Abstract
Rapid and accurate detection of threat is adaptive. Yet, threat-related attentional biases, including hypervigilance, avoidance, and attentional disengagement delays, may contribute to the etiology and maintenance of anxiety disorders. Behavioral measures of attentional bias generally indicate that threat demands more attentional resources; however, indices exploring differential allocation of attention using reaction time fail to clarify the time course by which attention is deployed under threatening circumstances in healthy and anxious populations. In this review, we conduct an interpretive synthesis of 28 attentional bias studies focusing on event-related potentials (ERPs) as a primary outcome to inform an ERP model of the neural chronometry of attentional bias in healthy and anxious populations. The model posits that both healthy and anxious populations display modulations of early ERP components, including the P1, N170, P2, and N2pc, in response to threatening and emotional stimuli, suggesting that both typical and abnormal patterns of attentional bias are characterized by enhanced allocation of attention to threat and emotion at earlier stages of processing. Compared to anxious populations, healthy populations more clearly demonstrate modulations of later components, such as the P3, indexing conscious and evaluative processing of threat and emotion and disengagement difficulties at later stages of processing. Findings from the interpretive synthesis, existing bias models, and extant neural literature on attentional systems are then integrated to inform a conceptual model of the processes and substrates underlying threat appraisal and resource allocation in healthy and anxious populations. To conclude, we discuss therapeutic interventions for attentional bias and future directions.
Keywords: attention, anxiety, event-related potentials, Attention Bias Modification Treatment, Mindfulness-Based Cognitive Therapy
1. Introduction
The visual system, like all sensory systems, is constantly bombarded by streams of information competing for awareness. However, only a small amount of the information available on the retina can be processed and used in the control of behavior (Desimone & Duncan, 1995). Fortunately, competition can be resolved through selective attention, which is the ability to focus on information currently relevant to behavior while filtering out irrelevant information (Desimone & Duncan, 1995; Posner & Petersen, 1990). In all humans, selective attention is powerfully biased towards threat-related information as an evolutionarily adaptive response in environments where dangers constantly threaten survival and reproductive advantage (Ohman, Flykt, & Esteves, 2001). Attentional bias to threat is defined as differential attentional allocation towards threatening stimuli relative to neutral stimuli (Cisler & Koster, 2010; Mogg & Bradley, 2018; Okon-Singer, Hendler, Pessoa, & Shackman, 2015).
Cognitive and neural research supports the theory that attentional processing of threatening stimuli (e.g., angry facial expressions) is prioritized over neutral stimuli in healthy individuals (Maratos, Mogg, & Bradley, 2008; Okon-Singer, 2018; Yiend, 2010). For example, studies using rapid serial visual presentation (RSVP) paradigms to investigate the effects of emotional face stimuli on attentional blink have observed enhanced detection (reduced attentional blink) of second target (T2) face stimuli associated with threat or danger compared to neutral (Maratos et al., 2008; Milders, Sahraie, Logan, & Donnellon, 2006) and happy faces (Milders et al., 2006). Similarly, in visual search tasks, fear-relevant pictures were found more quickly than fear-irrelevant ones (Ohman et al., 2001), and threatening faces guided visual search more efficiently than happy or neutral faces (Hahn, Carlson, Singer, & Gronlund, 2006; Hansen & Hansen, 1988). Importantly, selection for awareness and prioritized attention towards threatening information is influenced by a number of factors, including individual differences in the significance of stimuli to the observer (Milders et al., 2006; Mogg & Bradley, 2016; Okon-Singer, Lichtenstein-Vidne, & Cohen, 2013; Yiend, 2010). For example, participants fearful of snakes but not spiders (or vice versa) showed facilitated search for the feared objects, but did not differ from controls in search for non-feared, fear-relevant or fear-irrelevant targets (Ohman et al., 2001).
Empirical evidence from the extant attentional bias literature indicates that anxiety alters how attention functions, but in an inconsistent manner. Depending on an individual’s levels of state anxiety, defined as fear, nervousness, discomfort, and the arousal of the autonomic nervous system induced temporarily by situations perceived as dangerous (e.g., how a person is feeling at the time of a perceived threat) (Spielberger, Gorsuch, & Lushene, 1970; Spielberger & Sydeman, 1994), and trait anxiety, defined as a relatively enduring disposition to feel stress, worry, and discomfort (Spielberger et al., 1970; Spielberger & Sydeman, 1994), attentional biases may either shift toward or away from threat. Further complexifying our understanding of biased attention is the observation that the direction of bias varies depending on the temporal stage of stimulus processing. The components, or observable and measurable characteristics, of attentional bias to threat are (1) facilitated attention to threat, or hypervigilance (i.e., the relative ease or speed with which attention is initially and involuntarily drawn to a threat stimulus), during early, automatic processing stages, (2) difficulty disengaging attention away from threat (i.e., the degree to which a threatening stimulus captures attention and impairs switching attention from the threatening stimulus to another stimulus), and (3) attentional avoidance of threat (i.e., automatic or strategic shifting of attention away from the spatial location of threat, even when the threatening item is no longer present) during early or late processing stages (Cisler & Koster, 2010).
Although threat-related biases are important for survival, alterations in these biases are thought to contribute to the etiology and maintenance of anxiety disorders. Anxiety disorders, including generalized anxiety disorder (GAD), social anxiety disorder (SAD), and specific phobia, are associated with hypervigilance to potential threat in preparation for future danger, cautious or avoidant behaviors (DSM-5 American Psychiatric Association, 2013), and delayed disengagement from threat (Amir, Elias, Klumpp, & Przeworski, 2003). The attentional system of anxious individuals is thought to be distinctively sensitive to and biased in favor of threat-related stimuli in the environment (Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van Ijzendoorn, 2007). Consistent with this observation, threat-related attentional bias among anxious populations has been demonstrated to be a relatively consistent observation (Bar-Haim et al., 2007; Cisler & Koster, 2010; Mogg & Bradley, 1998) at the behavioral level. Continued hypervigilance and sensitivity to detecting potential environmental threat plays an important role in maintaining and intensifying anxiety (Mathews, 1990; Mogg, Bradley, De Bono, & Painter, 1997). Moreover, evidence from behavioral inhibition (BI) literature focusing on children (Pérez-Edgar et al., 2010, 2011) suggests that attentional bias to threat is also involved in the etiology of anxiety disorders. BI is a temperament trait which emerges early in childhood, characterized in young children by a heightened sensitivity to novelty, social withdrawal, and anxious behaviors. Pérez-Edgar et al. (2010) have shown that children with a history of BI demonstrate biases similar to those observed in anxious children and adults. Early BI, when coupled with attention biases to threat, are linked to sustained levels of social withdrawal in adolescence. The authors assert that attentional bias is a potentially important mechanism that sustains early maladaptive temperamental traits and may increase the risk for the later emergence of clinical anxiety. Although it is clear that attentional biases are present in anxiety disorders, there is a lack of agreement on how these biases present across different stages of information processing, due in part to limitations of behavioral indices that rely on reaction time (RT). Nevertheless, anxiety disorders provide an excellent model in which to study the components of attentional bias to threat.
Given the role of threat-related attentional bias in the etiology and maintenance of anxiety-related psychopathology and the need to better understand neural mechanisms in the temporal dimension, we (1) discuss existing models of threat-related attentional bias, (2) briefly discuss behavioral attentional bias research, and (3) conduct an interpretive synthesis of 28 attentional bias studies focusing on event-related potentials (ERPs) as a primary outcome in order to inform (4) an ERP model of the neural chronometry of attentional bias in healthy and anxious populations (Figure 1). (5) Findings from the interpretive synthesis, existing bias models, and extant neural literature on attentional systems are then integrated to inform a conceptual model of early and late-stage processes and substrates underlying threat appraisal and resource allocation in healthy and anxious populations (Figure 2). To conclude, we discuss (6) therapeutic interventions which may mitigate attentional bias and future directions.
Figure 1. An ERP model of the neural chronometry of attentional bias.
ERPs are schematically represented across stimuli and paradigms. In this image, negative voltages are plotted upward. Subcomponents are not explicitly shown, but are included with their primary, overarching components (e.g. “N1” in the figure includes N170 modulations and “N2” in the figure includes N2pc modulations). Amplitude and latency modulations have been estimated to enhance visualization of differences between populations and conditions.
Figure 2. A Conceptual Model for Threat Appraisal and Resource Allocation.
Early and late stages of threat appraisal and resource allocation are represented. The model builds upon existing attempts to synthesize the extant literature depicting stages of information processing in attentional bias (Bar-Haim et al., 2007; Cisler & Koster, 2010; Williams et al., 1988), the neural substrates supporting ERP markers, and state and trait modulation of attention (see Mogg & Bradley, 2016; 2018; Okon-Singer, 2018). A sensory object is appraised as having high or low threat value through an Affective Decision Mechanism (ADM). This mechanism is likely early and automatic in nature, detecting salience and threat potential of a sensory stimulus with little interference by conscious awareness. Low threat facilitates pursuit of current goals; high threat induces fear and its associated affective expression. The salience network, dorsal frontoparietal attention network, along with pre-conditioned limbic and brainstem (BS) circuits are implicated in mediating the monitoring and initial threat appraisal associated with the ADM. If the stimulus is appraised as high threat, fear expression is elicited, state arousal increases and the stimulus is sent to upstream neural pathways for increased processing. Early ERP markers (< 250 ms) are thought to be supported by salience and dorsal frontoparietal brain activity. A Resource Allocation Mechanism (RAM) is proposed to activate and determine the extent of attentional engagement for (potentially) high-threat stimuli. Later stage ERPs (> 250 ms) and fMRI data support the idea that high threatening stimuli are either engaged by attention with higher levels of inhibitory control for any distractions, or are elaborated further through cognitive processes supported by the default mode network (DMN), with low levels of inhibitory control. The RAM and associated engagement and disengagement processes are thought to be supported by dorsal and ventral aspects of the frontoparietal networks. Trait anxiety and mindfulness are proposed to modulate the RAM, such that high-trait anxiety (HTA) increases engagement and elaboration, thereby facilitating disengagement delays and increasing state modulatory input (e.g., arousal). Low-trait anxiety (LTA) is proposed to facilitate disengagement and negatively feedback on state modulatory input. Disengagement from task-relevant objects may also be displaced by ruminative thoughts that interfere with task demands. Mindful approach behavior and an inhibitory control mechanism are proposed to facilitate healthy disengagement. Salience network includes the dorsal anterior cingulate cortex (dACC), anterior insular cortex (AIC), amygdala (AMY), and parts of the brainstem (BS). The dorsal fronto-parietal attentional network includes the frontal eye fields (FEF) and superior parietal lobe. The ventral frontoparietal attentional network includes the frontopolar cortex (FPC), ventrolateral PFC (vlPFC), temporoparietal junction (TPJ), and anterior inferior parietal lobe (IPL), and are implicated in mediating the RAM. The DMN includes the ventromedial PFC (vmPFC) and posterior cingulate cortex (PCC), and is implicated in elaborative appraisal in later stages, facilitating avoidance through distraction, and decreasing available resources for ongoing task demands.
2. Existing Models of Threat-Related Attentional Bias
A number of models have been proposed to characterize how threat is detected, how attention to threat is modulated, and the context by which resources are allocated to process potentially threatening stimuli further (see Mogg & Bradley, 2016; Okon-Singer, 2018; Yiend, 2010). It has been proposed that an early-warning, automatic threat detection mechanism occurs at a pre-conscious level (e.g., Bar-Haim et al., 2007; Beck & Clark, 1997; Cisler & Koster, 2010; Öhman, 1993; Williams, Watts, MacLeod, & Mathews, 1988) in order to facilitate safety or danger-appropriate response behaviors. For example, Williams, Watts, MacLeod, & Mathews (1988) propose that the threat value of incoming stimuli is determined by an affective decision mechanism (ADM), which produces an initial decision on whether information is high or low threat. Indeed, it has been shown that evaluation of stimulus emotional valence may take place at very early stages of processing, both automatically and in the absence of awareness (Beck & Clark, 1997; LeDoux, 1996; LeDoux, 1995; Öhman, 1993). If stimulus input is appraised as highly threatening, a resource allocation mechanism (RAM) is proposed to be activated (Cisler & Koster, 2010; Williams et al., 1988). When the RAM is triggered, attentional resources will be allocated to sensory objects appraised as threatening. When stimulus input is determined to be of low threat during the ADM process, attention is maintained on the task at hand (e.g., pursue current goals) as per ongoing task demands, and the new stimulus input will not be attended to. High threat, however, will trigger mobilization of cognitive resources toward the stimulus. A number of theoretical models agree that attentional bias involves an interaction between both early, automatic stages of threat appraisal integrating salience-driven and goal-directed influences on automatized behavior, and later, cognitive processes (e.g., stimulus evaluation, inhibition, attentional engagement/disengagement) associated with and following a RAM (Mogg & Bradley, 2016, 2018).
How threat is detected, evaluated, and the extent to which resources are allocated in response to potential threat also depends on levels of state and trait anxiety. For example, the output of the ADM is moderated by an individual’s level of state anxiety. Some models propose that state or trait anxiety could contribute to facilitated or hypervigilant stimulus-driven attention – potentially impairing proper ADM output and causing avoidance and rumination-like distractibility (Eysenck, Derakshan, Santos, & Calvo, 2007; Grupe & Nitschke, 2013). Such non-conscious avoidance in the absence of awareness may be supported by habitual sensory and emotional habits sensitized and fueled by anticipation of threat (Bar-Haim et al., 2007; LeDoux, 1996; LeDoux, 1995; Öhman, 1993). Williams et al. (1988) elaborate that an individual’s level of trait anxiety modulates later stages of cognitive processing through the RAM. Individuals with this high trait anxiety (HTA) disposition are proposed to allocate attention to threat more readily, thereby facilitating threat appraisal, increasing arousal, and decreasing the likelihood of disengagement from threat. Individuals with low trait anxiety (LTA) are predicted to disengage from the threat object more readily, ignore the potential threat, thereby reducing autonomic arousal, and decrease threat potential of incoming sensory information. This may lead to the favorable attentive processing of threat at preconscious and conscious levels.
Furthermore, some researchers (e.g., Foa & Kozak, 1986; Yiend, 2010) suggest inhibition of elaborative processing of threatening information is the core deficit in anxiety symptom maintenance, which is reflected in strategic avoidance of threatening stimuli. For example, anxious patients often use distraction strategies, such as pretending to be somewhere else, distorting a fearful image, or concentrating on non-feared elements of a situation, to diminish encoding of fear-relevant information and thus impede activation of fear (Foa & Kozak, 1986). Such data support a vigilance-avoidance model, which describes that anxious individuals tend to initially direct attentional resources toward threat during early, automatic stages of processing, but may then direct their attention away from threat during later, more strategic stages of processing in an attempt to avoid detailed elaborative processing of threatening material (Mogg et al., 1997; Mogg, Mathews, & Weinman, 1987). Although this pattern of processing may regulate anxiety states in the short-term, it actually perpetuates anxiety disorders over time, because anxious individuals continue to identify potentially threatening events in the future, while subsequent cognitive avoidance strategies would prevent habituation to, or objective evaluation, of them. The threat cues would then retain their anxiety-provoking properties (Mogg et al., 1997) and perhaps function to sensitize the ADM towards high threat appraisal.
Some research groups (e.g., Fox, Russo, Bowles, & Dutton, 2001) have suggested that trait anxiety has little impact on the initial detection of threat, but has a stronger effect in modulating the maintenance of attention on the source of threat. These researchers have proposed that a delay in disengaging from threat stimuli and failure of inhibitory processes may be the primary attentional difference between anxious and non-anxious individuals (Bar-Haim et al., 2007; Fox et al., 2001). Failure to disengage from the object of threat maintains cognitive resources on the source of stress (i.e., threat stimuli), and, in turn, may maintain and enhance anxiety states (Fox et al., 2001) through both cognitive (e.g., rumination) and emotional output (autonomic arousal). Cognitive-motivational models (Mogg & Bradley, 1998, 2018) also suggest that it is the evaluation of what constitutes a threat, rather than how the attentional system responds to a threat, that differs in HTA and LTA. More specifically, Mogg & Bradley (1998) stipulate that a valence evaluation system similar to the ADM responds more readily in HTA than in LTA; thus, resources are shifted toward the stimulus more often in HTA than in LTA. Similarly, Williams et al. (1988) and others (e.g., Eysenck, 1997; Mogg & Bradley, 1998) propose that HTA individuals exhibit increased conscious engagement with threat, thereby disinhibiting control processes, facilitating further appraisal of threat, and potentially increasing arousal. Neither facilitated engagement nor strategic avoidance are adaptive long-term strategies.
Determining the mechanisms of appraisal and attentional resource allocation in relation to specific stages of information processing is essential for (1) understanding the neural chronometry of attentional bias, and (2) for using the appropriate interventions to target components of threat-related attentional bias. Understanding how attention is deployed to emotional stimuli can shed light on these issues; thus, behavioral studies of threat-related attentional bias in anxious and non-anxious populations are briefly discussed below.
3. Behavioral Measurement of Attentional Bias
Behavioral studies of attentional bias vary greatly in their methodology. Stimuli may consist of emotional, threatening, and non-threatening words (e.g., MacLeod, Mathews, & Tata, 1986; Mogg et al., 1997), scenes (Mogg, Bradley, Miles, & Dixon, 2004), or faces (e.g., Bradley, Mogg, White, Groom, & de Bono, 1999; Fox, Russo, & Dutton, 2002). Stimulus exposure duration may also be varied in order to examine different components of anxiety-related attentional bias (i.e., initial orienting versus maintenance of attention to threat) (Mogg et al., 1997). Subliminal exposure conditions, which preclude conscious awareness, or supraliminal exposure conditions, which allow access to awareness, can additionally be used (Bar-Haim et al., 2007).
In addition, various paradigms have been employed to explore attentional bias, including the dot-probe task, the emotional Stroop task, the emotional spatial cueing paradigm, and the visual search task. Each of these paradigms likely assess somewhat distinct aspects of attention (Bar-Haim et al., 2007). For example, the dot-probe task is used to measure the extent to which threatening stimuli capture attention (Koster, Crombez, Verschuere, & De Houwer, 2004; Rossignol, Philippot, Bissot, Rigoulot, & Campanella, 2012), and the emotional Stroop task assesses the extent to which attention towards emotional content interferes with performance when responding to non-emotional content (Thomas, Johnstone, & Gonsalvez, 2007). On the other hand, the emotional spatial cueing paradigm elicits spatial attention and covert shifts of spatial attention to threatening stimuli (Stormark, Nordby, & Hugdahl, 1995), and the visual search paradigm elicits spatial attention to and enhanced capture of attention by threatening and emotional stimuli (Wieser, Hambach, & Weymar, 2018). The dot-probe task is often used to assess facilitated attention to threat, difficulty disengaging attention away from threat, and attentional avoidance; the emotional Stroop and emotional spatial cueing paradigms are well-suited for studying difficulty disengaging attention away from threat, and the visual search task has shown promise in studying difficulty disengaging attention away from threat and attentional avoidance of threat (Cisler & Koster, 2010). Although the downstream behavioral manifestations of attention may differ across these tasks, they have some commonalities at the neural level. For example, early and later ERPs can be elicited both in response to threatening stimuli and/or targets presented in an emotional context.
Bar-Haim et al. (2007) conducted an extensive meta-analysis of 172 behavioral attentional bias studies (N = 2,263 anxious, N = 1,768 non-anxious). The authors concluded that anxious, but not non-anxious, populations display a significant attentional bias to threat, and the magnitude of the bias is similar across various anxious populations (individuals with different clinical disorders, high-anxious nonclinical individuals, anxious children and adults). Additionally, anxious populations, but not control subjects, demonstrate significant threat-related bias in emotional Stroop, dot-probe, and emotional spatial cueing paradigms.
However, behavioral studies investigating the different components and time course of attentional bias are conflicting. As an example, Mogg et al. (1997) manipulated the exposure duration of word pairs (100 milliseconds (ms), 500 ms, 1500 ms) in a dot-probe task with non-clinical anxiety patients. Evidence was found to support state anxiety-related bias for threat stimuli averaged across all three exposure conditions and specifically in the shorter exposure condition of 100 ms, reflecting attentional vigilance for threat. However, the bias was not significantly affected by exposure duration of the word stimuli. That is, the attentional bias for threat did not appear to vary over this range (100–1500 ms) in non-clinical anxiety, failing to support the vigilance-avoidance hypothesis. Mogg, Bradley, Miles, & Dixon (2004) later conducted another dot-probe study using pictorial stimuli presented for 500 or 1500 ms and found that, compared to LTA participants, HTA participants were more vigilant for high-threat scenes at 500 ms, but showed no attentional bias at 1500 ms.
Unfortunately, behavioral measures such as RT are not sensitive enough to clarify the time course of hypervigilance toward or avoidance of threat-related stimuli. RTs provide an indirect measure of attentional processing (Horley, Williams, Gonsalvez, & Gordon, 2004) and can be confounded by post-perceptual processes such as motor responses and decision making (Handy, Green, Klein, & Mangun, 2001; Mueller et al., 2009). Fortunately, the use of ERPs allows for the examination of the time course of attention to threat with millisecond resolution (Kappenman, Farrens, Luck, & Proudfit, 2014; Kappenman, MacNamara, & Proudfit, 2015).
4. ERPs and Attentional Bias
Using the superior temporal resolution of ERPs, researchers have investigated the timing and neural substrates related to the processing of emotional stimuli, cues, and targets in attentional bias paradigms. Differential processing of emotional and threatening stimuli is inferred when one or more ERP features, such as amplitude, latency, and scalp distribution, differ from neutral stimuli. Amplitudes are generally assumed to signify the degree or intensity of the engagement of cognitive processes, and latencies are thought to measure the time course of stages of processing (Luck, Woodman, & Vogel, 2000). Peak amplitude and peak latency are measured at the maximum point (e.g., P1 component) or minimum point (e.g., N1 component) in a defined time window, but mean amplitude can also be measured by taking the average voltage over a specified measurement window (Luck, 2014). Each ERP component has a distinctive scalp distribution that reflects the location of the patch of cortex in which it was originally generated; however, it is difficult to determine the exact location of the neural generator source simply by examining the distribution of voltage over the scalp (Luck, 2014). Most ERP components relevant to attentional bias are modulated by emotional stimuli and spatial attention (Eldar, Yankelevitch, Lamy, & Bar-Haim, 2010). The early C1, P1, N1, N170, P2, N2, and N2pc components and the later P3 and LPP components have been most analyzed in the literature. Early components capture earlier stages of attentional processing, and thus are likely well suited to probe more automatic forms of attentional bias. Later components capture later stages of attentional processing, and thus may reflect more strategic forms of attentional bias. A brief description of each component follows.
C1: The C1 component is not labeled with a P or an N because its polarity can vary. It is thought to originate in the primary visual cortex (V1), and thus shows polarity inversion for stimuli presented in the upper visual field versus stimuli presented in the lower visual field, consistent with the retinotopic organization of V1 (Clark & Hillyard, 1996). The C1 is largest at posterior midline electrode sites (Luck, 2014). It is the first ERP component triggered by the appearance of a stimulus in the visual field and is thought to be pre-attentive and independent of spatial attention (Clark & Hillyard, 1996; Eldar et al., 2010; Luck et al., 2000). The C1 wave typically onsets 40–60 ms poststimulus and peaks 80–100 ms poststimulus (Luck, 2014). There is some evidence that the C1 is modulated by stimulus valence. For example, Stolarova, Keil, & Moratti (2006) demonstrated that, when black-and-white grating stimuli were coupled either with a series of unpleasant or neutral pictures, there was an enhanced negativity in the very early C1 component (65–90 ms post stimulus) for negatively conditioned stimuli.
P1: The C1 is followed by the P1, which is typically the first major visual ERP component putatively originating in the extrastriate visual cortex. It is largest at lateral occipital electrode sites and typically onsets 60–90 ms poststimulus with a peak between 100 and 130 ms (Luck, 2014). Allocation of attention to stimuli leads to an increased P1 amplitude (Clark & Hillyard, 1996). Studies have shown that unpleasant images can produce larger P1 amplitudes compared to both pleasant and neutral images (Carretié, Hinojosa, Martín‐Loeches, Mercado, & Tapia, 2004; Delplanque, Lavoie, Hot, Silvert, & Sequeira, 2004; Olofsson, Nordin, Sequeira, & Polich, 2008; Smith, Cacioppo, Larsen, & Chartrand, 2003).
N1: The P1 is followed by the N1, which consists of several visual N1 subcomponents that sum together to form the N1 peak. The earliest N1 subcomponent peaks 100–150 ms poststimulus at anterior electrode sites. There appear to be at least two posterior N1 components that peak 150–200 ms poststimulus; one arising from the parietal cortex and another arising from the lateral occipital cortex. Although all three subcomponents are influenced by spatial attention, discrimination of attended stimuli specifically enhances the lateral occipital N1 subcomponent (Luck, 2014). It has been shown that the N1 amplitude is larger for both pleasant and unpleasant compared to neutral images (Keil et al., 2001; Olofsson et al., 2008; Schupp, Junghöfer, Weike, & Hamm, 2003).
N170: One component of the N1 wave is the N170, which typically peaks around 170 ms after stimulus onset and is largest over ventral areas of the visual cortex (Luck, 2014). It has been shown that faces elicit a more negative potential than non-face stimuli at lateral occipital electrode sites, especially over the right hemisphere (Bentin, Allison, Puce, Perez, & McCarthy, 1996; Luck, 2014). The N170 is generally thought to be a face-specific ERP component (Bentin et al., 1996), and has also been shown to be sensitive to emotional expressions (Kolassa & Miltner, 2006). While some studies have observed that the N170 amplitude is enhanced for fearful compared to neutral facial expressions (Batty & Taylor, 2003; Blau, Maurer, Tottenham, & McCandliss, 2007), others have found that the component is unaffected by emotional expression (Eimer & Holmes, 2002). An enhanced positivity to faces, termed the “vertex positive potential” (VPP), usually accompanies the N170 component. The VPP is elicited at vertex electrode Cz in the same time range as the N170 component (Eimer, 2011; Rossion & Jacques, 2011).
P2: A distinct P2 wave follows the N1 at anterior and central scalp sites, but at posterior sites, the P2 is often difficult to distinguish from the overlapping N1, N2, and P3 waves (Luck, 2014). In visual attention research, the P2 has been suggested to indicate allocation of attentional resources during the processing of emotional facial expressions (Bar-Haim, Lamy, & Glickman, 2005; Eldar et al., 2010; Torrence & Troup, 2018). Some studies have shown that the P2 amplitude is enhanced to both unpleasant and pleasant pictures compared to neutral pictures (Carretié et al., 2004; Delplanque et al., 2004).
N2: The second major negative peak is the N2, and it is made up of several subcomponents including the N2a, N2b, and N2c (Luck, 2014). The posterior N2 (N2c) (Luck, 2014) has been associated with discrimination and classification of visual stimuli. The peak latency of the component varies as a function of the discrimination difficulty and correlates with the timing of discriminative behavioral responses (Ritter, Simson, Vaughan, & Macht, 1982). The N2 has been observed in studies involving processing of emotional content (Sass et al., 2010). While some studies have shown that the N2 amplitude is enhanced to pleasant and unpleasant compared to neutral pictures (Schupp et al., 2003), others have observed that unpleasant stimuli elicit a decreased N2 negativity compared to pleasant stimuli (Carretié et al., 2004).
N2pc: The N2pc (N2-posterior-contralateral) is a subcomponent of the posterior N2, typically occurring between 200 and 300 ms. It is observed at posterior scalp sites contralateral to an attended object. Therefore, the N2pc is useful for determining whether attention has been covertly directed to a given object and for assessing the time course of attentional orienting (Luck, 2014). Interestingly, it has been shown that even task-irrelevant fearful faces, but not neutral faces, elicit an N2pc (Eimer & Kiss, 2007).
P3: The P3 peaked around 300 ms when it was first discovered (Sutton, Braren, Zubin, & John, 1965), but has since been found to peak anywhere between 350 and 600 ms (Luck, 2014). Although there is no clear consensus on which neural or cognitive processes are reflected by the P3 wave (Luck, 2014), the effect of various manipulations on P3 amplitude and latency have been well explored (Luck, 2014). The P3 amplitude is influenced by the amount of attention allocated to a stimulus (Luck & Kappenman, 2011); the amplitude is larger when subjects devote more effort to a task. Additionally, the hallmark of the P3 wave is its sensitivity to target probability; the amplitude increases as the target probability decreases. The P3 latency is thought to reflect the time required to categorize a stimulus and is insensitive to subsequent response-related processes (Luck, 2014). Distinguishable ERP components in the time range of the P3 wave include the frontally maximal P3a component and a parietally maximal P3b component. Although both subcomponents are elicited by unpredictable, infrequent changes in the stimuli, the P3b component is present only when these changes are task-relevant. Researchers generally use the term “P3” to refer to the P3b component (Luck, 2014). It has been shown that P3 amplitudes are enhanced to both unpleasant and pleasant compared to neutral pictures (Olofsson et al., 2008; Radilova, 1982; Radilova, Figar, & Radil, 1983).
LPP: The late positive potential (LPP), thought to be related to P3, is a central-parietal, midline component that becomes evident approximately 300 ms following stimulus onset, and similar to P3, is larger following the presentation of both pleasant and unpleasant compared to neutral pictures and words (Hajcak, Dunning, & Foti, 2009). Unlike the more transient P3, the LPP can be sustained for several seconds following the presentation of emotional stimuli and even in the period following stimulus offset (Hajcak et al., 2009).
To clarify the neural chronometry of attentional bias, an interpretive synthesis (Dixon-Woods et al., 2006; Gough, Thomas, & Oliver, 2012) was conducted on ERP studies of attentional bias focusing primarily on anxious populations, as anxiety disorders provide an excellent model in which to study attentional bias. Results focusing on healthy control, non-anxious, or low-anxious populations are also included to understand the “typical” pattern of attentional bias and how it is altered in anxiety. Studies were identified through a May 2019 literature search conducted in Google Scholar, PsychINFO, and PubMed using a combination of the following terms with no limit on publication date: “attentional bias”, “event-related potential” or “ERP”, and “anxiety”. To be included after the initial search, studies had to (1) be peer-reviewed, (2) be empirical, (3) focus on adult human participants aged 18 or above, (4) use a classic dot-probe, emotional Stroop, emotional spatial cueing, or visual search task concurrently with EEG recording in order to examine ERPs, (5) measure the C1, P1, N1, N170, P2, N2, N2pc, P3, and/or LPP components, (6) utilize threatening visual stimuli (e.g., faces, scenes, words), and (7) only include one time point (no interventions or inductions—interventions are discussed in the Discussion section). (8) In cases where studies utilized the DSM to characterize clinically anxious groups, only DSM-5 anxiety disorders were included (e.g., not PTSD or OCD). 28 studies were collected in total (14 dot-probe paradigms, 8 emotional Stroop paradigms, 3 emotional spatial cueing paradigms, and 3 visual search paradigms); 20 of these included an anxious population, while 8 solely focused on healthy populations. The findings are presented below by paradigm and by whether they examine early or later ERP components. The goal of the current review is to provide an ‘interpretive synthesis’, a form of review in which meanings are interpreted from the included studies, and concepts and theories that integrate those concepts are developed (Dixon-Woods et al., 2006; Gough et al., 2012). Findings from the 28 studies meeting our inclusion criteria are thus integrated in order to develop an ERP model of the neural chronometry of attentional bias in healthy and anxious participants (Figure 1). A summary of reviewed dot-probe articles is presented in Table 1, emotional Stroop articles are presented in Table 2, emotional spatial cueing articles are presented in Table 3, and visual search articles are presented in Table 4.
TABLE 1:
SUMMARY OF REVIEWED DOT-PROBE ARTICLES
| Author | Journal | Stimuli | Population & Sample Size | Main Screening/Grouping Questionnaire(s) | ERP Findings Presented in this Review (* & bold font = p ≤ .05) |
|---|---|---|---|---|---|
| Eldar et al. (2010) | Biological Psychology | Faces (angry, happy, neutral) |
|
State-Trait Anxiety Inventory-Trait Scale (STAI-T) (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983)
|
|
| Pourtois, Grandjean, Sander, & Vuilleumier (2004) | Cerebral Cortex | Faces (fearful, happy, neutral) |
|
|
|
| Santesso et al. (2008) | Neuropsychologia | Faces (angry, happy, neutral) |
|
|
|
| Fox, Derakshan, & Shoker (2008) | Neuroreport | Faces (angry, happy, neutral) |
|
STAI-T
|
|
| Helfinstein, White, Bar-Haim, & Fox (2008) | Behaviour Research and Therapy | Priming words (socially threatening, neutral) Faces (angry, neutral) |
|
Revised Cheek and Buss Shyness Scale (RCBS) (Cheek, 1983)
|
|
| Rossignol, Campanella, Bissot, & Philippot (2013) | Brain and Cognition | Faces (neutral, anger, disgust, happiness, fear) |
|
Fear of Negative Evaluation (FNE) Scale (Watson & Friend, 1969)
|
|
| Mueller et al. (2009) | Psychological Medicine | Faces (angry, happy, neutral) |
|
|
|
| Kappenman et al. (2015) | Social cognitive and affective neuroscience | Neutral and threatening International Affective Picture System (IAPS) (Lang et al., 1997) images |
|
|
|
| Kappenman, Farrens, et al. (2014) | Frontiers in Psychology | Threatening and neutral IAPS images |
|
|
|
| Pintzinger, Pfabigan, Pfau, Kryspin-Exner, & Lamm (2017) | Biological Psychology | Emotional-neutral picture pairs (complex social scenes) |
|
|
|
| Zhang, Dong, & Zhou (2018) | Neural Plasticity | Test-related-neutral (TR-N) word pairs, test-unrelated-neutral (TU-N) word pairs |
|
Test Anxiety Inventory (short form) (Taylor & Deane, 2002)
|
|
| Reutter, Hewig, Wieser, & Osinsky (2017) | Psychophysiology | Faces (angry and neutral) |
|
Online screening questionnaire for social anxiety consisting of five items on a 5-point scale based on the DSM-IV criteria for social phobia (Ahrens, Mühlberger, Pauli, & Wieser, 2015; Wieser & Moscovitch, 2015)
|
|
| Pfabigan, Lamplmayr-Kragl, Pintzinger, Sailer, & Tran (2014) | Frontiers in Psychology | Faces (angry, disgusted, fearful, happy, sad, neutral) |
|
|
|
| Holmes, Bradley, Kragh Nielsen, & Mogg (2009) | Psychophysiology | Faces (angry, happy, neutral) |
|
|
|
TABLE 2:
SUMMARY OF REVIEWED EMOTIONAL STROOP ARTICLES
| Author | Journal | Stimuli | Population & Sample Size | Main Screening/Grouping Questionnaire(s) | ERP Findings Presented in this Review (* & bold font = p ≤ .05) |
|---|---|---|---|---|---|
| Li, Zinbarg, & Paller (2007) | Cognitive, Affective, & Behavioral Neuroscience | Words (neutral, threatening) |
|
Behavioral Inhibition Scale (BIS) (Carver & White, 1994)
|
|
| Sass et al. (2010) | Psychophysiology | Words (threatening, pleasant, neutral) |
|
Penn State Worry Questionnaire (PSWQ) (Meyer, Miller, Metzger, & Borkovec, 1990; Molina & Borkovec, 1994)
|
|
| Kolassa, Kolassa, Musial, & Miltner (2007) | Cognition & Emotion | Schematic faces depicting angry, happy, and neutral facial expressions |
|
Structured Clinical Interview for DSM-IV (SCID-IV) (Wittchen, Wunderlich, Gruschwitz, & Zaudig, 1997) |
|
| Kolassa & Miltner (2006) | Brain Research | Faces (angry, happy, neutral) |
|
SCID-IV |
|
| Thomas, Johnstone, & Gonsalvez (2007) | International Journal of Psychophysiology | Words (personally threatening, neutral) |
|
N/A |
|
| Peschard, Philippot, Joassin, & Rossignol (2013) | Biological Psychology | Faces (angry, happy, neutral). Faces were upright, inverted, or superimposed with a colored mask Colored rectangles (for control task) |
|
Liebowitz Social Anxiety Scale (LSAS) (Liebowitz, 1987)
|
|
| Kolassa, Musial, Kolassa, & Miltner (2006) | BMC Psychiatry | Pictures of schematic spiders and flowers, colored red or blue |
|
SCID-IV |
|
| Fisher et al. (2010) | Emotion | Positive, negative, and neutral words |
|
Penn State Worry Questionnaire (PSWQ) (Meyer et al., 1990; Molina & Borkovec, 1994) Anxious arousal and anhedonic depression scales of the Mood and Anxiety Symptom Questionnaire (MASQ) (Watson, Clark, et al., 1995; Watson, Weber, et al., 1995) |
|
TABLE 3:
SUMMARY OF REVIEWED EMOTIONAL SPATIAL CUEING ARTICLES
| Author | Journal | Stimuli | Population & Sample Size | Main Screening/Grouping Questionnaire(s) | ERP Findings Presented in this Review (* & bold font = p ≤ .05) |
|---|---|---|---|---|---|
| Li, Li, & Luo (2005) | Neuroreport | IAPS images (threatening, nonthreatening) |
|
STAI
|
|
| Rossignol, Philippot, Bissot, Rigoulot, & Campanella (2012) | Brain Research | Faces (neutral, angry, disgusted, happy, fearful) |
|
FNE Scale
|
|
| Stormark, Nordby, & Hugdahl (1995) | Cognition and Emotion | Emotionally negative and neutral words |
|
|
|
TABLE 4:
SUMMARY OF REVIEWED VISUAL SEARCH ARTICLES
| Author | Journal | Stimuli | Population & Sample Size | Main Screening/Grouping Questionnaire(s) | ERP Findings Presented in this Review (* & bold font = p ≤ .05) |
|---|---|---|---|---|---|
| Weymar, Keil, & Hamm (2014) | Social Cognitive and Affective Neuroscience | Arrays containing pictures of spiders, butterflies, and flowers |
|
Spider Phobia Questionnaire (SPQ) (Hamm, 2006)
|
|
| Weymar, Gerdes, Low, Alpers, & Hamm (2013) | Psychophysiology | Arrays containing pictures of spiders, butterflies, and flowers |
|
SPQ
|
|
| Wieser, Hambach, & Weymar (2018) | Biological Psychology | Arrays containing happy, angry, and neutral faces |
|
Pre-screening questionnaire consisting of five items (Ahrens et al., 2015) based on the DSM-IV criteria for social phobia (American Psychiatric Association, 2013) on a 5-point Likert scale.
|
|
Recently, Torrence & Troup (2018) published a systematic review on ERPs of attentional bias toward faces in the dot-probe task. The review included 23 articles, 15 of which used a general sample (e.g., did not select or examine subclinical and/or clinical differences) and 8 of which used subclinical and/or clinical samples. Specifically, 4 studies examined anxiety, 3 studies examined social anxiety, and 1 study examined the effects of escitalopram on attentional bias in panic disorder. Results strongly indicated an attentional bias toward threatening stimuli in general samples and high-anxious samples, with attentional bias toward fearful and angry facial expressions seen in early ERPs time-locked to face onset, such as the N170 and N2pc. Additionally, some studies examining the P1 time-locked to target onset observed that, for angry and fearful congruent trials, the P1 amplitude was enhanced, indicating that attention was allocated toward the location of the threatening facial expression and was sustained in that location when the target appeared. However, the findings suggested that, although ERPs may be more reliable and consistent than measures based on RTs, there are inconsistencies in the literature. The authors posit that observed discrepancies could have resulted from the different facial expressions, methodologies, and samples used, as well as the review’s focus on studies of facial expressions. The present interpretive synthesis attempts to address some of these issues by including a variety of paradigms and stimuli used to assess attentional bias at the neural level. Presented findings are then integrated to develop a model representing the neural chronometry of attentional bias in healthy and anxious populations across paradigms and stimuli.
4.1. Dot-Probe Studies
The dot-probe task (MacLeod et al., 1986) is used to assess attentional bias in spatial orienting to threatening cues (Mogg & Bradley, 2016). In the task, two visual (e.g., words, faces, scenes) stimuli, called cues, are briefly and simultaneously presented above and below or to the left and right of a fixation cross. One cue is emotional or threatening and the other is neutral. After the cues disappear, a probe, or target (e.g., a dot or bar), appears in the location of one of the cues. Participants must quickly and accurately respond to the location or identity of the probe. Faster responses to probes are observed when they occur in the attended rather than the unattended location (Navon & Margalit, 1983). Thus, participants displaying attentional bias toward threat will typically demonstrate faster responses to probes that appear in the location of a threatening stimulus compared to a neutral stimulus (Bar-Haim et al., 2007; Van Bockstaele et al., 2014). The dot-probe has been deemed by some as the gold-standard in behavioral attentional bias research because participants respond to the neutral probe instead of the word or picture stimuli, thus eliminating response bias interpretations.
In this paradigm, ERPs time-locked to the presentation of cues and probes are typically analyzed separately. Amplitude or latency modulations of ERPs time-locked to cues may indicate attentional bias occurring at early stages of processing, whereas modulations of ERPs time-locked to probes may indicate attentional bias occurring at later stages of processing. In the following section, results from dot-probe studies are discussed, separated by component and by findings for cues and probes.
Emotional Cues
Early ERP Components
C1. Eldar et al. (2010) used the trait scale of the State-Trait Anxiety Inventory (STAI-T) (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983) to form anxious and nonanxious groups. The authors measured attentional bias toward threat, neutral, and positive face pair stimuli and found that anxious participants had a more distinct C1 negativity compared to nonanxious participants solely in threat conditions, where angry-neutral face pairs were presented. This suggests that, at the level of V1, anxious individuals have perturbations in early, pre-attentive threat processing. Similarly, when Pourtois et al. (2004) conducted a dot-probe task with healthy participants using emotional-neutral face pairs, they observed an enhanced negative C1 component with striate origins for fearful compared to happy faces. This provides further evidence that, as early as 90 ms post-stimulus presentation, V1 activity is enhanced by fear cues. However, inconsistent with Pourtois et al. (2004), Santesso et al. (2008) observed that the C1 elicited by neutral, happy, and angry face pair stimuli was not modulated by emotional valence or attention in healthy adults. Santesso et al. (2008) suggest that the use of angry versus fearful faces may have altered the results, and the C1 may not be a consistent measure of either early emotion-related neural activation arising from V1 or selective attention towards emotionally significant stimuli.
P1. In their study with anxious and non-anxious groups, Eldar et al. (2010) did not observe significant effects of group or emotion on the P1 time-locked to presentation of the faces. Zhang et al. (2018) used test-related-neutral (TR-N) and test-unrelated-neutral (TU-N) word pair cues with high and low-test anxious groups. The authors also observed no significant effect of threat or anxiety levels on the P1 component time-locked to cue onset; thus, there may have been no difference between the threat level of the test-related and test-unrelated threatening words used in the study. Several other studies focus on individuals with social anxiety symptoms or clinical SAD, who have a persistent fear of one or more social or performance situations in which they are exposed to unfamiliar people or possible scrutiny by others (DSM-5 American Psychiatric Association, 2013). Helfinstein et al. (2008) observed that high socially anxious (HSA) individuals displayed higher mean P1 amplitudes to the angry-neutral face pairs compared to low socially anxious (LSA) individuals. The enhanced P1 may indicate increased sensory processing of faces in individuals with high levels of social anxiety. Mueller et al. (2009) also demonstrated that, compared to healthy controls, SAD participants had potentiated P1 amplitudes to angry-neutral versus happy-neutral face pairs, suggesting an early hypervigilance to angry faces in SAD. Rossignol et al. (2013) observed that HSA individuals displayed increased P1 amplitudes in response to neutral-emotional face pairs (neutral–angry, neutral–happy, neutral–disgust and neutral–fear), irrespective of the emotional expression included in the pair. These results indicate that, in social anxiety, a generalized hypervigilance to emotional faces occurs. However, Santesso et al. (2008) observed no significant effects of face emotion on the P1 elicited by emotional-neutral face pairs in healthy adults. Similarly, Pourtois et al. (2004) did not observe P1 modulations to face pair presentation in healthy adults. Altogether, these results suggest that the P1 component may be sensitive specifically to pathological forms of early attentional bias. Pintzinger et al. (2017) conducted a dot-probe study with healthy individuals using emotional-neutral complex scene pairs. They observed that P1-cue amplitudes were more positive among women than men, suggesting that women allocated more attention to emotional stimuli.
N1. In their study with anxious and non-anxious groups, Eldar et al. (2010) did not observe significant effects of emotion or group on the N1 time-locked to presentation of angry, happy, and neutral faces. Helfinstein et al. (2008) showed LSA and HSA groups either a neutral or socially threatening prime word prior to presentation of angry-neutral face pairs and probes, and observed that the LSA group displayed larger N1 mean amplitudes to face display onset compared to the HSA group. However, regardless of anxiety group, participants showed a trend towards greater N1 mean amplitudes for faces displayed on threat prime trials compared to neutral prime trials. These results suggest differential processing of the face displays between groups, regardless of prime condition. In healthy populations, however, Pintzinger et al. (2017) observed significant enhancements in N1-cue amplitudes for neutral social scenes (compared to negative social scenes) in women, potentially indicating an avoidance of threatening stimuli.
N170. In the study conducted by Rossignol et al. (2013) with HSA and LSA groups, the N170 did not appear sensitive to anxiety levels nor the emotional load of emotional-neutral face pairs. Pourtois et al. (2004) also did not observe N170 modulations to fear-neutral or happy-neutral face pairs in healthy adults. Similarly, in healthy adults, Santesso et al. (2008) found that the N170 elicited to face stimuli was not modulated by emotional valence.
P2. Eldar et al. (2010) demonstrated that, compared to non-anxious individuals, anxious individuals displayed enhanced occipital P2 amplitudes in response to face displays, regardless of whether the facial emotion was angry, happy, or neutral. These results suggest that modulation of the P2 amplitude serves as an indicator of attentional commitment to processing facial emotional expressions. Studies focusing on socially anxious populations offer similar results. In the study conducted by Rossignol et al. (2013), HSA individuals displayed enhanced P2 amplitudes in response to angry-neutral compared to fear-neutral face pairs, which may indicate enhanced allocation of attention to angry faces. Indeed, Helfinstein et al. (2008) showed that, compared to individuals with LSA, individuals with HSA displayed a trend toward more positive P2 mean amplitudes to face display onset. Additionally, regardless of anxiety group, participants showed a trend towards smaller P2 mean amplitudes in response to face display on threat prime trials compared to neutral prime trials. As mentioned above, in this study, primes were presented prior to face display and probe occurrence. These results suggest that threat priming reduces attentional bias toward the subsequent faces.
N2pc. In HTA and LTA groups, Fox et al. (2008) observed that angry expressions elicited an enhanced N2pc, but only in participants reporting high levels of trait anxiety. These results suggest that HTA participants exhibit rapid exogenous orienting of spatial attention to threatening cues, supporting the theoretical view that anxiety is associated with an enhanced early shift of attentional resources toward threat. Other studies have utilized non-face stimuli. For example, Kappenman, Farrens, et al. (2014) collected a large sample of participants with individual differences in anxiety. Using threatening and neutral International Affective Picture System (IAPS) (Lang, Bradley, & Cuthbert, 1997) pictures in their dot-probe task, they examined the N2pc time-locked to the onset of the image pairs and observed that, following the onset of threat-neutral image pairs, the N2pc was elicited to the location of the threatening stimulus, reflecting a shift of covert visual attention in the direction of the threatening image. However, the N2pc was not correlated with trait anxiety, thus failing to provide a meaningful index of individual differences in anxiety in the dot-probe task. In a group of socially anxious individuals, Reutter et al. (2017) found that the N2pc revealed an attentional bias toward angry faces and showed excellent odd-even reliability. Higher (i.e., more negative) N2pc amplitudes and earlier peak latencies were associated with more severe symptoms of social anxiety, even when controlling for general trait anxiety. These results indicate that angry faces, as a depiction of negative social evaluation, drive attentional deployment in socially anxious samples. Holmes et al. (2009) conducted a study with healthy individuals focusing on the N2pc-cue. The authors observed that attentional orienting to threatening faces emerged earlier (early N2pc time window; 180–250 ms) than orienting to positive faces (after 250 ms), and attention was sustained toward emotional faces during the 250–500-ms time window (late N2pc component). These results indicate that healthy populations exhibit rapid attentional orienting toward angry faces and delayed attentional capture by happy faces, but then maintain attention toward emotional faces during the later part of the stimulus face cue presentation. Kappenman et al. (2015) conducted a dot-probe study with healthy participants using threatening and neutral IAPS pictures and also observed an initial shift of attention to threat-related stimuli reflected by the N2pc. Therefore, the N2pc trends observed in Torrence & Troup’s (2018) review may apply to threatening scenes, as well as emotional faces.
Later ERP Components
LPP. Few studies have examined later ERPs using the dot-probe task, possibly because of the brief stimulus duration times used for cue presentation. In one exception, Kappenman et al. (2015) conducted a dot-probe task with unselected individuals and observed that there was no evidence of sustained engagement with the threatening IAPS images as measured by the LPP. These results suggest that the dot-probe task is better at eliciting earlier rather than later stage neural markers of attentional bias.
The main conclusions from dot-probe cue-locked findings presented are: (1) Compared to controls, socially anxious subjects display potentiated P1-cue amplitudes to threatening and emotional faces, reflecting a generalized hypervigilance to emotional faces. Interestingly, these modulations are not reliably observed in healthy populations. (2) Individuals with social anxiety and high levels of trait anxiety display enhanced P2 amplitudes to emotional face displays, indicating enhanced allocation of attention to angry faces. (3) HTA, socially anxious, and healthy populations display N2pc amplitude or latency modulations to threatening and other emotional faces, reflecting rapid orienting to threatening and emotional cues, in general.
Probes Following Emotional Cues
Early ERP Components
Whereas modulations of ERPs time-locked to cues may indicate attentional bias occurring at early stages of processing, modulations of ERPs time-locked to probes may indicate attentional bias occurring at later stages of processing.
C1 & P1. In their dot-probe study with healthy subjects, Santesso et al. (2008) found that the C1 elicited to the probe was not modulated by the preceding emotional face pair or by validity (i.e., whether the probe replaced the emotional (valid) or neutral (invalid) face in the pair). However, Fox et al. (2008) presented happy-neutral and angry-neutral face pairs to HTA and LTA groups and observed enhanced P1 amplitudes to targets following presentation of angry expressions, but this effect was not modulated by trait anxiety levels. In contrast, in their study with anxious and non-anxious groups, Eldar et al. (2010) failed to find threat-related differences in the P1 component locked to the target processing phase of their dot-probe task. Clearly, these three studies offer conflicting results regarding early ERPs time-locked to probes following emotional cues.
Interestingly, Mueller et al. (2009) demonstrated that, compared to controls, SAD participants displayed decreased P1 amplitudes to probes replacing emotional (angry and happy) versus neutral faces. The authors propose that these results indicate reduced visual processing of emotionally salient locations at later stages of information processing—potentially a manifestation of attentional avoidance. Additionally, these results suggest that hypervigilance observed in response to the cue may prime early forms of strategic avoidance as measured by the P1 to the probe. In contrast, Rossignol et al. (2013) observed that, while LSA subjects showed similar responses to targets following neutral or emotional faces, HSA individuals showed enhanced P1 amplitudes to targets replacing emotional faces, indexing sustained attention for emotional face location in individuals with high levels of social anxiety. Thus, socially anxious populations, compared to populations with other forms of anxiety, may more reliably demonstrate modulations of the P1 component elicited to probes following emotional cues.
Using angry-neutral and happy-neutral face pairs, Santesso et al. (2008) found that the P1 amplitude was larger for validly cued probes following angry faces than invalid probes, confirming that threatening cues can modulate spatial attention in healthy adults. They also observed larger P1 amplitudes for invalidly cued probes following happy faces compared to validly cued probes or validly cued neutral faces. This result suggests that, for happy-neutral face pairs, attention was directed toward the relatively more threatening or more emotionally ambiguous stimulus within the visual field (the neutral face). In healthy adults, Pourtois et al. (2004) also found that the lateral occipital P1 component occurring approximately 130 ms poststimulus selectively increased when the probe replaced a fearful face compared to a neutral face. This effect was not found for upright happy faces or inverted fearful faces, indicating facilitated processing of targets presented in the location of fearful faces. In healthy adults, Pintzinger et al. (2017) observed that P1-probe peak amplitudes were significantly enhanced in negative compared to positive social scene conditions. Latencies were prolonged in negative compared to positive conditions, irrespective of congruence or gender. These results suggest that there was greater processing during initial and later stages in negative compared to positive conditions. In healthy populations, Pfabigan et al. (2014) observed that women showed overall enhanced P1-probe amplitudes compared to men, particularly after rewarding (i.e., happy) facial stimuli, suggesting that women orient their attention to a greater extent to facial stimuli than men.
N1. In healthy adults, Santesso et al. (2008) demonstrated that the N1 amplitude elicited by the probe was not modulated by cue validity, suggesting that no covert, visuospatial orienting of attention toward threat occurred. Pintzinger et al. (2017) found that N1-probe amplitudes were significantly enhanced for positive compared to negative social scene pairs in healthy adults, reflecting stronger covert visuospatial orienting toward positive compared to negative stimuli. Thus, attentional avoidance of negative information and stronger engagement with positive information might reflect automatic protective strategies that prevent individuals from ruminating about negative information.
P2. In healthy adults, Pintzinger et al. (2017) observed that P2-probe amplitudes were enhanced after negative compared to positive social scenes. In negative conditions, women showed enhanced P2 amplitudes in congruent compared to incongruent conditions. In congruent conditions, P2 amplitudes were enhanced after negative compared to positive pictures. These results collectively indicate that the participants exhibited more elaborative processing and emotional evaluation of negative stimuli, likely leading to difficulty disengaging from negative stimuli.
N2. In the study conducted by Zhang et al. (2018), in the low-test anxious group, N2-probe amplitudes in the incongruent TR-N condition were significantly less negative than those in the incongruent TU-N condition. For the high test-anxious group, the N2-probe amplitudes in the incongruent TR-N condition were marginally more negative than those in the incongruent TU-N condition. These results indicate that the high test-anxious group displayed attentional bias toward test-related threat stimuli, while low test-anxious individuals displayed avoidance of these stimuli.
Later ERP Components
LPP. In the study conducted by Zhang et al. (2018), the low test-anxious group elicited a larger LPP compared with the high test-anxious group. In the congruent condition, a significantly higher LPP-probe amplitude was elicited by the test-unrelated-neutral (TU-N) word pairs relative to the test-related-neutral (TR-N) word pairs. These results suggest that, after initially shifting attention to threatening stimuli (indexed by the N2 results discussed above), high test-anxious individuals did not show sustained attention to the test-related threat stimuli. This pattern could result from anxious individuals attempting to counterbalance their initial attention to threat by quickly disengaging attention away from threat.
The main conclusion for dot-probe probe-locked findings presented herein is that healthy populations display enhanced P1 amplitudes for probes replacing threatening and fearful faces and negative compared to positive social scenes. These results reflect facilitated processing of targets presented in the location of threat-related stimuli. Results regarding the P1-probe are equivocal in trait anxious and socially anxious populations.
4.2. Emotional Stroop Studies
Studies utilizing the dot-probe task are complemented by results from conceptually similar attentional bias tasks, such as the emotional Stroop task. The emotional Stroop task, a modified version of the original Stroop paradigm (Stroop, 1935), assesses interference effects of task-irrelevant threat on performance (Mogg & Bradley, 2016; Williams, Mathews, & MacLeod, 1996). The task utilizes a similar design as the original with one important change: threatening or neutral stimuli (e.g., words or faces) are presented and participants are required to report a particular stimulus quality (e.g., the color of the words or the gender of the faces) while ignoring the semantic or emotional content of the stimuli. Biased attention for threat is inferred when reporting is slower or less accurate for threatening stimuli than for non-threatening stimuli (Van Bockstaele et al., 2014). In the emotional Stroop paradigm, ERPs are often time-locked to presentation of the threatening and neutral stimuli.
Early ERP Components
P1. Using HTA and LTA groups, Li et al. (2007) observed an enhancement of occipital P1 amplitudes to threat words that was more prominent among people with higher levels of trait anxiety. This modulation can be interpreted as a signal of unconscious processing, as it was early and independent of whether word exposure was subliminal or supraliminal. Sass et al. (2010) studied anxious apprehension, anxious arousal, and control groups. They also demonstrated that, compared to the control group, the anxious arousal group showed an early processing bias evidenced by larger P1 amplitudes to emotionally arousing words; however, the P1 was specifically enhanced for pleasant stimuli, indicating preferential attention to emotionally arousing, but not threatening, stimuli. Additionally, in the anxious arousal group, women had larger P1 amplitudes than men regardless of emotional content, evidencing greater early visual processing in females. Fisher et al. (2010) used anxious apprehension, anxious arousal, anhedonic depression, comorbid, and control groups. In contrast, they observed no correlations between the P1 component and measures of psychopathology or perceived emotional intelligence scores. Results from socially anxious populations are generally consistent.
In HSA and LSA groups, Peschard et al. (2013) observed that the HSA group exhibited larger mean P1 amplitudes for all faces compared to the LSA group, demonstrating a general enhancement of perceptual processes in the HSA group, regardless of the emotional or social nature of the stimuli. Similarly, in spider phobic, social phobic, and control groups, Kolassa et al. (2006) observed that social and spider phobics generally elicited larger P1 amplitudes compared to controls. These results reflect increased (cortical) hypervigilance for incoming stimuli (schematic spiders and flowers) in phobic subjects. Kolassa et al. (2007) conducted a study where social phobic, spider phobic, and control groups had to identify either the color or emotional quality of angry, happy, and neutral schematic faces. Social phobics displayed generally larger P1 amplitudes compared to spider phobics and non-phobic controls, but behaviorally, schematic angry faces were not identified significantly faster than happy or neutral faces. The P1 component is putatively sensitive to attentional allocation; therefore, these results may indicate the presence of hypervigilance in social phobic participants, likely due to the performance situation posed by the experiment. However, a study by Kolassa & Miltner (2006) demonstrated that the early visual P1 amplitude did not differ between social phobic, spider phobic, and non-phobic control groups in response to angry faces. However, in all groups, the P1 amplitude was larger in response to emotional compared to neutral faces.
N170. In the study conducted by Kolassa et al. (2007), social phobics, spider phobics, and controls identified either the color (modified Stroop task) or the emotional quality (emotion identification task) of angry, happy, and neutral schematic faces. In all groups, emotional schematic faces led to larger N170 amplitudes than neutral schematic faces in the color identification task. This effect was even more pronounced in the emotion identification task, with angry schematic faces eliciting even larger N170 amplitudes than happy schematic faces. Similarly, Kolassa et al. (2006) observed that social phobic, spider phobic, and control groups displayed larger N170 amplitudes for schematic spiders compared to schematic flowers. This result potentially reflects either a general advantage for fear-relevant compared to neutral stimuli, or it may be due to a higher level of expertise in processing schematic spiders as compared to schematic flowers. In another study conducted by Kolassa & Miltner (2006), socially phobic and non-phobic individuals identified either the gender (modified emotional Stroop task) or the expression of angry, happy, and neutral faces. Social phobics displayed larger N170 amplitudes over right temporo-parietal sites compared to controls when identifying the emotion of an angry face, and higher social anxiety severity was associated with larger N170 amplitudes in response to angry faces. With these results, the authors suggest that social phobics show abnormalities in early visual processing of angry faces. RTs did not indicate the expected emotional interference in patients with social phobia when identifying the gender of an angry face. In contrast, in their study with HSA and LSA groups, Peschard et al. (2013) found no effects of emotion or group on the N170 mean amplitude.
P2. In their study with anxious apprehension, anxious arousal, anhedonic depression, comorbid, and control groups, Fisher et al. (2010) observed that attention to emotion was correlated with the P2 component for all stimuli (positive, negative, and neutral words). However, no group differences and no main effects or interactions of emotion with group were observed. Kolassa & Miltner (2006) also demonstrated that social phobics show no deviations from controls in P2 amplitudes elicited to angry faces. Similarly, in their task with HSA and LSA groups, Peschard et al. (2013) observed no effects of emotion or group on P2 mean amplitudes; thus, these results do not evidence greater mobilization of attentional resources by faces in socially anxious individuals. However, in healthy individuals, Thomas et al. (2007) observed larger P2 amplitudes elicited to threat words in the right compared to the left hemisphere, suggesting that threatening words require more intense processing during a relatively early, automatic, stage by specialized brain networks that are unevenly distributed between the cerebral hemispheres. The authors indicate that, although hemispheric differences in ERPs have not been consistently identified, some data suggest that the right hemisphere is more involved than the left in processing negative emotional information (Heller, Nitschke, & Miller, 1998; Thomas et al., 2007).
N2. In their study with anxious apprehension, anxious arousal, and control groups, Sass et al. (2010) observed enhanced N2 amplitudes to emotionally arousing words in the anxious apprehension group, reflecting an early processing bias (e.g., early discriminative processing) for emotional words.
Later ERP Components
P3. Comparing HTA and LTA groups, Li et al. (2007) observed that the P3 amplitude was enhanced for threat versus neutral words, and the enhancement increased with higher trait anxiety in the subliminal, but not the supraliminal condition of the experiment. The authors suggest that a later stage of threat processing subject to dynamic interactions between automatic and strategic influences is intensified in individuals prone to anxiety. Sass et al. (2010) demonstrated that anxious apprehension, anxious arousal and control groups display equivalent processing of emotional words as indexed by the P3 amplitude, with an enhanced P3 component associated with emotional arousal. Fisher et al. (2010) observed later P3 latencies for positive, compared to negative and neutral, words. The main effect of anxiety/depression group was not significant. These results indicate that positive stimuli were evaluated later in time, possibly because the participants required less time to evaluate negative stimuli because they were already primed for negative information. However, Kolassa et al. (2007) observed that individuals with social phobia do not show larger P3 amplitudes in response to angry faces. In healthy adults, Thomas et al. (2007) observed larger P3 amplitudes for threat words, indicating more thorough or intense processing of these stimuli during higher level, controlled stages of cognition.
LPP. Kolassa et al. (2007) observed no abnormalities in LPPs in individuals with social phobia when processing angry schematic faces. In contrast, Kolassa et al. (2006) found that spider phobics showed enhanced LPP amplitudes in response to schematic spiders compared to schematic flowers in the object identification task. These results suggest that schematic spiders trigger meaning-related evaluative processes in the brains of spider phobic persons similar to those observed when processing real spider pictures.
Conclusions from emotional Stroop findings presented are: (1) Compared to LSA and control groups, social phobics generally display larger P1 amplitudes for incoming stimuli (e.g., faces). However, no advantage for threatening stimuli is observed. These results signify that socially anxious individuals display increased hypervigilance for incoming stimuli in general. P1 trends are inconsistent for trait anxious and anxious arousal populations. (2) Social phobics, spider phobics, and controls generally show enhanced N170 amplitudes for emotional and angry faces and other threatening stimuli, with one study specifically finding an increased effect in socially anxious compared to control participants. These results could reflect either a general advantage for threatening versus neutral stimuli, or it may be due to a higher level of expertise in processing the threatening stimuli.
4.3. Emotional Spatial Cueing Paradigms
The emotional spatial cueing paradigm assesses biases in shifting and disengagement of attention (Fox et al., 2001; Mogg & Bradley, 2016). In the paradigm, participants focus on a fixation point located between two rectangles. Subsequently, a cue (e.g., a threatening or neutral stimulus) appears in one of the two rectangles, and after its disappearance, a target appears in one of the two rectangles. Participants are required to quickly and accurately indicate the identity or location of the target. Valid trials are those in which the target appears at the location that was previously cued, while invalid trials are those in which the target appears at the location not previously cued. Attentional bias to threat is indicated by faster responses on valid threat-cued trials relative to neutral-cued trials and slower responses on invalid threat-cued trials relative to neutral-cued trials (Cisler & Koster, 2010; Van Bockstaele et al., 2014). In this paradigm, ERPs are time-locked to presentation of the cues and targets.
Emotional Cues
Early ERP Components
P1. Rossignol et al. (2012) presented emotional and neutral face cues to HSA and LSA groups. They observed larger P1-cue amplitudes for the HSA group compared to the LSA group. Emotion did not significantly modulate the P1 amplitude, and there was no interaction with group. The general increase in perceptual processes for facial cues suggests that socially anxious participants pay particular attention to facial stimuli, and they show a general interest in stimuli carrying important information about social interaction.
P2. In their study with HSA and LSA groups, Rossignol et al. (2012) observed that angry faces evoked earlier P2-cue latencies compared to happy and disgusted faces; however, there was no effect of group. These results suggest that attention was more quickly mobilized to angry faces in both groups. Additionally, the HSA group displayed higher P2-cue amplitudes compared to the LSA group; however, there was no effect of emotion. The generalized enhancement of P2 amplitudes might mirror a global capture of attention by face cues and suggests that all face categories constitute salient stimuli in social anxiety.
Later ERP Components
P3. In their study with healthy adults, Stormark et al. (1995) observed enhanced P3-cue amplitudes for emotionally negative compared to neutral words. This result reflects that the emotionally negative words elicited more sustained, focused attention compared to the neutral words.
Regarding emotional spatial cueing cue-locked results, more studies are needed to corroborate the following findings. There is some evidence that the P1-cue is enhanced to faces in HSA groups compared to the LSA groups, indicating that socially anxious participants pay particular attention to facial stimuli in general. HSA and LSA groups display earlier P2-cue latencies to angry faces, suggesting that attention was more quickly mobilized to angry faces in both groups. Healthy populations displayed P3 amplitude enhancements for negative versus neutral words, reflecting more sustained, focused attention for the negative words.
Targets Following Emotional Cues
Early ERP Components
P1. In HTA and LTA groups, Li et al. (2005) observed that the P1 was modulated by threatening information contained in pictorial (IAPS) cues, occurring as early as 90 ms poststimulus in the contralateral hemisphere. In the HTA group, the occipitoparietal P1 amplitude was enhanced for valid threatening cues relative to valid non-threatening cues, reflecting vigilance to the location of threat. In the LTA group, however, the P1 amplitude tended to be enhanced on threatening invalid trials, reflecting avoidance from the location of threat. These results suggest that an individual’s trait anxiety level determines the mechanism by which attentional bias to peripheral threatening stimuli modulates visual inputs in early processing stages. In contrast, in their study with HSA and LSA groups, Rossignol et al. (2012) observed earlier P1 latencies for valid compared to invalid targets, indicating a cueing facilitation effect for the valid targets; however, there were no effects of group or emotion. In healthy adults, Stormark et al. (1995) observed reduced P1 amplitudes for targets validly cued by emotionally negative versus neutral words. This result may indicate that fewer attentional resources were involved in early sensory processing of the stimuli, because the emotion words already had elicited focused attention on these trials. The reduced amplitudes could therefore reflect a “cognitive benefit” of attaching and sustaining attention to an emotionally significant event.
N1. In their study with HTA and LTA groups, Li et al. (2005) did not observe any significant effects of cue validity or picture type on the N1 amplitude.
Later ERP Components
P3. In their study with HSA and LSA groups, Rossignol et al. (2012) observed no significant effects of validity, emotion, or group on P3 latencies and amplitudes, thus indicating that social anxiety did not lead to greater resource allocation toward the threatening faces. Stormark et al. (1995), however, observed enhanced P3 amplitudes for targets invalidly cued by emotion words in healthy subjects. This increased allocation of attentional resources to targets invalidly cued by the emotion words may reflect difficulties in disengaging attention away from the spatial location of the emotion words.
Regarding emotional spatial cueing target-locked results, more studies are needed to corroborate the following trends. There is some evidence that HTA subjects show enhanced P1-target amplitudes for valid threatening pictorial cues, reflecting vigilance to the location of threat, while LTA subjects display enhanced P1-target amplitudes to threatening invalid trials, reflecting avoidance from the location of threat. HSA and LSA groups display earlier latencies for valid targets, indicating a cueing facilitation effect for these targets. Healthy populations display reduced P1 amplitudes for targets cued by emotionally negative words, indicating that fewer attentional resources were involved in the early sensory processing of the stimuli. In healthy populations, enhanced amplitudes were observed for P3-targets invalidly cued by emotional words, reflecting difficulty in disengaging attention away from the location of emotional words.
4.4. Visual Search Paradigms
In visual search tasks (e.g., Ohman et al., 2001), participants must locate and respond to a target stimulus within an array of distracting, non-target stimuli. The target could be threat-related (e.g., a picture of a snake) surrounded by non-threatening distractors (e.g., pictures of flowers), or the target could be neutral (e.g., a picture of a mushroom) surrounded by threatening distractors (e.g., pictures of spiders). Attentional bias is demonstrated when participants are faster to respond to threat-related target stimuli amongst neutral distractors or are slower at detecting a neutral target stimulus amongst threat-related distractors (Cisler & Koster, 2010; Van Bockstaele et al., 2014). In this paradigm, ERPs are time-locked to presentation of the arrays.
Early ERP Components
C1. Weymar et al. (2014) presented arrays containing six objects (spider, butterfly, and flower images) arranged in a circle around a central fixation cross to spider-fearful and non-anxious control groups. Seven different kinds of arrays were presented: three arrays with spider, butterfly or flower distractors only and four arrays with a spider or butterfly target among flower objects and a flower target among spider or butterfly objects. Trials were initiated by the appearance of a fixation cross preceding each stimulus array to ensure that the participants kept their gaze focused on the central fixation location throughout the experiment, and participants were instructed to quickly and accurately detect possible targets from a discrepant category in the arrays using a button press. Enhanced C1 amplitudes were observed in response to all discrepant targets and distractors in spider-fearful compared with non-anxious participants, irrespective of fearful and non-fearful target contents. These results demonstrate that high-fearful individuals exhibit sensory vigilance and heightened sensitivity to visual stimuli in the environment, irrespective of their specific content.
P1. Weymar et al. (2014) observed no difference in P1 amplitudes between spider-fearful and non-fearful participants, indicating that attentional allocation to all content (arrays of spiders, butterflies, and flowers) did not differ between groups.
N1. Using similar methodology and stimuli as Weymar et al. (2014), Weymar et al. (2013) presented circular arrays of spiders, butterflies, and flowers to spider fearful and control participants. The authors observed that the spider fearful participants displayed enhanced N1 amplitudes for all nontarget arrays (spider, butterfly, and flower arrays without targets) compared to controls, suggesting that phobic individuals exhibit a general hypervigilance to all stimuli.
N2pc. Wieser et al. (2018) presented arrays containing six faces (angry, happy, and neutral) arranged in a circle around a fixation cross to HSA and LSA groups. Seven different kinds of arrays were presented: angry targets amongst neutral distractors, happy targets amongst neutral distractors, neutral targets amongst angry distractors, neutral targets amongst happy distractors, and three displays with no targets. Participants were instructed to quickly and accurately detect the discrepant face in the presented search arrays using a button press. The authors observed that the HSA group displayed enhanced N2pc amplitudes in response to emotional targets (angry and happy faces amongst neutral distractors), indicating that social anxiety is associated with an early attentional bias (i.e., enhanced attentional allocation and engagement) for emotional faces per se, but not for threat in particular. Similarly, Weymar et al. (2013) observed that, compared to nonfearful participants, spider fearful individuals showed a more enhanced posterior N2pc to spider versus butterfly targets in an array of flowers. These results provide electrocortical evidence for enhanced early sensory amplification of attention and target selection for fear-relevant stimuli in fearful, but not in nonfearful, participants.
Regarding visual search results, more studies are needed to corroborate the following trends and to more generally support the validity of this task as a neural measure of attention bias towards threat. There is some evidence that spider-fearful participants display enhanced C1 amplitudes in response to all discrepant targets and distractors compared with non-anxious participants, irrespective of fearful and non-fearful target contents, suggesting that high-fearful individuals exhibit sensory vigilance and heightened sensitivity to visual stimuli in the environment. Spider fearful participants also show N1 amplitude enhancements for all nontarget spider, butterfly, and flower arrays compared to controls, indicating a general hypervigilance to these stimuli. HSA participants display enhanced N2pc amplitudes to emotional face targets compared to LSA groups, reflecting enhanced allocation to and engagement with emotional faces. There is also some evidence that, compared to nonfearful groups, spider fearful participants show enhanced N2pc amplitudes to spider versus butterfly targets in an array of flowers, reflecting enhanced early sensory amplification of attention and target selection for fear-relevant stimuli in fearful participants.
5. Discussion
5.1. The Neural Chronometry of Threat-Related Attentional Bias
Although attentional biases for threatening and emotional stimuli are important for survival, hypervigilance, threat avoidance, and attentional disengagement delays may facilitate and maintain anxiety by potentially (1) increasing the likelihood of detecting and appraising benign emotional stimuli as highly threatening in the environment, and (2) increasing resource allocation (e.g., elaborative processing) to potentially threatening stimuli through decreased inhibitory control, thus increasing fear expression (e.g., arousal) and impeding habituation. Moreover, there is developmental evidence that BI in young children is implicated in the etiology of anxiety disorders. Children with a history of BI demonstrate biases similar to those observed in anxious children and adults (Pérez-Edgar et al., 2010), and attentional bias is a potentially important mechanism that sustains early underlying temperamental traits and may increase the risk for the later emergence of clinical anxiety.
Currently, there is a lack of agreement on how attentional bias presents across different stages of information processing, due in part to reliance on behavioral measures utilizing RT and fMRI measures with low temporal resolution. The reviewed studies highlight the usefulness of ERP methodology as a sensitive measure for the study of attentional bias and its chronometry. In anxious and healthy populations, specific ERP components are modulated by threatening and emotional information, making them useful markers of attentional bias at automatic and strategic stages of processing. Likely attributable to the diversity in tasks, sample sizes, stimuli, and timings used, results are, at times, conflicting. In dot-probe paradigms, (1) socially anxious subjects display potentiated P1, P2, and N2pc-cue amplitudes, and, in some cases, earlier N2pc-cue latencies to threatening and emotional faces, (2) HTA subjects display enhanced P2 and N2pc-cue amplitudes to emotional, and at times, threatening face displays, and (3) healthy subjects display earlier N2pc-cue latencies to threatening and emotional faces and enhanced P1 amplitudes for probes replacing threatening and fearful faces and negative compared to positive social scenes.
In emotional Stroop paradigms, (1) social phobics elicit larger P1 amplitudes for incoming stimuli (e.g., faces) and enhanced N170 amplitudes for emotional and angry faces and threatening stimuli, (2) spider phobics show enhanced N170 amplitudes for emotional and angry faces and threatening stimuli, and (3) healthy controls generally display enhanced N170 amplitudes for emotional and angry faces and threatening stimuli.
In emotional spatial cueing paradigms, some evidence indicates that (1) the P1 is enhanced to face cues in HSA compared to LSA groups, and both HSA and LSA groups display earlier P2-cue latencies to angry faces and earlier P1-probe latencies for valid targets, (2) HTA subjects show enhanced P1-probe amplitudes for valid threatening pictorial cues, and LTA subjects show enhanced P1-probe amplitudes to threatening invalid trials, and (3) healthy populations display P3-cue amplitude enhancements for negative versus neutral words, reduced P1 amplitudes for targets cued by emotionally negative words, and enhanced P3-probe amplitudes for targets invalidly cued by emotional words.
In visual search paradigms, there is some evidence that (1) HSA participants display enhanced N2pc amplitudes to emotional face targets compared to LSA participants, and (2) compared to control, nonfearful groups, spider-fearful participants exhibit enhanced C1 amplitudes to discrepant targets and distractors, enhanced N1 amplitudes to nontarget spider, butterfly, and flower arrays, and enhanced N2pc amplitudes to spider versus butterfly targets.
Overall, however, the presented electrophysiological evidence indicates that healthy and anxious populations (particularly individuals with social anxiety) display modulations of early ERP components, such as the P1, N170, P2, and N2pc, in response to threatening and other emotional stimuli. These findings suggest that both typical and abnormal patterns of attentional bias are characterized by enhanced allocation of attention to threat and emotion at earlier stages of processing. Importantly, however, anxious populations begin to diverge from their healthy counterparts at very early stages of processing, more reliably demonstrating hypervigilance to threat and emotion in the form of enhanced P1 amplitudes to stimulus cues. Interestingly, though, healthy populations more reliably demonstrate P1-probe amplitude enhancements compared to anxious populations, indexing enhanced attention for threatening and emotional stimulus locations. While results regarding later ERP modulations are equivocal in anxious populations, healthy control subjects more clearly demonstrate modulations of later components, such as the P3, indexing conscious, evaluative processing of threat and emotion as well as difficulty disengaging attention away from threat at later stages of processing.
These results conflict with the behavioral meta-analysis conducted by Bar-Haim et al. (2007), which found that anxious, but not non-anxious, populations display a significant attentional bias to threat in emotional Stroop, dot-probe, and emotional spatial cueing paradigms. It is likely that behavioral measures lack the sensitivity to detect differences in the temporal components of attentional bias in healthy populations. However, the results presented in this review concur with Torrence & Troup’s (2018) review on ERPs of attentional bias toward faces in the dot-probe task. The authors’ results strongly indicated an attentional bias toward threatening stimuli in general and high-anxious samples; however, they maintain that, although ERPs may be more reliable and consistent than RT-based measures, there are still inconsistencies in the literature.
5.2. An ERP Model of the Neural Chronometry of Attentional Bias
The present interpretive synthesis encompasses studies using a variety of paradigms and stimuli, and findings are integrated in order to develop an ERP model of the neural chronometry of attentional bias observed in healthy and anxious populations across stimuli and paradigms, shown in Figure 1. In Figure 1, amplitude and latency modulations have been estimated to enhance visualization of the differences observed between populations and conditions. Additionally, subcomponents are not explicitly shown, but are included with their primary, overarching component (e.g., “N1” in the figure includes N170 modulations, and “N2” in the figure includes N2pc modulations). Figure 1 illustrates that modulations of earlier ERP components, such as the P1, are associated with hypervigilance to threat at early, automatic stages of processing, which agrees with the theory proposed by Williams et al. (1988). However, modulations of later components do not appear to be associated with avoidance of threat at later, strategic stages of processing, described by Foa & Kozak (1986). Additionally, results indicate that P3 modulations are more associated with conscious engagement with threat at later stages of processing, which may also be interpreted as a form of difficulty disengaging attention away from threat. This finding is more reflective of the theory proposed by Fox et al. (2001), but does not appear to support the vigilance-avoidance model (Mogg et al., 1997, 1987). However, the finding that later ERP components may not be associated with avoidance could be due to a number of reasons. Avoidance likely does occur at later, strategic stages of processing, but the P3 and LPP components, which occur approximately 300–600 ms poststimulus, may still be occurring too early to capture and reflect strategic avoidance processes and too late to capture automatic forms of avoidance. Although the ERP technique appears to be a robust method to detect hypervigilance and engagement processes, further research is needed to clarify the time course and mechanisms associated with automatic and strategic forms of avoidance. Avoidance may be better observed in combination with eye-tracking techniques, which sample gaze direction at rates between 60 and 2000 Hz and provide a continuous measure of attentional selection performed via eye movements (overt attention) (Armstrong & Olatunji, 2012). In their meta-analysis of eye-tracking research (33 experiments; N=1579), Armstrong & Olatunji (2012) found that anxious participants show a marginal tendency to avoid maintaining gaze on threat compared to non-anxious individuals, and this effect was particularly pronounced in spider phobia.
Interestingly, some analyses on the P1 elicited to probes suggest that avoidance may be occurring at early, automatic stages of processing. As mentioned previously, Mueller et al. (2009) used a dot-probe paradigm with angry-neutral and happy-neutral face pairs to investigate attentional biases in SAD. Contrary to the majority of findings presented in this review demonstrating that the P1 is enhanced for probes following threatening and emotional stimuli in healthy and anxious populations, Mueller et al. (2009) observed that, compared to controls, SAD participants displayed decreased P1 amplitudes to probes replacing emotional (angry and happy) versus neutral faces. These results indicate reduced visual processing of emotionally salient locations at later stages of information processing—potentially a manifestation of attentional avoidance that is primed by hypervigilance to the emotional cue. Therefore, in this case, it remains to be tested whether attentional avoidance in SAD might occur automatically or may be controlled by strategic influences. Similarly, Li et al. (2005) conducted a modified cue-target paradigm with HTA and LTA participants, and observed that, in the LTA group, the P1 amplitude tended to be enhanced on threatening invalid trials, reflecting avoidance from the location of threat. It is therefore possible that attentional avoidance may occur at later, strategic stages of information processing accessible to consciousness, as described by Foa & Kozak (1986), and at early, automatic stages of information processing not accessible to consciousness. Future research using ERP and eye-tracking methodologies in conjunction with behavioral tasks focusing on attention and emotional processing may further illuminate the time course of threat avoidance and its associated mechanisms.
5.3. A Conceptual Model for Threat Appraisal and Resource Allocation
Findings from the interpretive synthesis, existing bias models, and extant neural literature on attentional systems and attentional bias (Bishop, 2008; Cisler & Koster, 2010; Corbetta, Patel, & Shulman, 2008; Mogg & Bradley, 2016) are integrated to inform a conceptual model of early and late-stage processes and substrates underlying threat appraisal and resource allocation in healthy and anxious populations (Figure 2). Our findings from the ERP literature are consistent with the larger fear and anxiety literature indicating that attention-related processes associated with threat appraisal occur at both early and later stages of information processing. A prevailing neurobiological model of attention systems in the human brain posits that there are two cortico-cortical neural systems responsible for directing attention: a dorsal and a ventral frontoparietal attention network (Corbetta et al., 2008; Corbetta & Shulman, 2002). The dorsal frontoparietal network, including the intraparietal sulcus (IPS), superior parietal lobule, and frontal eye fields (FEF), mediates goal-directed, top-down attention. The dorsal system is further responsible for pre-conditioning endogenous signals based on current goals, expectations, and pre-existing information about likely contingencies and sends top-down signals that bias the processing of appropriate stimulus features in sensory and association cortices (Corbetta et al., 2008). The ventral frontoparietal attention network includes the temporoparietal junction (TPJ), the ventral part of the supramarginal gyrus (SMG), frontopolar cortex (FPC), inferior frontal gyrus (IFG), and anterior insula (AIC), and responds more to behaviorally relevant and salient information (e.g., target stimuli in an oddball paradigm), thus facilitating a ‘circuit-breaking’ function to interrupt ongoing attentional processing and flexibly switch to another input (i.e., reorienting (Corbetta et al., 2008)). Thus, here we describe that the ADM, reflecting early stages of threat appraisal and emotion processing, involves the dorsal attention system for rapid resource allocation and salience network activity, including the amygdala and parts of the brainstem (Uddin, 2015). The amygdala and associated limbic and brainstem structures have been shown to be critically involved in the early processing of threat-related information and expression of fear-related behavior (LeDoux, 1996).
Although the spatial resolution of ERP measures is limited, evidence from ERP studies utilizing source localization techniques, such as low resolution brain electromagnetic tomography (LORETA) (Pascual-Marqui, Esslen, Kochi, & Lehmann, 2002), suggests that primary sensory and association cortices are the likely substrates from which early ERP components (< 250 ms from stimulus onset) are originating (Clark & Hillyard, 1996; Hu, Tian, Yang, Pan, & Liu, 2006; Mueller et al., 2009; Pourtois, Delplanque, Michel, & Vuilleumier, 2008). This may reflect pre-conditioned patterns of secondary sensory processing and integration of salience network activity. If the sensory stimulus is determined to be of low threat, further resources are conserved, and automatic inhibition or filtering of continued processing allows one to disengage from the object rapidly and pursue current goals. Enhanced salience network activity and associated sensory integration with sympathetic nervous system feedback (e.g., arousal) have been described to facilitate attention for threat and contribute to hypervigilance (Bögels & Mansell, 2004; Cisler & Koster, 2010; LeDoux, 1996).
Once the sensory object is determined to be of high potential threat, the ventral frontoparietal attention network has been implicated in managing attentional resources, including inhibitory control of distracting input (Fan, McCandliss, Fossella, Flombaum, & Posner, 2005; Hellyer et al., 2014; Zanto & Gazzaley, 2013; Zhang, Geng, & Lee, 2017), and is therefore likely involved in facilitating the theoretical RAM (Williams et al., 1988) to appropriately engage with and disengage from potentially threatening sensory objects. The later stage of information processing involves activity in the default mode network (DMN), including the ventromedial prefrontal cortex (vmPFC) and posterior cingulate cortex (PCC). This network has been implicated in ruminative self-evaluative processing (Andrews-Hanna, Smallwood, & Spreng, 2014) and is proposed to reduce inhibitory control and decrease the likelihood of attentional disengagement. Thus, the ventral frontoparietal attention network serves a regulatory purpose in later stages of threat appraisal and can down-regulate emotion-relevant limbic activity that may have become active earlier (Cisler & Koster, 2010; Dillon & Pizzagalli, 2007) or down-regulate DMN activity that produces interference with ongoing task demands. Imaging research, combined EEG-fMRI studies, and source localization studies suggest that the frontoparietal, salience, and default mode networks are all activated and share variance during evaluative and inhibitory processes supported by later stage ERPs (> 250 ms post-stimulus onset) (Corbetta et al., 2008; Hellyer et al., 2014; Linden, 2005; Liu, Huang, McGinnis-Deweese, Keil, & Ding, 2012; Ptak, 2012; Zhang et al., 2017). Attentional resource allocation is believed to be modulated by trait characteristics, such as anxiety or mindfulness. For example, it has been observed that LTA individuals strategically decrease elaborative processing, while HTA individuals more likely enhance elaborative processing, thus facilitating disengagement delays. Strategic forms of elaboration with increases in ruminative cognitions and DMN activity function as a form of avoidance – a short-term emotion regulatory strategy (Sylvester et al., 2012), while mindfulness is described as a self-regulatory strategy to facilitate rapid engagement and disengagement with objects of attention without further elaboration (Vago & Silbersweig, 2012). The model proposes that rapid disengagement may be adaptive and facilitated by mindfulness-oriented approach behavior and inhibitory control processes, while elaboration can be maladaptive through disinhibition and activation of ruminative processing, as supported by the DMN. Both perseverative engagement and elaboration (i.e., disengagement delays) and disengagement through displacement of attention towards self-reflective processing (e.g., rumination) relate to inhibitory control deficits and enhanced allocation of resources to threat. Increased elaborative processing is thought to manifest in increased latencies and amplitudes of later ERP components (e.g., the P3 component), yet the contribution of brain networks to the P3 appears variable and remains to be clearly specified (see Linden, 2005).
5.4. Implications for Clinical Intervention
Given the role of threat-related attentional bias in the etiology and maintenance of anxiety disorders, this could provide an effective intervention target, and ERPs may be particularly useful measures for evaluating outcomes. One therapeutic intervention aiming to target and modify attentional bias is Attention Bias Modification Treatment (ABMT), which utilizes computer-based attention training protocols to implicitly modify biased attentional patterns in anxious individuals (Linetzky, Pergamin-Hight, Pine, & Bar-Haim, 2015). ABMT commonly uses the dot-probe task to retrain attention (Bar-Haim, 2010). Other attention bias modification (ABM) methods utilize modified versions of the emotional spatial-cueing task to train threat-avoidance, and visual search tasks, which encourage active attention search for positive cues—a form of positive-search training (Mogg & Bradley, 2016).
Meta-analytic findings regarding the effects of ABM on attentional bias and anxiety have been mixed, likely because cognitive bias modification research is hampered by small and low-quality trials, extreme outliers, and risk of publication bias (Mogg & Bradley, 2016). As mentioned above, attentional bias to threat is composed of facilitated attention to threat, difficulty disengaging attention away from threat, and attentional avoidance of threat (Cisler & Koster, 2010). It is possible that, due to its relatively narrow range of action on attention, ABMT cannot target the various components of attentional bias, leading to poor outcomes for some anxiety patients. Additionally, ABMT protocols which train attentional bias away from threatening stimuli and toward neutral stimuli may further reinforce attentional avoidance of threat, which can be an important factor in the maintenance of anxiety under certain conditions (Koster, Baert, Bockstaele, & De Raedt, 2010).
Mindfulness training is another promising approach to decrease attentional bias in anxious populations (Khatibi, Dehghani, Sharpe, Asmundson, & Pouretemad, 2009; Vago & Nakamura, 2011), and has been shown to improve self-regulatory capacities (Vago & Silbersweig, 2012) and reduce anxiety symptoms (Hoge et al., 2015, 2013). Mindfulness-based cognitive therapy (MBCT), based on components of cognitive therapy (Beck, Rush, Shaw, & Emery, 1979) and mindfulness-based stress reduction (MBSR) (Kabat-Zinn, 1990), teaches patients to relate to thoughts and feelings as passing events in the mind rather than identifying with them or treating them as accurate readouts on reality. MBCT provides individuals with the skills needed to prevent escalation of negative thinking patterns at times of potential relapse or recurrence (Teasdale et al., 2000; Teasdale, Segal, & Williams, 1995). A core feature of MBCT and mindfulness training in general involves facilitation of meta-awareness and bottom-up sensory processing, characterized by continual monitoring of sensory experience as it arises. These characteristics are in contrast to a mode dominated by habitual, overlearned patterns of perceptual, cognitive, and affective processing, top-down expectations, and perseverative forms of evaluation or rumination (Hölzel et al., 2011; Kuyken et al., 2010; Vago & Silbersweig, 2012). Increased states of mindfulness allow for early detection of relapse-related patterns of negative thinking, feelings, and body sensations, thus allowing them to arise and pass with acceptance and equanimity (Teasdale et al., 2000). Furthermore, entering a mindful mode of processing at such times allows disengagement from the relatively automatic ruminative thought patterns that would otherwise trigger the relapse process (Teasdale et al., 2000). Thus, MBCT may be an acceptable and potentially effective treatment for reducing anxiety symptoms, increasing awareness of everyday experiences, and fostering approach-related behaviors to challenging emotional stimuli (Evans et al., 2008; Kuyken et al., 2010). While ABMT, due to its narrow range of action, may not be able to target the various components of attentional bias, MBCT may exert its effects through a variety of components aside from attentional regulation, such as body awareness, emotional regulation, and change in perspective on the self (Hölzel et al., 2011). Due to its broader range of action, MBCT may more effectively target both hypervigilant and avoidant modes of attentional bias, leading to more promising outcomes for anxiety patients.
ABMT and MBCT intervention research have often relied on the use of behavioral and self-report measures to track attentional bias and symptomatic improvements. More recently, ABMT studies have utilized ERPs to investigate the intervention’s neurocognitive effects. While Osinsky, Wilisz, Kim, Karl, & Hewig (2014) observed that a single session of ABM did not affect early attentional orienting to threatening facial expressions in healthy students, as indexed by the N2pc, other studies have found more promising results. O’Toole & Dennis (2012) observed that, following ABM training, non-anxious participants trained away from threat exhibited decreased P1 amplitudes to face pairs, suggesting that training attention away from threat (and towards non-threat) reduces early, automatic attentional capture of face cues. Similarly, in anxious nonpatients, Sass, Evans, Xiong, Mirghassemi, & Tran (2017) observed that training attention to pleasant stimuli in a dot-probe task led to larger P1 amplitudes for probes replacing neutral stimuli in threat-neutral word pairs, indicating increased processing of neutral information within the context of threat. Perhaps unsurprisingly, Suway et al. (2013) observed that non-anxious participants trained to attend toward threat in a dot-probe task used greater attentional resources compared to individuals who did not receive such training, indexed by increased face-evoked P2 amplitudes.
Studies with chronic pain patients and adults with alcohol dependence have demonstrated that mindfulness training can mitigate and modulate attentional bias (Garland, Boettiger, Gaylord, Chanon, & Howard, 2012; Garland, Gaylord, Boettiger, & Howard, 2010; Garland & Howard, 2013; Vago & Nakamura, 2011), albeit as assessed with behavioral measures such as RT and self-report measures. These measures may not be sensitive enough to reveal the mechanisms by which mindfulness acts on attentional biases in anxiety patients. Future studies using mindfulness interventions (e.g., MBCT) should also include neurophysiological techniques such as ERPs to investigate which components of attentional bias mindfulness acts upon and to analyze mindfulness-induced changes in neural markers of attentional bias.
6. Conclusion
Threat-related attentional biases, composed of hypervigilance, difficulty disengaging attention away from threat, and avoidance of threat, have clear evolutionary advantages; however, these biases are also responsible for contributing to the etiology and maintenance of anxiety disorders. Existing models have attempted to characterize how threat is detected, how it is modulated, and the context by which resources are allocated to process potentially threatening stimuli further. Behavioral studies of attentional bias aiming to test these models and better understand how attention is deployed to emotional stimuli have been inconclusive, likely due to their reliance on measures such as RT.
In this review, we conduct an interpretive synthesis of 28 attentional bias studies focusing on event-related potentials (ERPs) as a primary outcome to inform an ERP model of the neural chronometry of attentional bias in healthy and anxious populations. The model posits that, overall, healthy and anxious populations (particularly individuals with social phobia) display modulations of early ERP components, including the P1, N170, P2, and N2pc, in response to threatening and emotional stimuli, suggesting that both typical and abnormal patterns of attentional bias are characterized by enhanced allocation of attention to threat and emotion at earlier stages of processing. Importantly, however, anxious populations begin to diverge from their healthy counterparts at very early stages of processing, more reliably demonstrating hypervigilance to threat and emotion in the form of enhanced P1 amplitudes to stimulus cues. Interestingly, though, healthy populations more reliably demonstrate P1-probe amplitude enhancements compared to anxious populations, indexing enhanced attention for threatening and emotional stimulus locations. While results regarding later ERP modulations are equivocal in anxious populations, healthy populations more clearly demonstrate modulations of later components, such as the P3, indexing conscious and evaluative processing of threat and emotion and disengagement difficulties at later stages of processing. Findings from the interpretive synthesis, existing bias models, and extant neural literature on attentional systems are then integrated to inform a model of early and late-stage processes and substrates underlying threat appraisal and resource allocation in healthy and anxious populations.
As threat-related attentional bias is involved in the etiology and maintenance of anxiety disorders, it serves as an effective target for intervention, and the additional inclusion of ERPs are useful for evaluating outcomes. Behavioral and ERP findings regarding the effectiveness of ABMT on reducing biases and anxiety symptoms have been mixed. Behavioral results indicate that MBCT may be a more promising intervention to target and mitigate attentional bias and anxiety symptoms, perhaps due to the intervention’s unique ability to target a variety of components aside from attentional regulation. MBCT studies utilizing ERPs in addition to behavioral measures will further clarify the mechanism by which MBCT acts upon components of attentional bias and anxiety.
The present interpretive synthesis is unique in that it integrates results across various experimental paradigms to inform the model. However, there are potential limitations associated with this approach. The dot-probe, emotional Stroop, emotional spatial cueing, and visual search tasks presented in this review assess somewhat distinct aspects of attention, and each task is well-suited to detecting different forms of attentional bias occurring at different stages of processing. In this review, we present results separately for responses to emotional cues and targets in order to separate these stages of processing. The tasks also vary widely in terms of their design (e.g., differences in stimuli, stimulus presentation times, number of stimuli presented on the screen, and ITIs, whether a blocked or randomized design was used, and how well-controlled stimulus valence/arousal is). It has been shown that early and late ERPs are sensitive to perceptual qualities, stimulus repetition, and working memory load in tasks (Codispoti, Ferrari, & Bradley, 2007; Olofsson & Polich, 2007; Van Dillen & Derks, 2012; Wiens, Sand, & Olofsson, 2011). Such variability in task design may partly account for inconsistent findings in the literature. As an example, both Pourtois et al. (2004) and Santesso et al. (2008) used emotional-neutral face pairs in a dot-probe task with healthy adults; however, Pourtois et al. (2004) used fearful faces, while Santesso et al. (2008) used angry faces. Pourtois et al. (2004) observed an enhanced negative C1 component for fearful compared to happy faces in their dot-probe task, but Santesso et al. (2008) found that the C1 elicited by neutral, happy, and angry face pair stimuli was not modulated by emotional valence. It is possible that the use of angry versus fearful faces in these experiments led to the observed differences.
With the aforementioned limitations, it could be argued that one cannot compare or integrate ERP results across differing tasks and parameters. Although variability in effects across different tasks is a valid concern acknowledged in the field of neuroscience, it is of paramount importance to synthesize the literature in order to identify patterns of attentional bias in healthy and anxious populations that emerge and are robust across studies. In the present review, we also did not specifically investigate neighboring constructs such as fearfulness and neuroticism; however, these are important to examine further in future studies. Additionally, as anxiety and depression are highly comorbid (Zhou et al., 2017), it is possible that the anxious subjects discussed in this review also had varying levels of depression. Depression has been associated with attentional bias toward negative information (Peckham, McHugh, & Otto, 2010) and away from positive information (Pizzagalli, 2014); however, others have failed to observe such patterns (Elgersma et al., 2018). Attentional bias is also thought to emerge later in depression than in anxiety, and depression-related biases reflect more difficulty disengaging attention away from negative emotional, especially sad, stimuli (Joormann & Gotlib, 2007). Thus, the presence of comorbid depression may have contributed to the ERP effects reported in this review. Finally, while our primary focus in this review was to examine differences in the neural chronometry of attentional bias in anxious and healthy populations, some of the studies focusing on healthy populations (Pfabigan et al., 2014; Pintzinger et al., 2017) and anxious populations (Sass et al., 2010) reported findings reflecting gender differences. Gender significantly affects emotional processing (Cahill, 2006), and there is a higher prevalence of anxiety disorders among women (McLean, Asnaani, Litz, & Hofmann, 2011); thus, these gender effects may have influenced the ERP effects reported in this review. Future ERP research should continue to clarify the effects of gender on patterns of attentional bias in anxious and healthy populations.
Highlights:
Integrative models of attentional bias in healthy and anxious populations proposed
Enhanced allocation of attention to threat and emotion at earlier processing stages
Difficulty disengaging attention from threat and emotion at later processing stages
P1, N170, P2, N2pc modulations to threat and emotion stimuli in healthy and anxious
ABMT and MBCT interventions mitigate attentional bias to threat to varying degrees
Acknowledgements:
The authors would like to thank the anonymous reviewers for their invaluable feedback, which greatly improved this paper. The authors would also like to thank Drs. Reyna Gordon, René Marois, Bunmi Olatunji, and David Zald for their conceptual feedback on an early, oral presentation of this manuscript as part of Resh S. Gupta’s doctoral qualifying exam and Dr. Poppy Schoenberg for her conceptual contributions to the focus of this paper and development of the model.
Funding: Resh S. Gupta is supported by the National Institutes of Health [grant number: 1F31AT010299-01].
Footnotes
Publisher's Disclaimer: This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Conflict of Interest: The authors have no competing interests to declare.
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