similarly,

sometimes depressed individuals may lack a positive processing bias that psychologically

resilient individuals show. Under certain circumstances processing information in an accurate,

evenhanded fashion might stifle the motivational or mood-buffering benefits of an over-

optimistic cognitive bias (see DePue & Collins, 1999; Forgas & East, 2008a, 2008b).

As an important aside, recent studies employing pharmacological challenges or

psychophysiological techniques have reported negativistic cognitive processing in the absence of

sad mood in formerly depressed groups (Atchley, Ilardi, & Enloe, 2003; Atchley, Stringer,

Mathias, Ilardi, & Minatrea, 2007; Hayward, Goodwin, Cowen, & Harmer, 2005; Steidtmann,

Processing Biases 10

Ingram, & Siegle, 2010; Victor, Furey, Fromm, Öhmann, & Drevets, 2010). At least two

important theoretical and methodological issues arise from these findings. First, active cognitive

vulnerability factors may be present in nondysphoric depression vulnerable individuals. Second,

cognitive neuroscience techniques may complement traditional behavioral performance measures

of cognition to increase researchers’ ability to detect depressotypic processing patterns. For

example, if a study reveals neural activity differences but finds no behavioral performance

differences, there could be several possible explanations. Measures of neural activity might

provide greater sensitivity for detecting differences, neural activity differences might precede the

emergence of performance differences, or alternate areas of the brain might compensate to

maintain normal task performance (e.g., Drummond, Gillin, & Brown, 2001). Irrespective of

method or mood status, detected emotional biases in selective attention, inhibition, and memory

could serve as depression vulnerability or maintenance factors.

Selective attention involves discerning information that is relevant to a current objective

from irrelevant information, then activating relevant and inhibiting irrelevant information in

working memory (Houghton & Tipper, 1994; Neill, Valdes, & Terry, 1995). Accordingly,

Joormann (2004) proposed that depressotypic selective attention might partly result from an

inhibitory processing deficit for depressive information. Supportive evidence has been found in

depressed and depression susceptible groups (Goeleven, De Raedt, Baert, & Koster, 2006; Hsieh

& Ko, 2004; Joormann, 2004; Kuehner, Holzhauer, & Huffziger, 2007). Inhibition is also

thought to be instrumental in efficient memory encoding and retrieval. When an individual is

focusing on emotional information, a global selective attentional bias toward, or a specific

inhibitory deficit for, negative information might lead to increased elaborative processing of

negative content. With time and repetition, this pattern could strengthen connections among

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depressive cognitive structures (Ingram, 1984). Also, when an individual attempts to focus

attention on nonaffective task-relevant information but previously activated negative cognitive

content has been degraded insufficiently, the negative content could linger and contaminate

working memory. As a result, associations between task-relevant nonaffective information and

task-irrelevant depressive information could be encoded and later retrieved. Once this occurs,

depressive cognitive structures could be activated and strengthened by processing depressive or

associated nonaffective information (Linville, 1996).

Depressive cognitive residue could interfere with working memory, leading to difficulties

with the kinds of complex problem solving required in daily life (Siegle, Ingram, & Matt, 2002).

In turn, these difficulties might increase depressive thoughts and feelings. For this individual, the

omnipresent salience of a depressive schema could override attempts to focus on nonaffective

goal-relevant information processing, resulting in intrusive streams of depressive thoughts.

Therefore, selective attention biases for depressive facial affect might lead to negative,

ruminative thought patterns, which would perpetuate depressive moods (Nolen-Hoeksema et al.,

1993) and deeply encode depressive memories, the combination of which might initiate or

maintain depressive episodes. Prospective studies support that cognitive biases might contribute

to depressive onsets (e.g., Hammen & Goodman-Brown, 1990; Robinson & Alloy, 2003).

Interpersonal models of depression. Interpersonal models of depression hypothesize that

specific social behaviors and social deficits maintain or exacerbate depressive episodes by

impeding rewarding social experiences and increasing punishing ones. For instance, a depressed

woman may excessively seek reassurance from others, eventually leading to the erosion of close,

supportive relationships. Initially, well-meaning relational partners may validate that she is

valued and loved, reinforcing reassurance-seeking behavior. However, as requests for

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reassurance increase and persist, relational partners may become irritated and emotionally distant

(Joiner & Coyne, 1999). Alternatively, negative self-verification behaviors might also promote

social conflict and rejection (Swann, Wenzlaff, Krull, & Pelham, 1992). A depressed man may

insist that significant others evaluate him negatively, verifying an unfavorable self-concept.

Relational partners who confirm his negative self-concept further perpetuate his depression.

Relational partners who dispute his negative self-evaluation without any persuasive effect may

become frustrated and may avoid or reject him. Evidence supports the existence of depressive

interactional patterns proposed by these models (Joiner, Metalsky, Katz, & Beach, 1999; Swann,

Wenzlaff, & Tafarodi, 1992).

Depression has also been associated with social skills deficits, including poor conflict

resolution skills, insufficient eye contact, and deficient ability to foster rewarding, supportive

relationships (Lewinsohn, 1974). Although depressed individuals may be more interpersonally

dependent (Barnett & Gotlib, 1988), their interaction patterns tend to be aversive to others. As a

result, others may avoid them or react negatively toward them (Gilboa-Schechtman, Erhard-

Weiss, & Jeczemien, 2002). As more superficial friendships dissolve, a depressed individual may

more desperately lean on remaining close relationships. Greater relational strain may instigate

corrosive social conflicts that a depressed person struggles to resolve (see Whisman, 2001). It is

common for relational conflicts to trigger or exacerbate depression (e.g., Monroe, Rohde, Seeley,

& Lewinsohn, 1999; Paykel et al., 1969). Therefore, interpersonal models effectively explain

how a depressed person can become mired in a social environment that is increasingly punishing

and unrewarding (Joiner & Coyne, 1999) without the social skills or resources to obtain social

reinforcement or support (Gotlib & Hammen, 1992).

Given the nature of these social deficits, interpersonal models and facial affect processing

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might interrelate in several ways. Depressed groups might inaccurately identify others’ affects,

and this impairment might be linked to social dysfunction, excessive reassurance seeking, or

rejection. They also might attend less to others’ facial affect, find others’ affect more aversive,

and be less willing to engage with people displaying any affect, even happiness.

Cognitive-interpersonal model of depression. Gotlib and Hammen (1992) have

identified developmental interrelationships between cognitive and interpersonal factors linked to

depression susceptibility. Their synthesis describes how interpersonal behaviors and experiences

can influence self-esteem, perceived self-worth, and expected responsiveness of others. In turn,

this mindset may promote social behaviors and experiences that initiate or maintain periods of

depression. In particular, Gotlib and Hammen draw attention to conceptual similarity between

attachment and cognitive theories. According to Bowlby (1969, 1981), during the normal parent-

child attachment process, a child develops a mental representation, or working model, of close

relationships. This representation includes assumptions about what can be expected from others

and a relative sense of one’s importance to others. However, patterns of neglect, abuse, and harsh

judgment would thwart the development of secure attachment and self-esteem. This

conceptualization melds well with Beck’s negative cognitive triad. Combined, attachment and

cognitive approaches describe how a person could develop low self-esteem and expectations that

the social world will forever be unwelcoming and unmanageable.

Empirical findings support the plausibility of these interacting processes. For example,

those who develop depression tend to experience worse care in early family environments (e.g.,

Hammen, 1991). Poor parenting has been shown to predict lower social competence in children,

which, along with insecure attachment, predicts depressive symptoms longitudinally (Gotlib &

Hammen, 1992; Hammen, 1991; Lee & Hankin, 2009). Particularly important to Gotlib and

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Hammen’s synthesis, the influence of harsh parenting during childhood on adult depression may

be mediated by negative cognition (for review see Alloy, Abramson, Smith, Gibb, & Neeren,

2006). The cognitive-interpersonal model might be further enriched by findings that connect

abnormal facial affect processing with core constructs, such as early adverse interpersonal

experiences, social dysfunction, and negative cognitions about the self and the social world.

Social risk hypothesis. Evolutionary theorists have speculated that depressive social

behavior and cognition emerges from a normally adaptive depressive mechanism (Allen &

Badcock, 2003; Gilbert, 2001; Nesse, 1998; Nettle, 2004) that becomes maladaptive when

chronic activation leads to health-compromising major depression. Allen and Badcock’s (2003)

postulated mechanism manifests at multiple levels (social behavior, information processing,

neural activity) to help maintain tenuous social inclusion following significant reduction in social

capital. Specifically, their social risk hypothesis proposes that transient depressed states evolved

as a cautious mode of interaction that emerges when one’s perceived social burden on others

meets or exceeds his or her perceived social value to others. If this value-to-burden ratio—

referred to as social investment potential–drops below 1.0, a person becomes prone to exclusion

from social bonds that historically have facilitated survival and reproduction. Depressive

symptoms often follow a social loss, humiliation, or failure—decreases in social value. A man

with suddenly less to offer an interdependent social system could re-balance his social

investment potential and maintain social inclusion by conspicuously reducing his consumption of

limited group resources (Allen & Badcock, 2003). His submissive behavior would decrease

group competition for mates, food, and property. From this perspective, temporary decreased

appetite, anhedonia, social isolation, and feelings of worthlessness and hopelessness are products

of an adaptive depressive mechanism.

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Allen and Badcock’s proposed depressive-mood-activated mechanism engages patterns of

thought, emotion, and behavior that decrease social burden and protect damaged social value.

This social risk-averse strategy is marked by reluctance toward social interactions, except those

with predictable, safe outcomes. To this end, the strategy promotes vigilance to and avoidance of

any interaction with the slightest chance of social devaluation. As such, a depressed individual

would be unlikely to seek out interactions with unfamiliar people. Even an apparently friendly

stranger could turn out to be an outcast or a deceitful manipulator. Instead a depressed person

would try to bolster existing close relationships (Gilbert, 1992). From this perspective, both

reassurance seeking and negative self-verification behaviors may help discern dependable

relationships amid perceptions of low self-worth.

Most relevant to this review, Allen and Badcock’s (2003) depressive mechanism

presumably promotes greater cognitive sensitivity and attention toward socially threatening

information and away from socially affirming information. The mechanism also negatively

biases interpretation of social information, such that ―negative conclusions are drawn from any

semblance of social threat, no matter how minor‖ (Allen & Badcock, 2003, p. 899). Although

Allen and Badcock focus on hypersensitive social-perceptual processes, it follows that memory

for past social threats would aid a risk-averse strategy. Facial affect would be a particularly

important reference to gauge one’s social value and to avoid potentially risky social interactions.

Allen and Badcock do not detail which facial affects might connote risk. However, based on

their theory, face-to-face expressions of neutral, angry, contemptuous, sad, fearful, disgusted,

and mixed affect might foreshadow unpredictable and socially hazardous interactions (Adams,

Ambady, Macrae, & Kleck, 2006; Fischer & Roseman, 2007; Hendriks & Vingerhoets, 2006;

Knutson, 1996; Rozin, Haidt, & McCauley, 2008). From a depressive framework, a frown could

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indicate displeasure, a fearful or disgusted expression could indicate aversion to the person’s

presence, and angry affect could signify irritation or even physical threat. Thus, ensuing

interactions would carry the potential for social devaluation. Until now, the accuracy of social

risk hypothesis predictions has not been evaluated based on the depressive facial affect

processing literature.

The importance of facial affect processing.

Human evolution. We have come to understand that facial affect perception is important

in human social development, social function, and evolution. Charles Darwin (1872) observed

the similarity of specific human emotional expressions (e.g., laughter, fear, rage, dejection)

across different ages, races, and cultures, and also in comparison with non-human primates. A

century later, Paul Ekman (1992) presented evidence of universal human facial expressions of

basic emotions (e.g., happiness, sadness, anger, disgust, fear, surprise). He also taxonomized

expression-specific facial musculature contraction and relaxation patterns (Ekman & Friesen

1978), instructions for which are presumably encoded in our genome. It appears facial

expressions of emotion have been preserved through evolution because they serve an adaptive

purpose.

Emotional facial expressions function as a fundamental paralanguage for communicating

basic needs, a medium that preceded the development of language in human evolution (Wilson,

1999). Perceiving facial identity and affect allows a person to recognize family, friends,

acquaintances, and rivals; to assess their internal mental and mood states; and to obtain

immediate feedback within every interaction (Cosmides & Tooby, 2000). This information can

be used to predict a person’s intentions and ensuing behaviors. For example, facial affect can

signal social danger to be averted. Accordingly, humans judge angry, fearful and disgusted faces

Processing Biases 17

as more interpersonally critical and harsh (Stein, Goldin, Sareen, Zorrilla, & Brown, 2002).

Humans also more quickly attend to and more accurately identify angry faces (Krysko &

Rutherford, 2009). In turn, other people’s facial affect can effectively guide one’s pursuit of

relational goals (e.g., reassure trust, establish dominance or submissiveness, or disengage) and

define relational status (Leathers, 1997). For instance, humans are less willing to approach and

interact with a person displaying angry or disgusted facial affect (Campbell et al., 2009), as this

person might threaten one’s social position or safety. In terms of interpersonal outcomes, facial

affect decoding ability correlates with social competence, social adjustment, and peer popularity

(Leppӓnen & Hietanen, 2001). Throughout our ancestral history, facial affect processing would

have been indispensable in solving problems related to survival, co-existence, and reproduction.

The ability to decode facial affect and react accordingly has been critical for satisfying

interaction partners, fostering close attachments, and building alliances (Elfenbein & Ambady,

2002; Marsh, Kozak, & Ambady, 2007; Montagne, Kessels, Frigerio, de Haan, & Perrett, 2005).

For countless millennia these bonds have facilitated reliable access to communally gathered

resources, mating opportunities, and protection for oneself and one’s offspring. In contrast,

abandonment and ostracism have threatened extinction for the individual and his or her genes. In

this way, genes that program mechanisms to execute facial affect perception and the social skills

that depend on this processing would have provided a selective advantage (Dawkins, 1976).

Social development and functioning. Humans are programmed to transmit and receive

emotional information via face-to-face visual interaction. Days after birth, infants start to

discriminate happy from sad faces, show basic facial imitation (Field, Woodson, Greenberg, &

Cohen, 1982), and show a preference for their mother’s face (Johnson & Morton, 1991). This

early attentional orienting to a mother’s face encourages bonding interactions, which stimulate

Processing Biases 18

the release of dopamine, prolactin, oxytocin, and endorphins that produce shared enjoyment,

contentment, and emotional attachment. Recurrent interactions like this help develop neural

networks of attachment, which include the orbitofrontal cortex (Panksepp, 1998; Schore, 1994).

The orbitofrontal cortex is particularly important in assessing nuanced reward and punishment

contingencies in dynamic social interchanges. As such, orbitofrontal damage impairs recognition

and use of facial affect to assess reinforcement value or gauge approachability in interpersonal

situations (Hornak et al., 2003; Zald & Kim, 2001). Thus, throughout life, an optimally

functioning orbitofrontal cortex is critical for obtaining desired social outcomes. Also, face-to-

face infant-mother interactions bring online networks responsible for contracting the infant’s

facial muscles to imitate its mother’s expression. This initially reflexive mirroring process carries

on into adult life at a subtle but measurable level (Dimberg, Thunberg, & Elmehed, 2000).

Facial mirroring has several important consequences. Typically, a primary caregiver

perceives emotional responsiveness from the infant and reciprocates with increased interest,

attention, and positive emotion. For the infant, such attunement could also help build working

models of attachment figures as being responsive and caring. Similarly, face-to-face interactions

may help establish mental representations of the world as being safe and manageable, or

dangerous and overwhelming (Schore, 1994). These important mental representations would be

well-developed before linguistic and narrative interpretation capabilities are fully functional

(Chapman, 2000; Gazzaniga, 1989). In other words, a model of the socio-emotional landscape is

established before the words to describe it. This primary source material for approach and

avoidance decisions is activated in the brain’s right-hemisphere and gets expressed as

unconscious intuitions (Cozolino, 2002). Lastly, facial mirroring may help develop bidirectional

connections between emotions and facial expressions. Through bidirectional connections,

Processing Biases 19

positive emotion can prompt a genuine Duchenne smile, or intentionally contracting muscles that

are typically used to smile can induce positive emotion (Soussignan, 2002). Capitalizing on a

baby’s developing emotion-facial expression connections, facial mirroring with a caregiver may

also help establish rudimentary emotion regulation.

A newborn possesses limited ability to regulate emotion. When the baby becomes upset, a

parent may initially attend to and mirror the child’s distress. The parent may then soothe the

baby through touch, verbal inflection, and calm facial expression. In turn, the baby’s facial

mirroring system may slowly attune to the parent’s calm expression. Through facial muscle-

affect connections, the child’s underlying emotions also become more in line with the parent’s.

Simultaneously, these interactions scaffold the development of the baby’s inhibitory neural

networks. Over time, the parent’s emotion regulation becomes internalized in networks that

enable the child to self-soothe (Cozolino, 2002). Furthermore, these experiences teach the child

that attachment relationships can help regulate emotion and facilitate contentment.

When a parent has depression, attunement can backfire for a child. Rather than being

soothed by a parent’s interest and positive affect, a child might mirror his depressed parent’s sad

facial affect. Evidence indicates that infants show increased right frontal EEG asymmetry

consistent with negative affect when viewing sad versus happy faces (Diego et al., 2004). In

effect, a depressed parent’s affective experience would become the child’s (Gunnar & Stone,

1984). Potentially as a basic mood regulation strategy, an infant will tend to look less at his

mother’s face if she is depressed (Boyd, Zayas, & McKee, 2006). Without a stable parental

scaffold to construct robust emotion regulation, a child’s inhibitory capacity might not fully

develop. This would result in deficient self-soothing. Furthermore, Cozolino (2002) proposes

that in the midst of this type of suboptimal attachment experience, a child acquires a

Processing Biases 20

hypersensitivity to negative social cues, such as facial expressions (see Joormann, Talbot, &

Gotlib, 2007 in this review). Interestingly, research on serotonin transporter gene variants (5-

HTTLPR) linked to increased depression risk indicate that environmental factors during

development may help calibrate a pattern of increased neural responsiveness to negative facial

affect (e.g., Gibb, Benas, Grassia, & McGeary, 2010; Wolfensberger, Veltman, Hoogendijk,

Boomsma, & de Geus, 2008). Notably, it is challenging to separate environmental and genetic

influences in studies examining signs of emotional vulnerability in children of depressed parents.

However, it is plausible that early facial affective attunement processes help shape the

development of emotional attachment and emotion regulation.

Functional neuroanatomy of facial affect processing. Characteristics of the human

neural system supporting facial affect perception further corroborate the evolutionary and social

significance of emotional expressions. Substantial evidence indicates that the amygdala, the

orbitofrontal cortex, and fusiform gyrus of the right temporal cortex are instrumental in

processing facial affect (see Rolls, 2008 for review). In fact, specific neurons in the amygdala,

orbitofrontal cortex, and superior temporal sulcus activate preferentially to affective faces.

Dysfunction in these regions can result in impaired social and emotional behavior. More

complex theoretical models have described specialized subsystems involving additional brain

regions, such as the parietotemporal cortices, insula, thalamus, dorsal anterior cingulate, and

cerebellum (Gerber et al., 2008; Haxby, Hoffman, & Gobbini, 2000; also see Fusar-Poli et al.,

2009 fMRI meta-analysis). Although cortical activity promotes conscious perception of

emotional expressions, subcortical processing can efficiently and independently decode facial

affect. One effect is that humans can read and react to facial affect that they have not consciously

Processing Biases 21

seen (Vuilleumier & Schwartz, 2001; Whalen et al., 1998). Thus, the brain appears to have

evolved rapid, specialized facial affect processing (Schore, 1994).

Heller and colleagues (1993, 1998) proposed a model that might help explain depressive

responses to facial affect. The model suggests that experience of emotion is mediated by a frontal

valence system, and the perception of emotion is lateralized in a right parietotemporal arousal

system. Further, these systems are functionally interconnected (see Leathers, 1997 for similar

conceptual discussion of categorical and dimensional aspects of face processing). In support of

the valence-arousal model, resting state electroencephalographic (EEG) studies show that

depression prone groups exhibit less left (relative to right) frontal activity (Jones, Field, Fox,

Lundy, & Davalos, 1997; Tomarken, Dichter, Garber, & Simien, 2004), a pattern linked to

negative emotion and reduced social approach behaviors (Davidson, 1993). They also show

abnormally reduced right-than-left posterior activity during resting state (Bruder, Tenke, Warner,

& Weissman, 2007) and in response to affective faces (Deldin, Keller, Gergen, & Miller, 2000;

Jaeger, Borod, & Peselow, 1987), a pattern related to impaired facial affect identification (Heller

et al., 1998). Impaired facial affect discrimination due to right posterior hypoactivation might

maintain depressive experiences and behaviors, as represented by left frontal hypoactivation. For

instance, a depressed man might not register another person’s subtle smile and would thus not

experience its positive emotion.

Major depression is also often characterized by amygdala dysregulation (Mayberg, 1997).

In particular, depression has been strongly linked to greater amygdala reactivity to sad faces and

lower reactivity to happy faces (Dannlowski et al., 2007b; Surguladze et al., 2005; Suslow et al.,

2010). This pattern may be reversible with antidepressant medication (Victor et al., 2010). Given

that cognitive biases can disappear when depression remits, it is not surprising that effective

Processing Biases 22

treatment can lead to changed neural responses to facial affect. Interestingly however, Fu and

colleagues (2004, 2008) found that mode of treatment differentially affected specific neural

activity changes. Cognitive behavioral therapy affected mainly cortical functioning, while

antidepressants led to cortical and subcortical changes. In summary, facial affect processing

requires the coordinated activity of many regions of the brain (Vuilleumier & Pourtois, 2007),

and abnormal regional function can be linked to depressotypic processing biases.

Information Processing Tasks Employed in the Literature

Many studies have compared healthy and depression-prone groups’ attention to affective

facial stimuli. Studies assessing for attentional biases have used dot-probe, face-in-the-crowd,

negative priming, exogenous cueing, and deployment of attention tasks (DOAT). Other studies

have employed affect identification tasks or memory tasks. In these studies, behavioral measures

such as accuracy and reaction time are the key dependent variables.

Deployment of attention task. On each deployment of attention task trial, two side-by-

side affective faces (e.g., negative and neutral, positive and neutral, or positive and negative) are

presented simultaneously. However, participants are misinformed that one face will appear

slightly before the other face, and they are instructed to identify which face appears first. It is

inferred that the face the participant chooses in each trial is the first to become consciously

available because it has received greater attentional allocation.

Dot-probe task. To begin each dot-probe task trial, two affective facial expressions (e.g.,

positive-neutral, negative-neutral, or positive-negative) of the same actor appear on opposite

locations of a computer screen. The pair of faces is quickly replaced by a single dot, which

appears in place of one of the faces. The participant is to identify the position of the dot by

pressing one of two buttons as quickly as possible. If attentional allocation is greater toward the

Processing Biases 23

face where the dot subsequently appears, the dot’s position will be identified faster. In contrast, if

attention is greater toward the face opposite the location where the dot subsequently appears, the

response time will be delayed.

Exogenous cueing task. The exogenous cueing task (Posner, 1980) attempts to measure

selective attention toward useful information and attention inhibition of potentially distracting

information. At the start of each trial, a cue stimulus (e.g., a face) is briefly presented in one of

two opposite locations on a screen, and then a target square is presented in either the same or the

opposite screen location. Participants must watch for the target square and, when it appears, they

quickly press a button corresponding with the square’s location. In any given trial, the cue either

serves as an accurate predictor, pulling attention toward the spatial location of the subsequently

presented target square, or it serves as a distracter, pulling attention away from the location of the

subsequently presented target square. A faster response to the target square when the cue is an

accurate predictor is known as the cue validity effect. This effect is expected to increase with the

amount of attention a cue captures (Leyman et al., 2007).

Face-in-the-crowd task. The face-in-the-crowd task assesses how efficiently one detects

specific facial emotions within slides that include between two-to-six schematic faces. Schematic

faces are rudimentary line drawings conveying happy, sad, or neutral affect. Schematic faces

stand in for human faces in this task because facial emotion detection presumably occurs rapidly

and efficiently based on low-level perceptual features (Purcell & Stewart, 1988). Some slides

show faces with identical affect, while other slides include one emotional expression that

contrasts with surrounding expressions (e.g., one happy among three neutrals). In response to

slides, participants are to rapidly decide if all expressions are identical or if one is different.

Facial affect identification tasks. Ineffective facial affect identification can result from

Processing Biases 24

insensitive detection of various affective intensities (e.g., subtle happiness) or from

misinterpreting the specific categorical meaning of facial affect (e.g., fear rather than anger).

Thus, investigators have varied methodological elements to examine affect identification

sensitivity and specificity in depression prone groups. In these tasks, participants view facial

stimuli and then either label the displayed emotion or attempt to match the stimulus to a model

face with the same emotion. Depending on the study, participants view stimuli anywhere from

briefly (e.g., 100 ms) to as long as they need to respond. These stimuli either show prototypical

facial affect (i.e., 100% sad) or ambiguous morphed gradations of two affects (e.g., 50% happy

and 50% neutral). Some studies have had participants discriminate only among happy, sad, and

neutral affects, while others have had participants identify as many as seven different affects.

Negative priming task. A negative priming paradigm that utilizes affective facial stimuli

can index cognitive inhibition of specific emotional content. On each trial, two pairs of affective

faces are presented in succession (prime then probe). For each presented pair the participant must

quickly press a button corresponding to an identified target face (e.g., framed in black) and

ignore a distracter face (e.g., framed in gray). When a specific affect appears first as a prime

distracter and then as a probe target, the probe target response is typically delayed. This is known

as the negative affective priming effect. On such a trial, a person might need to inhibit processing

of a first sad face and soon thereafter respond to a second sad face. To the degree that inhibition

of the prime distracter affect (e.g. sadness) carries over into the probe trial, it will slow responses

to probe targets of the same affect (e.g., sad faces). When this negative affective priming effect is

missing it typically indicates an attentional inhibition deficit.

Oddball task. A facial oddball task requires participants to ignore viewed faces from one

predominant affective category and to respond to faces from a different, rarely presented target

Processing Biases 25

category. For a particular block, participants might view 80% neutral faces and respond only to

the 20% of presented faces, which convey happy affect. As such, the task elicits selective

attention for a particular infrequently presented target affect. The task also can evoke response

inhibition if rare non-target affective distracter stimuli are presented.

Recognition memory task. To begin a recognition memory task, participants encode

affective facial expressions either intentionally or unintentionally by completing another

information processing task that includes those stimuli (e.g., affect identification task). After a

delay, participants are challenged to identify only previously viewed (―old‖) faces in a set that

also includes novel (―new‖) faces. Typically, the familiar faces in the recognition phase are the

identical images (i.e., same actor, same affect) presented in the encoding phase.

Remember/know/guess task. In this recognition memory task, participants first

intentionally encode images of affective facial expressions displayed by actors. Later, they view

neutral expressions of old and new actors, and are instructed to identify only those actors who

were presented in the encoding phase. Participants answer ―remember‖ if they both recognize the

identity of the actor and the specific affect the actor had displayed at encoding. They respond

―know‖ when they recall the actor but not the actor’s previously displayed affect. They respond

―guess‖ if they are unfamiliar with the actor or the previous affect.

Cognitive Neuroscience Measures Employed in the Literature

A collection of studies has employed cognitive neuroscience to examine brain correlates

of facial affect processing in depressed and depression vulnerable groups.

Event-related potentials techniques. Orienting, engagement, disengagement, and

shifting of attention are events that take place on a very brief time scale. For this reason, event-

related potentials (ERP) research, with its precise temporal resolution, has been most effective in

Processing Biases 26

elucidating how attentional processes occur in time within particular neurocognitive systems.

Several ERP waveform components correspond with distinct stages of attention, including low-

level stimulus feature processing, high-level abstract classification, and sustained engagement.

The present review focuses on the N200 and P300 because these components have been linked to

depressive attentional and classification biases. ERP components appear as positive or negative

amplitude peaks or troughs within specific time windows of the waveform. For example, a P300

is the third positive deflection that appears roughly 300-500 milliseconds (ms) post-stimulus.

The P300 may represent the amount of attentional resource allocation, which is inferred by the

component’s observed amplitude (Debener, Kranczioch, Herrmann, & Engel, 2002). The N200

is the second negative waveform deflection that peaks around 250 ms. An N200 appearing in

frontal scalp locations may represent conflict monitoring and/or inhibition, as in detecting and

suppressing information processing or behavioral responses that conflict with a primary task goal

(Jonkman, 2006). In contrast, an N200 appearing in a right posterior scalp location can reflect

neural activity related to facial processing (e.g., Deldin et al., 2000).

Functional neuroimaging. Neuroimaging techniques such as functional magnetic

resonance imaging (fMRI) and positron emission tomography (PET) have been used to monitor

physiological correlates of brain activity in response to passively viewed affective stimuli. fMRI

detects regional recruitment of oxygenated blood, while PET detects changes in regional glucose

metabolism. With both techniques, physiological responses trail cognition by a couple seconds.

However, these techniques can precisely localize and measure sustained cognitive processing

within networked neural structures.

Summary

As discussed, facial affect processing is essential to social development and functioning,

Processing Biases 27

and is particularly relevant to models of depression. We therefore review the burgeoning

depressive facial affect processing literature and examine its potential for integrating disciplines,

theories, and research. Two main questions guide this review. First, what do information

processing and cognitive neuroscience studies indicate about facial affect processing in

depression and depression susceptibility? Second, what support can such studies provide for

cognitive, interpersonal, cognitive-interpersonal, and social risk models of depression? Based on

the extant literature, we identify ways in which future research might yield greater understanding

of facial affect processing and depression susceptibility.

Method

This review was influenced by AMSTAR recommended criteria for systematic reviews

(Shea et al., 2007). It focuses on empirical studies that examine information processing of

affective facial stimuli in depression susceptible samples.

Literature Search

To capture relevant studies, we completed thorough searches on several online databases,

including PsycINFO, MEDLINE, PUBMED, and Psychiatry Online. We used numerous search

terms: words beginning with the root depress, face, facial, face processing, expression, affect,

emotion, bias, information processing, interpretation, recognition, attention, memory, recall,

imaging, psychophysiology, and ERP. We also examined reference sections of studies meeting

inclusion criteria for any additional relevant studies.

Inclusion and Exclusion Criteria for Included Studies

We included in the present review studies that met the following criteria. We selected

articles that were written in English and were identified by searches through November 2010.

We included articles irrespective of publication status to reduce potential publication bias. To

Processing Biases 28

refine our search, we selected only studies that included a significant, identifiable proportion of

participants with current major depressive disorder or risk for depression. To this end, included

studies assessed the presence of past or present depression as defined by the Diagnostic and

Statistical Manual, Fourth Edition, Text Revision (American Psychiatric Association, 2000). We

considered study subsamples to be at-risk for depression if an assessment indicated a personal or

parental history of depression or a measurement of stable, elevated depressive symptoms. In

addition, we reviewed only studies that included a low-risk or nondepressed control group to

enable between-group contrasts. Only these studies can assess for specific depressive processing

biases. Related to this, we described only studies that assessed facial affect identification,

attention, or memory with information processing tasks. As such, we acknowledged, but did not

thoroughly review, findings derived from passive viewing tasks. This applied to certain

neuroimaging studies that more broadly implicate structures involved in facial affect processing.

Principally, we included research with adult samples, with the exception of studies of children

and adolescents known to possess greater risk for developing adult depression. In addition, we

summarize only studies with samples indicating experience with or risk for unipolar depression,

excluding studies in which a significant portion of the depressed sample met bipolar criteria.

Forty-seven studies met inclusion criteria and are reviewed below.

Results

Identification of Facial Affect

One’s ability to identify facial affects may be normal, generally impaired, or specifically

biased/impaired. General impairment implies decreased accuracy in identifying an array of

different facial affects and may be a facet of more pervasive cognitive deficits. As such, general

impairment might decrease one’s understanding and interaction skill in a wide variety of social

Processing Biases 29

situations. In contrast, specific impairment refers to insufficiently recognizing select affects;

identification of most affects is normal. Specific bias can refer to abnormally increased

identification of select affects. Also, consistently misinterpreting neutral affect as negative and

positive affect as neutral would constitute a negative cognitive bias. Specific bias or impairment

might impact one’s social understanding and behavior in certain situations but not others.

Normal identification. There is substantial evidence suggesting that facial affect

identification in depression susceptible groups can be normal (affect identification studies are

summarized in Table 1). Many depressed and dysphoric samples have shown unimpaired,

unbiased identification of unambiguous, prototypical positive and negative facial affects (e.g.,

Archer, Hay, & Young, 1992; Beevers, Wells, Ellis, & Fischer, 2009; Deldin, Keller, Gergen, &

Miller, 2001; Frewen & Dozois, 2005; Gaebel & Wolwer, 1992; Gollan, Pane, McCloskey, &

Coccaro, 2008; Gollan, McCloskey, Hoxha, & Coccaro, 2010; Leppӓnen, Milders, Bell, Terriere,

& Hietanen, 2004; Milders, Bell, Platt, Serrano, & Runcie, 2010). These results have usually

emerged with longer stimulus presentations, which allow more time for considering responses.

General impairment. Several other studies have reported general affect identification

impairments in depressed groups (Feinberg, Rifkin, Schaffer, & Walker, 1986; Persad & Polivy,

1993; Surguladze et al., 2004; Weniger, Lange, Ruther, & Irle, 2004). Studies reporting general

deficits have been somewhat methodologically distinct from studies reporting no between group

differences or specific depressive biases/impairments. First, studies reporting a general deficit

have more consistently included depressed inpatients. Depressed inpatients are more likely than

depressed outpatients or dysphoric community members to exhibit pervasive cognitive deficits,

such as slowed processing and impaired concentration and memory, which also correlate with

facial affect perception deficits (Csukly et al., 2011). This is consistent with findings that more

Processing Biases 30

acute levels of distress are associated with decreased ability to discriminate between emotions

(van Marle, Hermans, Qin, & Fernández, 2009). Possibly related to this, more genetically-

mediated depression, which tends to be more pernicious (McGuffin, Katz, Watkins, &

Rutherford, 1996), appears linked to attenuated amygdala responses to facial affect

(Wolfensberger et al., 2008). Increased baseline amygdala activity coupled with phasic

unresponsiveness to facial expressions could undermine affect discrimination. Second, some

studies reporting general deficits have presented participants with significantly fewer trials-per-

affect (e.g., 1-3 trials x 7-8 affects; Feinberg et al., 1986; Persad & Polivy, 1993; Weniger et al.,

2004) than studies reporting valence-specific differences have presented (e.g., 15-50 trials x 3-7

affects). Fewer trials would lead to comparatively lower power to detect any simple main effects

emerging from a group-by-affect interaction. As such, Feinberg et al. (1986) only assessed for

general impairment in identifying affects. Although Persad and Polivy (1993) did test for

valence-specific effects, visual analysis of their data suggests that specific difference trends

might have reached significance with greater power. Third, some studies reporting general

deficits have used briefer stimulus presentations (e.g., 100 ms, 500 ms; Feinberg et al., 1986;

Surguladze et al., 2004). In the case of depressive cognitive slowing, briefer presentations might

impede sufficient elaborative processing, impairing affect discrimination (Cooley & Nowicki,

1989). Complementary with this explanation, specific depressive cognitive biases typically

emerge only when longer stimulus presentations enable post-automatic elaborative processing

(Bradley et al., 1997). Thus, shorter presentations, fewer trials, and increasing depressive

severity may help explain why general facial affect processing deficits have been found in some

depressed groups.

Processing Biases 31

Specific impairments/biases. Still other studies have found impairments or biases in

identifying specific affects in depressed and dysphoric groups. Wright et al. (2009) found that

depressed women were less accurate than nondepressed women in discriminating brief

presentations of sad and fearful facial affects, but were not different in discriminating happy and

angry affects. Similarly, Ridout, Noreen, and Johal (2009) found that a group with naturally

occurring dysphoria showed impaired categorical identification of sad and neutral faces. This

second result was conceptually consistent with two previous findings. First, depression has been

associated with less accurate identification of neutral faces (Leppӓnen et al., 2004). Second,

although moderate depression can be linked to more sensitive identification of sadness (Gollan,

et al., 2010; Milders et al., 2010), acute levels of negative affect have been associated with less

accurate discrimination of sadness (Gur et al., 1992). This latter finding could be due to

stimulus-evoked hypoactivation of brain regions responsible for attentional control, evaluation of

social reward and punishment contingencies, and emotion regulation (e.g., caudate,

hippocampus, and orbitofrontal and dorsolateral prefrontal cortices, as identified by fMRI; Lee et

al., 2008). Seemingly in contrast, Hale (1998) reported that depressed outpatients rated higher

levels of sadness in several nonambiguous-affective categories of schematic faces. However,

these four findings may be complementary and not contradictory. Evidence suggests that

increased depression severity may be related to decreased specificity in differentiating between

sad and neutral affects and a generalized over-sensitivity to perceive sadness across the spectrum

of negative affective expressions (van Marle et al., 2009).

Indeed, there is relatively clear evidence that depression, naturally occurring dysphoria,

and pharmacologically induced dysphoria are associated with abnormal interpretation of subtle

or ambiguous facial affect (e.g., Gollan et al., 2010). In comparison to healthy control groups,

Processing Biases 32

depressed and dysphoric groups have exhibited lower sensitivity in identifying happy-neutral

facial affect (Gur et al., 1992; Joormann & Gotlib, 2006; Yoon, Joormann, & Gotlib, 2009).

Similarly, LeMoult and colleagues (2009) found that a formerly depressed group primed with sad

mood was less sensitive to lower intensities of happy facial affect than a never depressed control

group. Conceptually consistent with these results, depression has been associated with an

attenuated right posterior N200 ERP component during the identification of positive facial affect

but not positive words (Deldin et al., 2000). The location of this attenuated N200 for happy faces

suggests reduced processing in the right parietotemporal cortex and fusiform face area,

consistent with Heller’s regional specialization model of depressive cognition. These regions are

more specialized for visuospatial processing and might be less likely to show abnormally

decreased response to positive words, because words require only basic right-posterior visual

processing (Cohen et al., 2000). Subcortically, depression has been linked to attenuated

amygdala reactivity to subliminally presented positive facial affect (Suslow et al., 2010).

Supported neurobiological reward-motivation models indicate that insensitivity to reward

incentive stimuli, such as smiling faces, would stifle motivational states that drive social

approach behaviors and reinforcement attainment (Depue & Collins, 1999).

Depression has also been associated with increased sensitivity to neutral-sad and neutral-

fearful subtle affects (Beevers et al., 2009). Possibly related to this, Merens and colleagues

(2008) found that a remitted formerly depressed group identified neutral-disgusted faces faster

following acute tryptophan depletion. This procedure temporarily reduces available serotonin

and can induce a transient relapse of depressive symptoms (also see Surguladze et al., 2010

regarding increased psychophysiological reactivity to disgust faces in depressed group).

Additionally, ATD was linked to impaired specificity in identifying ambiguous fearful affect in

Processing Biases 33

this group. However, the sample showed no post-ATD changes in processing ambiguous happy,

sad, or angry faces. While these null results cannot be meaningfully interpreted, the authors

posited a mood-congruency effect for disgust. Specifically, they suggested that participants’

more efficiently processed disgusted faces due to the recent unpleasant gustatory experience of

consuming a tryptophan depleting drink.

Other studies have evoked biased responding through forced-choice paradigms in which

participants view neutral or ambiguous faces and must provide an affect label or intensity rating.

For example, Gollan and colleagues (2008) found that a depressed group interpreted neutral

faces as sad significantly more than a nondepressed group. Likewise, Hale (1998) reported that a

depressed group perceived more sadness in ambiguous schematic faces than controls.

Interestingly, Raes et al. (2006) found that the perceived intensity of negative facial affect across

a set of schematic faces significantly correlated with endorsed rumination. The neural

instantiation of negative interpretive bias is somewhat speculative. An intriguing possibility is

that this bias could occur due to the right amygdala remaining activated from previously viewed

negative facial affect. Two studies have shown that increased amygdala reactivity to

unconsciously viewed negative faces predicts negatively biased interpretations of subsequent

consciously viewed neutral faces (Dannlowski et al., 2007a, b).

Correlates of affect identification performance. Facial affect identification performance

appears to be related to the course of depressive episodes as well as shorter-term mood changes.

For example, perceiving greater levels of negative affects, such as disgust, fear, rejection, and

particularly sadness in ambiguous facial expressions has been found to predict a more prolonged

course (Geerts & Bouhuys, 1998; Hale, 1998; but see also Bouhuys, Geerts, Mersch, & Jenner,

1996). Further, continuing to perceive greater levels of negative facial affect into remission has

Processing Biases 34

been found to predict greater risk of relapse (Bouhuys, Geerts, & Gordijn, 1999). Additionally,

investigators have described how negative biases in facial affect interpretation can be

manipulated pharmacologically and how remission of such biases may serve as reliable

indicators of treatment outcome (Merens, Willem Van der Does, & Spinhoven, 2007; Venn,

Watson, Gallagher, & Young, 2006). However, other research has suggested that mood

congruent facial affect interpretation shifts can be induced in healthy individuals by a negative

mood prime. Some of this research has produced results similar to those found with depressed

groups (Bouhuys, Bloem, & Groothuis, 1995), implicating the influence of current mood state on

cognition. On the other hand, Ridout, Noreen et al. (2009) provided evidence that naturally

occurring dysphoria and induced negative mood are associated with opposite affect identification

patterns. Specifically, their findings suggested that natural dysphoria may impair accurate

identification of sadness, while transient induced sadness may facilitate sadness recognition.

Abnormal facial affect identification may also correspond with schemas indicative of

social maladjustment (Reeves & Taylor, 2007; Young, 1990). To examine this possibility,

Csukly and colleagues (2011) had depressed inpatients complete a facial affect identification task

and the Young Schema Questionnaire, which assesses for core cognitions related to unmet needs.

These authors found that poor recognition of happiness correlated with cognitive themes of

impaired autonomy and social disconnection, poor recognition of sadness correlated with themes

of impaired interpersonal limits and social disconnection, and poor recognition of fear also

correlated with themes of impaired autonomy. The authors also noted a negative correlation

between psychiatric distress levels and affect discrimination performance, implicating possible

influences among depression severity, facial affect identification, and dysfunctional perspectives

on negotiating social environments.

Processing Biases 35

Evaluative summary. Patterns in facial affect identification findings are intertwined

with methodological variables such as stimulus affect ambiguity, stimulus presentation duration,

and sample depressive severity. Eight of 10 studies of depression susceptible groups have shown

intact, unbiased identification of unambiguous positive or negative affects presented for long

durations (but see Ridout, Noreen et al., 2009). Presumably, some depressed individuals

effectively identify static, prototypical affect in dyadic interactions. However, research suggests

that depression may also confer sensitivity to subtle sadness (found in 4 of 6 relevant studies)

and fear (in 1 of 4 studies), insensitivity to subtle happy affect (in 4 of 6 studies), and with acute

clinical severity, less accurate affect discrimination (found in 3 of 4 inpatient samples). In more

complex social interactions, displayed affects may be dynamically brief, subtle, and problematic

for depressed individuals. Such displays could overtax slowed evaluative processing resources,

leading to incomplete or mistaken perceptions. Ambiguous affective displays might evoke

negative cognitive interpretive biases. In this way, depressotypic cognitive deficits and cognitive

biases could both alter identification of facial affect. In practice, depression prone individuals

may overestimate the intensity of negative affect in others’ facial expressions and may

insufficiently differentiate subtle affects.

Importantly, depressotypic affect identification has been linked to rumination, prolonged

course, and increased relapse risk (Bouhuys et al., 1999). Although abnormal facial affect

identification has yet to predict new onsets of depression, presented research suggests that it

could be part of a theoretically viable vulnerability factor or maintenance factor. It remains

unclear whether affect identification biases or impairments are discrete trait or state effects.

Several important propositions emerge from the reviewed studies. First, findings of

depressive interpretive biases are conceptually consistent with cognitive models of depression,

Processing Biases 36

which implicate negative construal of incoming information in the development and maintenance

of depression (e.g., Beck, 1967). Second, regarding interpersonal theories (e.g., Coyne, 1976;

Lewinsohn, 1974), stable negative interpretation of others’ default, modal facial expression—

neutral affect—could shift one’s construal of social environments to be disproportionately

unpleasant. Similarly, impaired facial affect decoding presumably could lead to ineffective social

conflict resolution and confusing, dissatisfying social interactions (Csukly et al., 2011). If so,

impaired affect identification might contribute to deficient social skills, interpersonal rejection,

and asocial behavior (Depue & Collins, 1999; Greimel et al., 2010; Leppӓnen & Hietanen, 2001;

Meyer & Kurtz, 2009; Santos, Silva, Rosset, & Deruelle, 2009). However, possible connections

between abnormal facial affect processing and social dysfunction have not been sufficiently

studied in depression vulnerable groups. In support of the cognitive-interpersonal model,

dysfunctional social schemas that have been linked to affect identification difficulties (Csukly et

al., 2011) also appear related to adverse interpersonal experiences in childhood (e.g., Lumley &

Harkness, 2007). Moreover, Pollak and colleagues (2000) have established a connection between

childhood neglect (a depression risk factor) and impaired facial affect discrimination.

Additionally, the tendency to interpret ambiguous facial expressions as sad, disgusted,

fearful, or rejecting (Hale, 1998) is conceptually consistent with a depressive social risk-averse

strategy (Allen & Badcock, 2003). Accordingly, individuals in a dysphoric mood less frequently

trust others’ facial affect as genuine and they more accurately detect deception (Forgas & East,

2008a; 2008b; Lane & DePaulo, 1999). This might steer them away from unpredictable

interactions and peer victimization. In contrast, depressotypic interpretation biases that endure

even when depression has remitted (e.g., Bouhuys et al., 1999; Milders et al., 2010) contradict

the premise that the social risk-aversion strategy is mood state dependent. Lastly, at a practical

Processing Biases 37

level, studies that enroll depression vulnerable groups to examine attention and memory for

facial stimuli will need to account for interpretive biases as a potential confounding variable.

____________________________

Insert Table 1 approximately here

____________________________

Selective Attention for Facial Affect

Attention to sad affect. As catalogued in Table 2, several studies have shown a

depressive bias toward sad faces.

Depressed samples. Gotlib, Krasnoperova et al. (2004) examined whether attentional

biases to specifically depressive facial stimuli would be associated with depression and not

anxiety. Sample groups with major depression, with generalized anxiety disorder, or without

psychiatric illness completed a dot-probe task that included happy, sad, angry, and neutral facial

stimuli. At stimulus presentation durations thought to evoke attention engagement processing

(e.g., 1000 ms), the depressed group showed an attentional bias exclusively to sad faces. By

comparison, the anxious and healthy comparison groups attended equally across affects. Thus,

findings supported the content specificity hypothesis of depressive attentional bias toward

depressive information (Beck, 1976; Ingram et al., 1998). Gotlib, Kasch, et al. (2004) replicated

these specificity findings in depressed individuals, this time with a comparison group diagnosed

with social phobia. Subsequent replications provided additional evidence that an attention bias

for sad facial affect is found reliably and specifically in depression (Fritzsche et al. 2009;

Joormann & Gotlib, 2007). This conclusion is strengthened by the fact that these four studies

excluded individuals with comorbid anxiety disorders from their depressed samples—unlike

most studies examining facial affect processing and depression. Functional MRI evidence

Processing Biases 38

suggests that a depressive attentional bias toward sad affect could be supported by increased

activity in the right fusiform gyrus, left putamen, amygdala, and parahippocampal gyrus

(Surguladze et al., 2005; Suslow et al., 2010; Victor et al., 2010).

In addition, research has begun to assess whether depression is associated with biased

inhibitory processing of facial affect, as this could influence selective attention and memory.

Goeleven, De Raedt, Baert, and Koster (2006) utilized a negative priming paradigm to compare

the inhibitory processing of currently depressed and healthy control groups. As is typical, the

control group demonstrated functional negative priming (e.g., happy prime distracters slowed

subsequent responses to happy probe targets) for both valences. In contrast, despite showing

normal negative priming on happy experimental probe target trials, the currently depressed group

lacked negative priming on sad experimental probe target trials. This finding suggests that

depression may be associated with deficient attentional inhibition of peripheral sad facial affect

in the social environment.

In contrast, two studies have found no depressive attentional bias. Mogg, Millar, and

Bradley (2000) had depressed individuals complete a facial dot-probe paradigm while

monitoring directional gaze, but neither reaction time nor eye movement data indicated any

attentional bias to sad faces. However, nearly all of the depressed individuals in the Mogg et al.

sample were experiencing comorbid generalized anxiety disorder, limiting the generalizability of

this particular finding. Karparova, Kersting, and Suslow (2005) investigated whether a depressed

group would exhibit delayed disengagement of attention from negative faces compared to a

nondepressed group. All participants completed the face-in-the-crowd task twice, corresponding

with the depressed individuals’ pre- and post-treatment time points. However, at both time points

there were no between-group differences in the ability to disengage attention from distracting

Processing Biases 39

backgrounds of negative faces. It is worth noting that the schematic faces in Karparova et al.

(2005) differed only by the ends of the mouth line, a coarse, low-level feature. Thus, it is

possible that more complex spatial features comprising true emotional faces could elicit stronger

sustained attention and provide more of a challenge for disengagement processing.

At-risk samples. Several studies have explored whether attentional biases to sad facial

affect exist in populations that are at-risk but not currently depressed. Joorman and Gotlib (2007)

compared the performance of formerly, currently, and never depressed participants on a facial

dot-probe paradigm without manipulating mood. Results showed that both currently and

formerly depressed groups selectively engaged attention with sad faces. Conversely, the healthy

control group shifted attention away from sad faces and oriented toward happy faces. This study

both replicated prior findings with currently depressed individuals (Gotlib, Kasch et al., 2004;

Gotlib, Krasnoperova et al., 2004) and uniquely showed evidence of a cognitive marker that

persists beyond remission from depression (see Fritzsche et al., 2009 for replication of Gotlib &

Joormann, 2007). This contrasts with most cognitive vulnerability research evidence that

depressive cognition emerges only within a depressed mood state (Scher et al., 2005).

In another study of an at-risk group, Hsieh and Ko (2004) explored whether increased

levels of trait-depression were associated with facial affect attentional biases. The study’s

college student sample completed a deployment of attention task and a self-report personality

inventory that assessed stable trait depression. A median split on trait depression formed two

groups for comparison. Because clinical interviews were not conducted, some high trait-

depressed participants might have met criteria for major depression. But given that the one-year

prevalence rate of major depression approximates 4.1% internationally (Waraich, Goldner,

Somers, & Hsu, 2004), a larger proportion of the above-median trait-depression group may have

Processing Biases 40

been experiencing less-severe dysthymia or dysphoria (Ingram & Hamilton, 1999), clinically

significant phenomena that portend greater risk of major depression. Although high trait-

depression participants attended more to sad faces than low trait-depression participants, this

effect was mostly driven by the tendency for low trait-depression participants to inhibit

attentional engagement with sad faces. Thus, the at-risk group lacked normal attentional

inhibition of others’ sad facial affect.

Goeleven and colleagues (2006) utilized a negative priming procedure to examine

attentional inhibition of facial affect in currently euthymic individuals with a history of recurrent

depression. In contrast to currently depressed and never depressed groups (reviewed earlier), the

formerly depressed group showed impaired inhibition for both sad and happy facial affect. This

surprising result might suggest that multiple depressive episodes sensitize one’s attention to

generalized facial affect in the social environment. An alternative speculation stems from the

finding that greater recurrence tends to correspond with increasingly heritable forms of

depression (Sullivan, Neale, & Kendler, 2000). More heritable forms of depression have been

associated with greater baseline amygdala activity, but less reactivity to affective stimuli,

including faces (Drevets, 2000; Wolfensberger et al., 2008). Such a profile could potentially be

genetically-mediated (e.g., MAOA-H risk allele) via decreased prefrontal-amygdala

connectivity, which would hinder adaptive feedback and regulation in response to emotional

stimuli (Dannlowski et al., 2009). In a negative priming task, decreased prefrontal-amygdala

connectivity might lead to insufficient removal of irrelevant affective content from working

memory. This deficiency might be most prominent in the genetically vulnerable person who is

experiencing depression, when sad distracters would be mood-relevant and unlikely to elicit

robust attentional inhibition. When the same person is not depressed, sad content is no longer

Processing Biases 41

mood-congruent, so deficient inhibition might appear equivalent for sad and happy facial affect.

If Goeleven et al. (2006) represents a reliable effect, differing levels of recurrence and/or

heritability within depression susceptible samples might contribute to heterogeneous results in

studies examining potential depressive cognitive biases (see also Gilboa-Schechtman, Ben-Artzi,

Jeczemien, Marom, & Hermesh, 2004).

The aforementioned research has shown that attentional biases are not just correlates of

major depression, but these studies did not assess whether biases arise in advance of first

depressive onsets. In a study with this objective, Joormann, Talbot, and Gotlib (2007) explored

whether young, euthymic, never depressed daughters of mothers with a history of recurrent

depression would show negative attentional biases to facial affect, compared to girls of never

depressed mothers (high- and low-risk). Participants completed a sad mood prime and then a dot-

probe task consisting of happy, neutral, and sad facial stimuli. The high-risk group showed a bias

toward sad faces but not happy faces. In contrast, the low-risk group showed a bias toward happy

faces but not sad faces. Interestingly, Gibb et al. (2009) found that without a sad mood induction,

children of mothers with a history of depression avoided attending to sad faces but not happy or

angry faces. Avoidance was strongest in children with putative environmental stress-sensitivity

(5-HTTLPR S or LG allele carriers), whose elevated level of dysphoria correlated highly with

their mothers’. Notably, the at-risk sample included some formerly depressed children, endorsed

varying levels of current dysphoria, and was mixed-sex, all of which differed from Joormann et

al. (2007). Nonetheless, these studies indicate that when not acutely sad, some children of

depressed mothers try to regulate their affect by ignoring sad faces; when sad, their attention

might be drawn to sad faces and lack a potentially mood-enhancing bias toward happy faces. If

Processing Biases 42

research can demonstrate that these response patterns are influenced by suboptimal attachment,

such findings would support the cognitive-interpersonal model.

Attention to happy affect. Several studies have reported that depression susceptible

groups attend less to positive faces.

Depressed samples. Suslow and his collaborators have examined attentional capture of

facial affect via the face-in-the-crowd task. Suslow, Junghanns, and Arolt (2001) compared the

performance of clinically stabilized depressed inpatients and nondepressed individuals. These

investigators found that the patient group was significantly slower to orient and engage attention

with positive faces than their counterpart group, even after controlling for trait anxiety. To

examine potential treatment effects on this bias, Suslow et al. (2004) assessed face-in-the-crowd

performance of depressed patients (half of whom were diagnosed with comorbid anxiety

disorders) before and after six sessions of psychotherapy. The authors compared this depressive

performance profile with that of nondepressed individuals at parallel time points. Despite

evincing clinically significant symptom improvements, the comorbid depressed group (but not

the depressed group without comorbidity) more slowly detected positive faces than the

nondepressed group at both time points. Between depression and anxiety, only depression has

been reliably linked to deficits in serial, effortful processing (Hartlage, Alloy, Vázquez, &

Dykman, 1993), which is required for detecting happy faces (White, 1995). Thus, it is unclear

why the comorbid group showed slowed processing and the non-comorbid depressed group did

not. One explanation is that the comorbid group possessed more severe psychopathology (e.g.,

average BDI was 5 points greater than the non-comorbid depressed group), and severity is

related to impaired processing of positive affect. Consistent with this explanation, Gotlib, Kasch,

Traill, Joormann, Arnow et al. (2004) found that, within a depressed group, greater symptom

Processing Biases 43

severity was associated with greater attention away from happy faces. Fritzsche et al. (2009)

found a similar result in their depressed sample.

Reduced attentional processing should be measurable in corresponding neural activity. To

this end, Cavanagh and Geisler (2006) examined the parietal P300 ERP response of depressed

and nondepressed students on an oddball task that presented alternating blocks of rare happy and

fearful target faces interspersed among frequently presented neutral faces. The investigators

found that, compared to the control group, the depressed group showed reduced mean P300

amplitude to happy target faces but not to fearful ones. In line with this finding, neuroimaging

research has revealed links between depression and reduced cortical activity in parietotemporal

and prefrontal areas, as well as in the insula. Reduced attentional engagement could also result

from hypoactivity in subcortical structures that process socially rewarding stimuli. Depression

has been associated with reduced amygdala, hippocampus, putamen, and caudate activity in

response to viewed positive facial affect (see Domschke et al., 2008; Schaefer, Putnam, Benca,

& Davidson, 2006; Surguladze et al., 2005 fMRI studies).

Still, other research suggests that depression can be associated with the lack of a normal

positive attentional bias that would otherwise promote positive moods and social engagement.

Joormann and Gotlib (2007) found that a currently depressed group lacked the tendency shown

by a never depressed group to bias attention toward happy faces. Likewise, depressed groups

may lack the tendency shown by healthy groups to allocate greater neural processing resources to

happy faces than sad faces (Deldin et al., 2001; Deveney & Deldin, 2004; Victor et al., 2010). In

contrast, several studies have reported no difference between depressed and nondepressed groups

in attention to positive facial affect (Gotlib, Kasch et al., 2004; Gotlib, Krasnoperova et al., 2004;

Karparova et al., 2005; Mogg et al., 2000).

Processing Biases 44

At-risk samples. Groups at risk of depression have also shown less attention to positive

facial affect than low-risk groups. Two studies have indirectly examined whether dysphoria

might impact attention to mood-congruent or mood-incongruent affective faces. In Bradley and

colleagues (1998) study, high and low trait anxiety groups performed a dot-probe task that

included happy, threatening and neutral facial stimuli. Although the hypothesized bias toward

threatening stimuli was found in highly anxious individuals, dysphoria was found to be

correlated with a bias away from happy faces relative to neutral faces. Bradley and colleagues

(2000) replicated this result in sample grouped by low, medium, and high levels of state anxiety.

Additionally, at-risk individuals may lack a normal attentional bias toward positive facial

affect. Joormann and Gotlib (2007) found that a formerly depressed group lacked the positive

attentional bias that a never depressed group exhibited. Joorman, Gotlib, and Talbot (2007) also

found a parallel difference between never depressed girls with maternal depression history and

never depressed daughters of never depressed mothers. However, Hsieh and Ko (2004) found

that high-risk and low-risk groups were no different in processing positive facial affect.

One study suggests that at-risk groups may insufficiently filter affective information,

particularly happy affect, when they process others’ faces. Gilboa-Schechtman et al. (2004)

compared dysphoric and nondysphoric groups’ ability to focus on a nonemotional dimension of

facial expressions while ignoring the potentially distracting emotional dimension. Participants

completed a Garner speeded-classification task in which they were asked to identify, quickly and

accurately, the gender of actors displaying happy, angry, and neutral affects. The degree to which

each emotion slowed gender-labeling was a measure of attentional interference. Results indicated

that the dysphoric group showed greater interference than the nondysphoric group for all affects,

Processing Biases 45

and showed greater interference from happiness than from anger. Therefore, some—but not all—

evidence suggests that depression susceptibility evinces reduced attention to positive faces.

Attention to angry and fearful affect. Theory and evidence suggest that depression

could be associated with attentional biases toward other negative affects, such as anger, fear, and

disgust (Allen & Badcock, 2003; Beevers et al., 2009).

Depressed sample. Leyman, De Raedt, Schacht, and Koster (2007) examined the

performance of currently depressed and nondepressed individuals on an exogenous cueing task

that used neutral and angry facial stimuli. Both groups showed facilitated early attention for

angry affect. However, the depressed group’s attentional engagement and maintenance with

angry faces was greater relative to neutral faces and relative to the nondepressed group, who

more rapidly shifted attention away.

At risk sample. When asked to view faces for 12 seconds each, a dysphoric group showed

broader visual attentional scanning of angry faces relative to other affective faces and relative to

a nondysphoric group (Wells, Beevers, Robinson, & Ellis, 2010). These results may or may not

correspond with depressive attenuated orbitofrontal activity in response to angry affect (Lee et

al., 2008). Conversely, several studies have indicated that dysphoric and depressed groups’

attention to angry facial affect was no greater than for other affects and indistinguishable from

nondepressed groups (Gilboa-Schechtman et al., 2004; Gotlib, Kasch et al., 2004; Gotlib,

Krasnoperova et al., 2004; Hsieh & Ko, 2004; Koster et al., 2006). Similarly, depressed

individuals have shown normal attentional processing of fearful facial affect.

Evaluative summary. Attention biases to facial affect are relatively reliable correlates of

major depression, and cognitive neuroscience has begun to illuminate possible

neuropsychological mechanisms of these biases (Cavanagh & Geisler, 2006). Three of four

Processing Biases 46

studies examining the orienting phase of attention (e.g., stimulus presentations of 500 ms) have

shown decreased orienting to happy faces in depression prone groups. In five studies, these

groups have shown biases away from positive affect toward other affects. In two other studies

depression prone groups have lacked a bias toward positive affect that was observed in healthy

control groups. When biases away from positive affect have been found, they have consistently

occurred in groups experiencing stable dysphoric mood. In contrast, a lack of a positive bias has

been observed in one currently depressed group, in one never-depressed group with familial

depression risk, and in one nondysphoric formerly depressed group (Joormann & Gotlib, 2007;

Joorman et al., 2007). If more research finds that nondysphoric at-risk individuals lack a

normative attention bias toward positive facial affect, such a processing tendency might reflect

an enduring trait marker of risk.

Six of nine studies that assessed attentional engagement and maintenance (e.g., 1000 ms

presentations) found greater maintained engagement with sad faces in depressed groups.

Deficient inhibition of sad irrelevant facial affect in the social environment might contribute to

such a depressive attentional bias (Goeleven et al., 2006; Hsieh & Ko, 2004). Null results have

emerged from a smaller depressed sample (Mogg et al., 2000) and from a study that tracked

visual attentional gaze (Wells et al., 2010), while a bias away from sad affect was found in an at-

risk child sample (Gibb et al., 2009). Although the content specificity hypothesis has been

generally supported, bias toward angry affect was found in two of seven relevant studies

(Leyman et al., 2007; Wells et al., 2010). Thus, attentional biases to negative facial affect have

been frequently found in depression. However, only studies of at-risk samples can examine

whether attentional biases precede depressive onsets and impact the etiology of depression.

Processing Biases 47

Results with depression-vulnerable groups mostly match results found with currently

depressed groups. In particular, at-risk groups have shown deficient orienting to happy affect in 2

of 3 studies and greater attentional maintenance with sad affect in 4 of 6 studies. Importantly,

longitudinal evidence suggests that attention deficits for positive facial affect can persist even

after depression remits (Suslow et al., 2004). Further, depression-susceptible groups appear to

insufficiently inhibit attention to viewed sad (Goeleven et al., 2006; Hsieh & Ko, 2004) and

happy facial affect (Gilboa-Schechtman et al., 2004; Goeleven et al., 2006). Perhaps due to the

emotional discomfort others’ sadness might cause them (Persad & Polivy, 1993), some

vulnerable children direct their attention away from sad faces (Gibb et al., 2009).

Relevant to cognitive theories of depression, findings from studies that have examined

attention with emotional facial stimuli largely parallel studies that have used verbal stimuli.

Depression susceptible groups tend to allot greater attention toward negative words (e.g.,

Bradley, Mogg, & Lee, 1997; Karparova, Kersting, & Suslow, 2007), to lack mood-buffering

avoidance or inhibitory processing of negative words (e.g., Joormann, 2004; MacLeod,

Mathews, & Tata, 1986), and to exhibit deficient engagement with positive words. Although

depression has been linked to impaired disengagement from negative words (Koster et al., 2005),

impaired disengagement from sad faces has not been reported. Also, it remains unclear whether

past depression, in the absence of dysphoric mood, is linked to either an attentional inhibition

deficit for facial affect in general or specifically for negative affect. Overall, however, studies

using affective facial stimuli have provided complementary support for cognitive theories.

Interestingly, attending to negative faces can instigate a depressive mood-maintaining

internal monologue. In a clever study, Frewen and Dozois (2005) asked women to: (a) identify

the affect of each presented happy, sad, fearful, disgusted, or angry face; (b) imagine they had

Processing Biases 48

just seen the person; and (c) characterize their own subsequent automatic thoughts. Though both

groups accurately identified all affects, the dysphoric women were more likely than

nondysphoric women to blame themselves for others’ negative affects and to ascribe external

causes for others’ positive affect. Also, dysphoric women more often interpreted others’ negative

affects to reflect a negative evaluation of them. Lastly, after viewing sad, angry, disgusted, or

happy faces, dysphoric women were more likely to endorse negative thoughts about themselves.

Possibly related, Fritzsche et al. (2009) found that depression prone groups who preferentially

attended to sad facial affect also recalled more negative self-referent adjectives they had

previously endorsed, compared to a never depressed group who showed neither pattern. In

addition, Persad and Polivy (1993) found that depressed and dysphoric groups endorsed elevated

sad emotional responses to negative and positive affective faces they viewed. Thus, greater

attention to negative facial affects might evoke immediate heightened depressotypic cognitive

and emotional responses, possibly giving rise to the kind of ruminative processing that prolongs

depressed moods (e.g., Nolen-Hoeksema et al., 1993). Although depressotypic attentional biases

have appeared in never depressed at-risk individuals (Joormann et al., 2007), it is unknown

whether these biases predict future depressive onsets, as the combination of negative cognitive

style and stress-reactive rumination has (Robinson & Alloy, 2003).

The reviewed research also highlights plausible connections between attention findings

and interpersonal, cognitive-interpersonal, and social risk theories, which warrant further

empirical testing. Regarding interpersonal theories, attention bias toward others’ sad affect could

serve as a particularly aversive consequence (Persad & Polivy, 1993) that inhibits assertive social

behavior or as a sobering reminder to cling to close relationships (Barnett & Gotlib, 1988;

Lewinsohn, 1974). Per cognitive-interpersonal theory, non-depressed children who have

Processing Biases 49

experienced depressive parenting have shown abnormal attention to sad affect (Gibb et al., 2009;

Joormann et al., 2007). Similarly, heightened emotional reactivity and dysphoria-promoting

patterns such as self-criticism and perceived negative evaluation by others can originate from

suboptimal interpersonal experiences (Hammen & Gotlib, 1993). Consistent with the social risk

model, a depressive tendency to assume blame for others’ misery and to take no credit for others’

happiness (e.g., Frewen & Dozois, 2005) seems to exemplify a mode of low perceived social

investment potential (i.e., high burden and low value to others; Allen & Badcock, 2003). In

tandem, vigilant attention to others’ sad, angry, and disgusted affect could regularly restore a

depressive cognitive-emotional mode, which presumably guides risk-averse behavior.

Irrespective of theoretical perspective, the reviewed attentional research suggests that distinct

patterns of thought and emotion occur when depression susceptible individuals encounter

affective expressions in their daily lives.

___________________________

Insert Table 2 approximately here

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Memory for Facial Affect

Studies indicate that depressed and depression susceptible groups can show abnormal

recognition memory for faces displaying sadness, happiness, anger, or fear.

Memory for sad affect.

Depressed samples. Ridout et al. (2003) asked depressed and nondepressed individuals to

complete a facial emotion identification task and then a delayed recognition memory task (see

Table 3 for listing of memory studies). Although both groups categorized the emotional faces

similarly at presentation, groups differed in the types of faces they recalled. The depressed group

Processing Biases 50

recognized more sad faces and fewer happy faces compared to neutral faces. The nondepressed

group showed better memory for happy faces and worse memory for sad faces.

In another study that explored recognition memory for faces, Gilboa-Schechtman et al.

(2002) included a group with comorbid depression and anxiety, a group with non-comorbid

anxiety, and a group without psychiatric illness. Participants first completed an incidental

encoding task with sad, angry, happy, and neutral facial expressions (i.e., rating whether they

would like to meet the photographed person). On this task, both patient groups reported less

willingness to meet people who displayed angry or sad affect than the control group. Later,

participants were asked to identify only the faces of people they had seen previously, although

these familiar people displayed different affects in the recognition phase than in the encoding

phase. On this task, the comorbid group recalled more actors who had previously conveyed

angry and sad faces compared to happy and neutral facial expressions. The control group

exhibited the opposite pattern of memory bias.

Ridout, Dritschel et al. (2009) also examined whether major depression would be

associated with memory biases for sad facial affect following an incidental encoding task.

During the encoding phase, depressed and healthy never depressed participants viewed affective

facial expressions and identified each actor’s gender. Next, participants completed a recognition

memory test. Contrary to initial predictions, the depressed group showed no biased recognition

of sad faces. Based on these findings, mood congruent memory biases for affective faces might

only emerge if emotion is processed explicitly during encoding (Ridout, Dritschel et al., 2009).

Interestingly, this rationale could also explain results from Gilboa-Schechtman et al. (2002).

Specifically, when considering whether one wants to meet a person in a photograph, she or he

might explicitly process facial affect cues for friendliness and dominance. As Gilboa-

Processing Biases 51

Schechtman et al. (2002) found, this encoding task should evoke in depressed individuals a

subsequent bias for recognizing more persons who had displayed sad affect. In contrast,

discriminating gender requires no explicit processing of facial affect and should evoke no

memory bias.

Deldin et al. (2001) utilized ERP techniques to investigate links between depression and

recognition memory of affective faces. These investigators compared parietal P300 amplitudes of

depressed and nondepressed individuals during incidental encoding and subsequent recognition

of positive and negative faces. The authors found that the nondepressed group showed greater

P300s during encoding and attenuated P300s when recognizing positive faces compared to

negative faces. In contrast, the depressed group showed no differential processing allocation

between positive and negative faces or between encoding and recognition memory phases.

Therefore, relative to the pattern of nondepressed individuals, depressed individuals tended to

allocate lesser processing resources to encoding positive expressions and greater resources to

recognizing negative expressions. Interestingly, the study did not find behavioral differences in

recognition memory. This could indicate that the brain can recruit compensatory neural resources

to minimize impairment in performance (Drummond et al., 2001). Lastly, the P300 ERP

component can provide a snapshot of attentional processing (e.g., context updating; Donchin &

Coles, 1988) during encoding and retrieval. However functional neuroimaging, which can track

and measure ongoing elaborative processing, will likely provide important additional knowledge

about depressive memory biases.

At-risk samples. Three studies have examined memory for sad facial affect in dysphoria.

Hsieh and Ko (2004) asked high and low trait depression participants to identify only affective

faces they had seen previously in an attention task. On this recognition memory task, the high

Processing Biases 52

trait depression group showed significantly greater memory strength for sad faces than the low-

risk control group. No between-group memory differences were found for angry, happy, or

neutral faces.

Jermann et al. (2008) utilized a remember/know/guess paradigm to compare dysphoric and

nondysphoric groups’ performance on recognizing the identity and affect of previously presented

faces. Participants first viewed images of sad and happy facial expressions of actors. Later in the

recognition phase, they viewed neutral expressions of old and new actors, and were asked to

identify only those actors who were presented in the encoding phase. Dysphoric individuals

showed a greater proportion of accurate ―remember‖ responses for actors presenting sad facial

expressions than nondysphoric individuals. In this study, encoding was intentional, and the only

recall bias to emerge was conscious recognition of both affect and identity.

Recently, Ridout, Noreen et al. (2009) conducted experiments examining possible mood

congruent memory biases for facial affect in naturally occurring dysphoria and in induced

moods. In both experiments, participants completed a delayed recognition memory task

following an incidental encoding (affect identification) task that included sad, happy, and neutral

facial stimuli. In the first experiment, the naturally dysphoric group recognized significantly

more sad faces relative to neutral and happy faces. They also recognized significantly more sad

faces and fewer happy faces than the nondysphoric group. In the second experiment, researchers

induced negative or positive mood in healthy, euthymic participants and examined their

subsequent information processing. Parallel to the dysphoric group in the first experiment, the

negative mood-induced group recognized significantly more sad faces, relative to happy and

neutral faces, and in comparison to the positive mood-induced group. Unlike the between-group

contrast in the first experiment, the negative mood group did not recognize fewer happy faces

Processing Biases 53

than the positive mood group. Thus, naturally occurring and induced mood may affect memory

differently, an important nuance in the conceptualization of mood-congruent cognition.

Memory for happy affect.

Depressed samples. Depression can also be characterized by decreased memory for

positive facial affect. For example, Ridout et al. (2003) found that a depressed group recognized

fewer happy faces than a nondepressed group. The depressed group also recognized fewer happy

faces than neutral or sad faces. In other studies, depressed groups have shown no bias, while

their nondepressed counterparts showed preferentially increased memory for positive faces

(Gilboa-Schechtman et al., 2002; Ridout et al., 2003). Aligned with these results, Deldin et al.

(2001) found that a nondepressed group allocated more cognitive resources to encoding happy

faces and less to recalling them. Their depressed group showed no differential allocation between

encoding and recall. Theoretically, greater encoding promotes more efficient retrieval. In this

way, depression could reduce encoding of positive affect and thereby limit its accessibility in

stored memory.

At-risk samples. Thus far, a memory deficit for positive facial affect has not been found in

groups susceptible to depression (Hsieh & Ko, 2004).

Memory for angry and fearful affect.

Depressed sample. Although an increased memory bias for angry facial affect was found

in a group with comorbid depression and anxiety (Gilboa-Schechtman et al., 2002), no findings

from non-comorbid depressed samples have been reported.

At-risk samples. Groups with above average trait depression or parental history of

depression have shown no memory bias for angry faces (Hsieh & Ko, 2004; Pine et al., 2004).

However, in one study, a stable dysphoric group more accurately recalled angry faces after a 15-

Processing Biases 54

minute delay than a nondysphoric group (Wells et al., 2010). Importantly, broader visual

attention during prolonged face viewing (i.e., incidental encoding) mediated this effect.

However, with only a brief encoding period, a dysphoric group held angry faces in working

memory no more strongly than a nondysphoric group (Noreen & Ridout, 2010). As such,

attentive, elaborated encoding may be necessary to cultivate long-term memory biases for angry

faces. Regarding fearful affect, Pine and colleagues found that a formerly depressed adolescent

group showed reduced memory for fearful faces compared to a never depressed group.

Evaluative summary. Depressed and depression susceptible groups have recalled more

sad faces than control groups in three studies and have recalled more sad faces than happy faces

in two other studies (but see Deldin et al, 2001; Ridout, Dritschel et al., 2009; Wells et al., 2010

for null findings). These biases might be partially mediated by relatively increased or decreased

cortical processing, respectively (e.g., Cavanagh & Geisler, 2006; Deldin et al., 2000). Also,

comorbid anxiety or prolonged attentional engagement with others’ faces may lead to a memory

bias for angry affect (e.g., Gilboa-Schechtman et al., 2002; Wells et al., 2010).

Current emotional facial expression recognition memory findings in depressed and

depression vulnerable groups fit most strongly with cognitive models of depression, which

predict greater memory access to depressive information. By extension, these findings are also

consistent with the more broadly developed literature using verbal information (e.g., words,

sentences, idea units) on explicit and implicit memory tasks. Specifically, explicit recall and

implicit priming biases for depressive verbal information have been found in depressed and

depression vulnerable individuals (Matt et al., 1992; Watkins, 2002). Additionally, most results

have been in line with the content specificity hypothesis (e.g., Hsieh & Ko, 2004; Jermann et al.,

2008; Ridout et al., 2003; Ridout, Noreen et al., 2009). However, two of five studies that have

Processing Biases 55

examined possible memory biases for angry affect have found them. These findings partially

support the social risk hypothesis. Presumably, memory for socially aversive people, when

paired with a motivation to avoid such people (e.g., Gilboa-Schechtman et al., 2002), would aid a

risk-averse strategy.

Important questions relevant to cognitive theories remain to be investigated. First, do at-

risk individuals in a nondysphoric or dysphoric mood exhibit memory biases for mood-relevant

affective facial expressions? Second, are there differential effects of incidental and conscious

encoding of facial affect on memory storage and implicit or explicit retrieval? Because cognitive

schemas are thought to have limited conscious accessibility, the current collection of studies

examining explicit memory retrieval represents only a partial empirical exploration of cognitive

theories. Preliminary results suggest that explicit processing may be necessary for explicit

recognition memory biases to emerge (e.g., Gilboa-Schechtman et al., 2002; Ridout et al., 2003;

Ridout, Dritschel et al., 2009). However, it will be important for future research to examine

depressive implicit memory for relevant facial affect following explicit encoding of affect. Such

an approach may increase external validity, as conscious awareness and external motivation may

be absent when people access affective associations in naturalistic social settings.

____________________________

Insert Table 3 approximately here

____________________________

Discussion

Although depressive cognition has historically been investigated using verbal stimuli,

others’ facial affect should be particularly important in depression (Allen & Badcock, 2003;

Processing Biases 56

Gotlib, Krasnoperova et al, 2004). We have described information processing studies that use

emotional facial stimuli and have begun to evaluate findings within the frameworks of cognitive,

interpersonal, cognitive-interpersonal, and social risk models of depression. The literature

indicates that depression susceptible groups often show attentional, interpretive, and memory

biases regarding depression-relevant facial affect.

Affect identification

Depressed individuals tend to recognize static, unambiguous facial affect without bias

(e.g., Segrin, 2001), but they tend to interpret ambiguous affective facial expressions more

negatively than do nondepressed individuals (e.g., Gollan et al., 2008). Interpretive biases may

be an important factor in the assessment of treatment outcome (Merens et al., 2007; Venn et al.,

2006) and risk of relapse, as some symptomatically remitted individuals might continue to

perceive social interactions negatively and be more vulnerable to relapse (Bouhuys et al., 1999).

More-severe depression can be characterized by increased sensitivity to various negative affects

but decreased specificity in discriminating among them. Such a pattern has been associated with

increased rumination and decreased prosocial behavior (Marsh et al., 2007; Raes et al., 2006).

Importantly, we did not encounter this sensitivity/specificity pattern in depression research that

used emotional word stimuli or non-facial picture stimuli.

Attention

Depression and depression vulnerability have frequently been associated with early

deficits in orienting to happy faces, which may persist even after depressive symptoms remit

(Suslow et al., 2004). In addition, depression susceptibility has been reliably linked to increased

attentional engagement with sad faces. Depression may also be characterized by a deficit for

inhibiting attention to negative facial emotion (Goeleven et al., 2006). Future research should

Processing Biases 57

evaluate the hypothesis that abnormal cognitive inhibition influences biased attention and

memory processing (Linville, 1996). Also, comorbid depression and anxiety may be

characterized by increased attention and memory for angry affect (Gilboa-Schechtman et al.,

2002; Leyman et al., 2007). This fits with evidence that anxiety confers sensitivity to threatening

information (Bradley et al., 1998; Fox, Russo, Bowles, & Dutton, 2001).

Attention findings are mostly consistent with affective verbal research supporting

cognitive models of vulnerability (Scher et al., 2005). However, select attentional findings with

verbal stimuli have not been replicated with facial stimuli (Karparova et al., 2005), vice versa

(Deldin et al., 2000). Similarly, whereas depressotypic biases have been proposed and found to

occur at later elaborative stages of processing (Bradley et al., 1997; Williams et al., 1997),

cognitive neuroscience evidence indicates that attention and interpretation biases for facial affect

begin during pre-conscious, automatic processing (e.g., Csukly et al., 2011; Suslow et al., 2010).

Studies that examine both verbal and facial affect processing in a single depressed sample are

rare and are needed to help answer an important question: Do depressotypic cognitive biases

generalize across modalities, or can biases for facial affect be dissociated from biases for

affective words? Although extant data implicate non-identical neurocognitive substrates (Deldin

et al., 2000; Vanderploeg et al., 1987), more empirical comparisons are needed.

Memory

Depression and depression susceptibility has been frequently linked to increased recall of

sad facial affect. In tandem, memory for happy affect is often abnormally deficient in depressed

groups (Gilboa-Schechtman et al., 2002; Jermann et al., 2008; Ridout et al., 2003). Less

frequently, depression susceptible groups have shown increased recognition memory for angry

Processing Biases 58

affect. Abnormal attentional processing of facial affect may modulate memory encoding and

subsequent access (Deldin et al., 2001; Ingram, 1984; Wells et al., 2010).

Integrating perspectives

Studies have assessed cognitive models of depression nearly by default, even though

findings are also relevant to the other theories of interest. Accordingly, substantial evidence

supports hypothesized cognitive processing biases. Related to this, processing facial affect can

promote negative thought patterns putatively generated from depressive schemata (e.g., Frewen

& Dozois, 2005; Fritzsche et al., 2010). Although many facial affect processing studies support

the idea of cognitive reactivity in negative mood states, negative biases have also been found in

nondysphoric depression-vulnerable samples (e.g., Bouhuys et al., 1999; Joormann & Gotlib,

2007). Regarding the cognitive-interpersonal model, impaired facial affect processing has been

linked with childhood neglect and with early maladaptive social schemas (Csukly et al., 2011;

Pollak et al., 2000). In line with interpersonal models, processing others’ facial affect appears to

be less rewarding and more aversive for depression-susceptible individuals (Domschke et al.,

2008; Persad & Polivy, 1993; Surguladze et al., 2005). Evidence for a risk-averse mechanism has

been mixed and more difficult to interpret due to operationalization issues explained later.

Cognitive neuroscience research on depression has begun to forge links between cognitive

biases and observable differences in neural activity. In particular, depressed groups viewing

depression-relevant facial affect have shown correspondingly biased activity in structures with

specialized face-processing neural networks, such as the fusiform gyrus, orbitofrontal cortex, and

amygdala (Rolls, 2008; Surguladze et al., 2005; Suslow et al., 2010; Wolfensberger et al., 2008).

Findings that connect abnormal fusiform response and common depressotypic facial affect

processing biases (e.g., Deldin et al., 2000) most strongly indicate a face-modality-specific

Processing Biases 59

element to these biases. Thus, the aforementioned biases appear to be, at the least,

neurocognitive markers of depression or depression risk.

Beyond this, however, interpretive, attentional, and memory biases present plausible

causal mechanisms. So, these biases may actually be vulnerability or maintenance factors

(Ingram et al., 1998). After initial facial affect processing, cognitive and interpersonal factors

may interact dynamically. Construing the social environment as more unpleasant, unmanageable,

and less rewarding might constrict one from pursuing social interactions with the potential to

boost mood and reinforce affiliation (Csukly et al., 2011; Depue & Collins, 1999). Also,

negatively perceived social interactions and related thoughts and feelings of low self-worth and

sadness (Frewen & Dozois, 2005; Fritzsche et al., 2009; Persad & Polivy, 1993) could fuel

aversive reassurance seeking and self-verification behaviors that might ultimately worsen

alienation and dysphoria (Joiner & Coyne, 1999; Swann, Wenzlaff, Krull et al., 1992).

Importantly, these theoretical processes need empirical support. Selective attention toward sad

faces and away from happy faces in the social environment might lead to greater sustained

depressive cognitive processing and moods, forming deeper, elaborated, depressive memory

structures (Ingram, 1984; Linville, 1996; Nolen-Hoeksema et al., 1993). As such, proportionally

greater memory for depressive interpersonal affect could also contribute to a broad negative

schema about the social world (e.g., Jermann et al., 2008). Further, in that facial expressions

elicit congruent emotions, a proportional memory deficiency for positive faces might also reduce

social approach related behaviors, a pattern characteristic of depressed individuals (e.g., Ridout,

Noreen et al., 2009). Again, empirical research is needed to test the theorized connections joining

the largely discrete cognitive and interpersonal findings.

Additional comments and recommendations for future investigation

Processing Biases 60

Emotional faces carry both valence and social (e.g., affiliation, dominance) information,

and in the reviewed studies these two types of information cannot be disentangled. For example,

the angry face of a stranger might convey high dominance and low affiliation. In this case, both

valence and social information predispose one to avoid this person. Therefore, found biases

could be toward valence information, social information, or an interaction of both. This dual

nature underscores that facial affect is not arbitrarily interchangeable with other types of valent

stimuli. Acknowledging this, the current review has emphasized the interplay between cognitive

and interpersonal factors.

The present review has also shown that methodological factors (e.g., type of task, stimulus

duration) and sample diagnostic factors (e.g., comorbid anxiety and depressive severity)

influence the findings and theoretical conclusions derived from individual studies. Studies that

test theories or fill empirical gaps left by these conditional factors are needed to expand our

understanding of abnormal facial affect processing. For instance, variability in empirical findings

(e.g., inconsistently found biases for angry faces) may reflect the known heterogeneity of

depressive presentations. Examining neurocognitive processing of facial affects in various

feature presentations of depression (e.g., agitated/hostile, atypical, melancholic, recurrent) might

clarify seemingly anomalous findings and specific diagnostic features. Given the high co-

occurrence of anxiety with depression, studies have increasingly reported sample anxiety levels,

and a few have even controlled for anxiety level in their analyses. However, most studies have

not explicitly excluded comorbid anxiety disorders or reported their presence in depressed

samples, unless comorbidity itself was a research focus. Future facial affect processing studies

should carefully document the extent to which clinically significant anxiety co-occurs with

depression in their samples. Similarly, continued research examining gene and gene-by-

Processing Biases 61

environment operationalizations of depression risk and abnormal facial affect processing (e.g.,

Dannlowski et al., 2008; Dannlowski et al., 2009; Gibb et al., 2010; Wolfensberger et al., 2008)

might account for variance and help explain potential mechanisms of risk.

Additionally, emotional valence specificity and generality findings merit refined

hypothesis formulation and testing. Some studies have found information processing biases

specific to depression-relevant facial affects, while other studies have found biases toward a

wider variety of negative—perhaps socially threatening—affects. However, more consistent

support for content specificity is provided by attention and memory studies, and to a lesser

extent, the affect identification literature. Also, greater differentiation among emotions is needed

when operationalizing the social risk hypothesis descriptor, ―socially threatening.‖ We have

presumed that this concept includes facial expressions portraying sadness, disgust, anger,

contempt, fear, and ambiguous neutrality, emotions that have distinct motivational, behavioral,

and neurobiological signatures (Bradley & Lang, 2007; Springer, Rosas, McGetrick, & Bowers,

2007). However, only a few studies have found abnormal depressive cognitive biases for anger,

fear or disgust (notably, only affect identification studies included disgust).

Sad facial affect processing more consistently distinguishes depression-prone from low-

risk groups. This could be due to the differential salience of the affect. A happy woman might

find an unfamiliar man’s sadness irrelevant, and she might sense that her mood will worsen (e.g.,

Ruys & Stapel, 2008) if she interacts with him. Positive moods promote health and social

behaviors that increase one’s potential for survival and reproduction (Pressman & Cohen, 2005).

Thus, in an evolutionary or clinical sense, relatively minimizing perception and memory of

others’ misery might actually be beneficial. In depression, a cognitive state appears to make

others’ sad affect unavoidably salient. Concomitantly reduced positive emotion may prevent one

Processing Biases 62

from reaching out to these sad others, as the social outcome would be unacceptably

unpredictable. For instance, cavorting with an outcast might worsen one’s social standing. Thus,

a risk-averse social strategy might be adaptive in the short term for a person with temporarily

depreciated social standing (Badcock & Allen, 2003; Forgas & East, 2008a, 2008b), but not in

the long term. Allen and Badcock (2003) also predict that depressed moods should direct one’s

attention away from positive reassuring information, but it is unclear what purpose this would

serve. One speculation is that a depressed man could protect himself from being deceived by

avoiding a smiling stranger, who has unknown intentions (see Forgas & East, 2008a, 2008b).

Indeed, reviewed studies exclusively used facial expressions of strangers, and not familiar

persons who might be more predictable and safe. Nevertheless, the social risk hypothesis

incisively connects traditionally cognitive concepts, such as self-worth, hopelessness, and

cognitive bias, with social status and functioning. The theory also offers a speculative

evolutionary account of a putative biasing program activated by depressed moods and

maladaptively de-regulated in major depression.

Future research that examines different aspects of information processing with multiple

methodologies will lead to a greater understanding of the processes at work in depressive

cognition. Cognitive neuroscience research will continue to examine both discrete and protracted

psychophysiological processes related to cognitive biases. Studies that assess biases on

simultaneous behavioral and neural levels of analysis will be invaluable (e.g., Dannlowski et al.,

2007a). For example, research could examine the depressive response to sad target and distracter

faces in an oddball paradigm.

Ideally, future research will employ prospective designs to test possible causal pathways.

The current, largely cross-sectional evidence base presents theoretically viable but empirically

Processing Biases 63

untested causal chains. Thus far, we have seen that children interacting with a depressed mother

show depressotypic mood and facial affect processing (Diego et al., 2004; Joormann et al., 2007)

and evidence of poor bonding and attachment (Boyd et al., 2006; Dawson, Klinger, Panagiotides,

Hill, & Spieker, 1992). Consistent with cognitive-interpersonal and interpersonal models,

insecure attachment, interpersonal difficulties, and insufficient social support have all been

linked to depression (Coyne, 1976; Gotlib & Hammen, 1992; Lewinsohn, 1974) and to abnormal

facial affect processing (Leppӓnen & Hietanen, 2001; Pollak et al., 2000). We have also noted

that gene-by-environment interactions may be associated with abnormal depressive neural

processing of affective faces (Wolfensberger et al., 2008). Specific dysfunction in the temporal

and orbitofrontal cortices has been linked to social impairments (Hornak et al., 2003; Rolls,

2008), and abnormal parietotemporal and orbitofrontal responses to depression-themed facial

affect have been found in depression (Cavanagh & Geisler, 2006; Deldin et al., 2001; Lee et al.,

2008). This, in addition to blunted neurocognitive-emotional response to happy faces (Domschke

et al., 2008; Surguladze et al., 2005), suggests that depressed individuals can experience a more

difficult, less positive social environment—also consistent with interpersonal theories. In turn,

abnormal neural responses to facial affect have been linked to cognitive biases in depression

(Dannlowski et al., 2007a).

Importantly, for depressed individuals, viewing others’ facial affect can elicit prolonged

depressed mood and negative automatic thoughts about the self in relation to others (Frewen &

Dozois, 2005; Persad & Polivy, 1993). However, we do not yet know whether premorbid

affective processing biases prospectively predict new onsets of depression. Armed with a

foundational network of interrelations, longitudinal research can help differentiate factors that

cause, maintain, or spuriously accompany depression. Although the relative absence of

Processing Biases 64

prospective studies is a limitation of the literature, the few studies that have examined facial

affect processing prospectively (Bouhuys et al., 1999; Bouhuys et al., 1996; Geerts & Bouhuys,

1998; Suslow et al., 2004) support the promising potential for future research.

Refined theory-testing should more comprehensively assess relationships between facial

affect processing and the reviewed depression models. To substantiate proposed interpersonal

mechanisms, empirical research will need to connect abnormal facial affect perception with

depressotypic reassurance seeking and social dysfunction. Perceptual-social links similar to those

found in normal, autistic, schizophrenic, and Williams syndrome groups (Greimel et al., 2010;

Meyer & Kurtz, 2009; Santos et al., 2009) might also be found in depression-prone groups.

Greater support for putative cognitive-interpersonal model processes would be provided by

research directly linking abnormal facial affect processing with negative thought patterns in

depression-vulnerable children. Regarding the social risk hypothesis, research should examine if

depressed individuals are more sensitive or attentive to disgust and contempt displays, as these

facial affects might reliably predict rejection (Fischer & Roseman, 2007; Rozin et al., 2008).

It will also be important to determine whether particular cognitive biases related to

emotional facial expressions are dependent on mood-state activation (i.e., occurring in currently

dysphoric or depressed individuals) or whether biases are also associated with trait-but-not-state

vulnerability status (e.g., nondysphoric formerly depressed individuals, nondysphoric children of

depressed parents) under certain conditions. For ease of description, we have assumed that all at-

risk groups may have similar cognitive processing biases. Clearly, this may not be the case.

Studies that are able to examine multiple at-risk groups—with and without mood priming

procedures—will be uniquely positioned to test this naïve assumption. Determining which biases

appear prior to a first onset or persist after depression remission will provide crucial knowledge

Processing Biases 65

about vulnerability processes. In turn, this could inform prevention efforts. For example,

paradigms that remediate negative emotional information processing biases are accumulating

empirical support (see Koster, Fox, & MacLeod, 2009 for introduction to cognitive bias

modification). It will be important to determine whether facial affect processing biases are also

mutable through psychosocial intervention.

Conclusion

Caveats, comments, and recommendations aside, neurocognitive research examining

facial affect processing provides moderate support for cognitive models of depression and

glimpses into how processing biases manifest in brain activity. In that facial affect processing

impacts attachment, social competence, and resource acquisition (Cozolino, 2002; Leppӓnen &

Hietanen, 2001; Wilson, 1999), it should be particularly important for those susceptible to

depression. These individuals might perceive and remember a more daunting social world, which

could promote dysphoria, self-deprecation, isolation, and rumination (Frewen & Dozois, 2005;

Persad & Polivy, 1993; Raes et al., 2006). Although the current literature implicates

interrelations among interpersonal experience, depressive cognition, and social behavior, future

work must directly test potential causes and effects of depressive facial affect processing.

Processing Biases 66

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Table 1

Studies evaluating identification of facial affect

Study

Participants Task Stimuli Presented Normal, General,

Specific Effects

Findings Description

Beevers et al.

(2009)

52 Dys, 55 HC

college students. AIT with

morphed

emotion dyadic

faces

Faces: sad, happy, angry,

fearful, morphed happy-

sad, happy-fearful, fearful-

angry, sad-angry Duration: response latency

N, S Dys = HC identification of

prototype affects. Dys > HC

identification of sadness mixed

with happiness, fear mixed with

happiness.

Bouhuys et al.

(1996)

30 CD, 1

cyclothymic, 2

dysthymic patients.

Emotion

recognition and

judgment task

Schematic faces: sad,

angry, disgusted, fearful,

happy, surprise Duration: response latency

N, S CD patients who perceived less

rejection, sadness, anger in

ambiguous faces had less

symptomatic improvement after 6

& 30 weeks.

Deveney &

Deldin (2004)

17 CD, 17 HC

community

individuals.

Identity

matching task

(yes/no) with

serially

presented face

pairs

Faces: sad, happy, neutral Duration: response latency

S In HC, slow wave ERP amplitudes

following sad < happy faces. In

CD, slow wave amplitudes

following sad = happy faces.

Frewen &

Dozois (2005)

105 college women

(48 nondysphoric, 55

dysphoric).

Timed emotion

recognition task Faces: angry, sad, fearful,

happy, disgusted, neutral Duration: up to 10,000 ms

N BDI scores not significantly

correlated with accuracy of affect

identification for any of the affects.

Gaebel &

Wolwer

(1992)

21 CD inpatients, 15

HC community

individuals.

AIT Faces: angry, happy, sad,

fearful, disgusted,

surprised Duration: 8,000 ms

N CD = HC performance on facial

affect identification.

Gollan et al.

(2008) 29 CD, 37 HC

individuals from

AIT with forced

choice among

Faces: happy, neutral, sad,

angry, disgusted, fearful S CD > HC bias of interpreting

neutral faces as sad. CD = HC

Processing Biases 97

university campus

and community. non-neutral

affects on

neutral

presentations

Duration: 3,000 ms accuracy across affects. CD > HC

reaction time to sad faces.

Gollan et al.

(2010)

44 CD, 44 HC

community

individuals.

AIT Faces: sad, harsh, surprise,

happy in 10-80%

gradations from neutral,

and 100% neutral faces Duration: 500 ms

S CD > HC in accuracy for labeling

sadness, collapsing across all affect

intensity levels. CD > HC between

10-40% sadness intensities. CD =

HC between 50-80% sadness

intensities.

Gur et al.

(1992)

14 CD, 14 HC clinic

and community

individuals.

Facial

discrimination

task

Faces: neutral, varying

degrees of happy-neutral,

sad-neutral. Stimuli

gender-matched to

participant Duration: 7,000 ms

S CD < HC on sensitivity to happy-

neutral faces, specificity for sad

faces. CD perceived happy as more

neutral, neutral as more sad. In

CD, higher negative affect

inversely correlated with accurate

identification of sad faces.

Hale (1998) 48 CD, 47 HC clinic

and community

individuals.

Emotion

recognition and

judgment task

Schematic faces: happy,

sad, fearful, rejecting,

angry, disgusted, inviting,

three ambiguous blends Duration: response latency

S CD > HC judging sadness in

prototype and ambiguous affective

faces. Increased judgment of

sadness at baseline related to

severity of symptoms, also to

persistence of depression at 13, 26

weeks.

Joormann &

Gotlib (2006)

23 CD, 27 SP, 26 HC.

Anxiety disorders

excluded from CD.

AIT with

morphed

emotion after

sad mood prime

Faces: neutral morphed

with happy, sad, angry,

fearful in fluidly

increasing affective

intensity Duration: 500 ms motion

picture style presentation

S CD required less intensity before

identifying sad compared to angry

affect. CD required greater

intensity before identifying happy

affect than HC, SP.

Processing Biases 98

LeMoult et al.

(2009)

39 PRD (3 with

current anxiety

disorders), 56 NC.

AIT with

morphed

emotion after

sad mood prime

Faces: neutral morphed

with happy, sad, angry in

fluidly increasing affective

intensity Duration: 500 ms motion

picture style presentation

S PRD required greater intensity

before identifying positive affect

than NC.

Leppӓnen et

al. (2004)

18 CD, 18 HC

individuals. Excluded

comorbid psychiatric

diagnoses.

AIT Faces: happy, sad, neutral Duration: 200 ms

N, S CD = HC at identifying sad, happy

faces. CD < HC accuracy in

identifying neutral, but no specific

bias.

Merens et al.

(2008)

18 individuals with

remitted MDD. Pre- and post-

ATD facial

recognition task

for sensitivity to

affective

intensities

Faces: happy, sad, angry,

disgust, fear in 10-100%

gradations from neutral.

S Accurate identification of sadness

pre-ATD predicted more severe

depressive symptoms post-ATD.

High-dose ATD associated with

impaired recognition and memory

of fear. Low-dose ATD related to

faster processing of disgust.

Milders et al.

(2010)

19 CD (5 CAD), 25

HC community

individuals.

Emotion

matching task,

emotion labeling

task

Faces: sad, happy, fearful,

disgusted, angry in 20-

80% gradations from

neutral Duration: 1000 or 2000 ms

S CD > HC in accuracy for labeling

sadness, collapsing across all affect

intensity levels. CD > HC at 20%

and 40% sadness intensities. CD =

HC at 60% and 80% sadness

intensities. CD = HC on matching

task.

Noreen &

Ridout (2010)

29 Dys, 22 HC

students. AIT, RMT for

affect displayed,

RMT for identity

displayed

Faces: happy, sad, angry,

neutral Duration: 2500 ms

N Dys = HC identifying affect. Dys

< HC on recognition memory

across affects and specific

identities that had previously

displayed angry, happy or neutral

affect. Controlled for anxiety.

Persad & 16 Dys and 16 HC Facial affective Faces: happy, sad, angry, G Dys and CD groups < HC college

Processing Biases 99

Polivy (1993) college students. 16

CD and 11

nondepressed

psychiatric patients.

booklet (emotion

labeling)

fearful, surprised,

contemptuous, disgusted,

indifferent Duration: response latency

students in accurate identification

across affects. No emotion-specific

deficit or bias in CD groups.

Ridout,

Noreen et al.

(2009)

Study 1: 24 Dys, 20

HC. Study 2: 24 negative

mood-induced HC,

24 positive mood-

induced HC.

Study 1: AIT

then RMT Study 2: Positive

or negative MI,

AIT, RMT

Faces: happy, neutral, sad Duration: response latency

for AIT and RMT.

S Study 1: Dys < HC identification

of sad and neutral faces, memory

for happy faces. Dys > HC

memory for sad. Dys memory for

sad > happy. Study 2: Negative MI HC >

positive MI HC in identification of

sad faces, memory for sad faces.

Negative MI HC identification of

sad > neutral, also memory for sad

> happy or neutral.

Suslow et al.

(2004)

22 CD (11 CAD, 11

no comorbidity)

treatment-seeking, 22

HC community

persons.

Face-in-the-

crowd. Before

and after 6

weeks

psychotherapy

Faces: schematic positive,

neutral, negative Duration: 500 ms

S, N CAD > HC on RT detection of

positive faces. MDD without

comorbidity = HC.

Surguladze et

al. (2004)

27 CD, 29 HC

individuals from

inpatient/outpatient

clinics and

community.

AIT Faces: 100% happy, sad,

prototypes and 50%

happy-neutral, sad-neutral

morphs Duration: 100 ms, 2000

ms

G, N, S On 100 ms sad and happy affects,

CD < HC accuracy. On 2,000 ms

prototype affects CD = HC. On

2,000 ms affect morphs, CD bias to

interpret happy as neutral, HC bias

to interpret neutral as happy. In

CD, higher depression scores

associated with worse

identification of sad affect.

Weniger et al.

(2004)

21 CD, 30 HC

inpatient clinic and

community

Emotion

matching task,

emotion arousal

Faces: angry, sad, fearful,

happy, disgusted Duration: response latency

G CD < HC overall matching

performance. CD < HC in rated

arousal intensity for emotional

Processing Biases 100

.individuals rating faces.

Wright et al.

(2009)

79 CD (56 women,

15 CAD), 72 HC (34

women) clinic and

college community

individuals.

AIT Faces: happy, sad, angry,

fearful Duration: 300 ms

S, N CD women < HC women in

accurate identification of sad,

fearful faces. Misinterpreted

fearful as angry. No main effects

of depression or gender. No

differences between CD, HC men.

Yoon et al.

(2009) 21 CD, 23 SAD, 20

HC community

individuals.

Forced choice

intensity

judgment task

(presentations of

paired faces with

differing affects)

Faces: happy, sad, angry,

fearful faces of 40%

affective intensity and

neutral faces Duration: response latency

S CD < HC, SAD in proportional

likelihood of judging happy faces

as more intense than neutral faces.

CD, SAD < HC in likelihood of

judging happy faces as more

intense than sad or angry faces.

Note. AIT = affect identification task; ATD = acute tryptophan depletion; BDI = Beck Depression Inventory; CAD = comorbid anxiety and

depression; CD = currently depressed; Dys = Dysphoric; ERP = event-related potentials; FMT = face memory task; FD = formerly depressed; HC

= healthy controls; MDD = major depressive disorder; MI = mood induction; ND = never depressed; RMT = recognition memory test; RT =

reaction time; SAD = social anxiety disorder; SP = social phobia.

Processing Biases 101

Table 2

Studies evaluating selective attention to affective faces

Study

Participants Task Stimuli Presented Bias Found Findings Description

Bradley et al.

(1998)

20 high, 20 low trait

anxiety students.

Dot-probe Faces: happy,

threatening, neutral Duration: 500 ms, 1250

ms

↓ happy Dys correlated with reduced attention to

happy faces for 500 ms but not 1250 ms

presentations.

Bradley et al.

(2000)

55 students, mostly

high and low state

anxiety.

Dot-probe Faces: happy, sad,

threatening, neutral

Duration: 500 ms

↓ happy Dys correlated with reduced attention to

happy faces. No bias toward sad faces.

Cavanagh &

Geisler (2006)

18 CD (no SCID

diagnosis), 18 HC

students.

Oddball Faces: happy, fearful

(targets) neutral

(standard) Duration: 750 ms

↓ happy CD < HC P300 amplitude for happy

faces only. CD > HC P300 latency for happy faces

only.

Fritzsche et al.

(2009)

20 CD, 20 FD, 20 NC,

20 ND with diagnosed

asthma. Past/present

anxiety disorders

excluded.

Dot-probe Faces: sad, happy,

neutral Duration: 1000 ms

↓ happy, ↑ sad CD showed bias toward sad, away from

happy faces. FD > HC attention to sad.

HC showed bias toward happy, away

from sad faces.

Gibb et al.

(2009) 40 children (10 FD) of

mothers with past

MDD within child’s

lifetime, 32 children of

NC mothers.

Dot-probe Faces: sad, happy,

angry, neutral Duration: 1000 ms

↓ sad Children of FD mothers showed specific

bias away from sad faces. Finding

strongest in carriers of 5-HTTLPR S or

LG allele.

Goeleven et al.

(2006)

20 CD, 20 FD, 20 HC

community individuals. Negative

affective

priming

Faces: happy, sad Duration: response

latency

↑ sad CD < HC, FD on NAP effect for sad

faces. FD lacked NAP effect for both

valences.

Processing Biases 102

Gotlib, Kasch

et al. (2004)

88 CD, 35 GSP, 55 ND

clinic and community

individuals. Anxiety

disorders excluded

from CD.

Dot-probe Faces: sad, happy,

angry, neutral Duration: 1000 ms

↑ sad CD bias toward sad faces. Positive

correlation between depressive

symptoms and bias away from happy

faces. No biases in comparison groups.

Gotlib,

Krasnoperova

et al. (2004)

19 CD, 18 GAD, 16

HC individuals from

clinic and community. Anxiety disorders

excluded from CD.

Dot-probe Faces: sad, happy,

angry, neutral Duration: 1000 ms

↑ sad CD bias toward sad faces compared to

happy or angry faces. No biases in

comparison groups.

Hsieh & Ko

(2004)

17 high, 13 low trait

depression students. DOAT, RMT Faces: happy, angry,

sad, neutral Duration: 300, 400, 500

ms randomly varied

↑ sad DOAT: High group > low group

attention to sad faces. RMT: High group > low group on sad

faces.

Joormann &

Gotlib (2007)

26 CD, 23 FD, and 19

NC clinic and

community individuals.

Anxiety disorders

excluded.

Dot-probe Faces: happy, neutral,

sad Duration: 1000 ms

↑ sad CD, FD bias toward sad faces. HC

avoided sad faces, oriented to happy

faces.

Joorman et al.

(2007)

21 girls of mothers with

history of RMD, 20

girls of NC mothers

from clinic and

community.

Dot-probe

following sad

MI

Faces: happy, neutral,

sad. Duration: 1500 ms

↑ sad At-risk girls bias toward sad faces, not

happy faces. Low-risk daughters bias

toward happy faces, not sad faces.

Leyman et al.

(2007)

20 CD, 20 HC

individuals from clinic

and governmental

agency.

Exogenous

cueing task Faces: angry, neutral Duration: 1000 ms

↑ angry CD > HC attentional engagement with

angry faces. CD attentional engagement

for angry > neutral faces.

Mogg et al.

(2000) 15 CD (13 CAD), 16

HC individuals from

Dot-probe Faces: happy, sad,

threatening, neutral none CD = HC for dot-probe RT and eye

movement monitoring.

Processing Biases 103

community.

Duration: 1000 ms

Suslow et al.

(2001)

15 mood disordered (10

MDD, 5 dysthymia)

treatment seeking

persons, 15 HC

community persons.

Face-in-the-

crowd Schematic faces:

positive, neutral,

negative Duration: 500 ms

↓ happy Mood disordered group RT > HC RT for

positive faces only.

Wells et al.

(2010)

32 Dys, 24 HC college

students. Eye tracking

during passive

viewing trials

of 12s, RMT

Faces: happy, sad,

angry, neutral Duration: 12000 ms

↑ angry Dys > HC distance between attentional

fixations on angry faces. Dys > HC in

recognition accuracy for angry faces,

mediated by fixation effect. Note. 5-HTTLPR = serotonin transporter-linked polymorphic region; CAD = comorbid anxiety and depression; CD = currently depressed; DOAT

= deployment of attention task; Dys = Dysphoric; FMT = face memory task; FD = formerly depressed; GAD = generalized anxiety disorder; GSP

= generalized social phobia; HC = healthy controls; MDD = major depressive disorder; MI = mood induction; NAP = negative affective priming;

NC = never disordered controls; ND = never depressed; P300 = third positive peak in event-related electroencephalographic waveform; PRD =

past recurrent depression; RMD = recurrent major depression; RMT = recognition memory test; RT = reaction time; SCID = Structured Clinical

Interview for DSM-IV.

Processing Biases 104

Table 3

Studies evaluating memory for affective faces

Study

Participants Task Stimuli Presented Bias Found Findings

Deldin et al.

2001

19 CD (14 inpatient, 5

community), 15 HC

from community.

Positive/negative

affective judgment,

RMT

Faces: happy, sad,

neutral Duration: 200 ms

none For HC, encoding P300 greater,

recognition memory P300 lower

for positive faces. For CD, no

differences. CD = HC in memory

accuracy and RT.

Gilboa-

Schechtman

et al. (2002)

23 CAD, 20 ANX

treatment seeking and

23 HC community

individuals.

Incidental encoding,

FMT Faces: sad, angry,

happy, and neutral Duration: response

latency

↑ sad, ↑ angry CAD recall: sad and angry faces >

happy and neutral faces. HC

recall: happy and neutral faces >

sad and angry faces.

Hsieh & Ko

(2004)

17 high, 13 low trait

depression students. DOAT, RMT Faces: happy, angry,

sad, neutral Duration: 300, 400, 500

ms randomly varied

↑ sad DOAT: High group > low group

attention to sad faces. RMT: High group > low group on

sad faces.

Jermann et al.

(2008)

65 Dys, 78 HC

students and

community

individuals.

Intentional encoding,

remember/know/guess

recognition memory

procedure

Faces: happy, neutral,

sad Duration: 5000 ms for

encoding task, response

latency for memory task

↑ sad Dys > HC recognition memory

for sad faces.

Noreen &

Ridout (2010)

29 Dys, 22 HC

medication-free

students.

AIT, RMT affect

discrimination trials

15s post-display,

RMT identity

discrimination trials

15s post-display

Faces: happy, sad,

angry, neutral Duration: 2500 ms

↓ happy Dys = HC identifying affect. Dys

> HC on WM across affects. In

Dys, WM for identities that

previously displayed happy <

those that displayed angry, happy

or neutral affect.

Pine et al.

(2004) Adolescent children:

19 FD, 26 of NC

FMT Faces: happy, fearful,

angry ↓ fearful FD children reduced memory for

fearful faces. No biases for

Processing Biases 105

parents, 53 of FD

parents, 58 of CAD

parents.

Duration: 4000 ms

children of parents with history of

depression.

Ridout et al.

(2003)

16 CD, 16 HC

community persons. RMT Faces: happy, sad,

neutral Duration:

response latency

↑ sad CD recognized sad > neutral >

happy. HC recognized happy >

neutral > sad.

Ridout,

Dritschel et

al. (2009)

16 CD, 18 HC clinic

and community

individuals.

Implicit encoding

(gender identification)

task, RMT

Faces: happy, neutral,

sad Duration: response

latency for encoding and

recognition phases

none CD showed no recognition

memory bias for sad faces. CD,

HC show no significant memory

differences for specific valences.

Ridout,

Noreen et al.

(2009)

Study 1: 24 Dys, 20

HC Study 2: 24 negative

mood-induced HC, 24

positive mood-induced

HC.

Study 1: AIT then

RMT Study 2: Positive or

negative MI, AIT,

RMT

Faces: happy, neutral,

sad Duration: response

latency for AIT and

RMT.

↑ sad Study 1: Dys < HC identification

of sad and neutral faces, memory

for happy faces. Dys > HC

memory for sad. Dys memory for

sad > happy. Study 2: Negative MI HC >

positive MI HC in identification

of sad faces, memory for sad

faces. Negative MI HC

identification of sad > neutral,

also memory for sad > happy or

neutral.

Wells et al.

(2010)

32 Dys, 24 HC college

students. Eye tracking during

focused viewing,

RMT

Faces: happy, sad,

angry, neutral Duration: 12000 ms

↑ angry Dys > HC distance between

attentional fixations on angry

faces. Dys > HC in recognition

accuracy for angry faces,

mediated by fixation effect.

Note. AIT = affect identification task; ANX = anxiety disorder; CAD = comorbid anxiety and depression; CD = currently depressed; DOAT =

deployment of attention task; Dys = Dysphoric; FMT = face memory task; FD = formerly depressed; GAD = generalized anxiety disorder; HC =

Processing Biases 106

healthy controls; MI = mood induction; ND = never depressed; P300 = third positive peak in event-related electroencephalographic waveform;

RMT = recognition memory test; WM = working memory