Influence of Anxiety on Cognitive Control Processes
Summary and Keywords
Cognitive control is the ability to direct attention and cognitive resources toward achieving one’s goals. However, research indicates that anxiety biases multiple cognitive processes, including cognitive control. This occurs in part because anxiety leads to excessive processing of threatening stimuli at the expense of ongoing activities. This enhanced processing of threat interferes with several cognitive processes, which includes how individuals view and respond to their environment. Specifically, research indicates that anxious individuals devote their attention toward threat when considering both early, automatic processes and later, sustained attention. In addition, anxiety has negative effects on working memory, which involves the ability to hold and manipulate information in one’s consciousness. Anxiety has been found to decrease the resources necessary for effective working memory performance, as well as increase the likelihood of negative information entering working memory. Finally, anxiety is characterized by focusing excessive attention on mistakes, and there is also a reduction in the cognitive control resources necessary to correct behavior. Enhancing our knowledge of how anxiety affects cognitive control has broad implications for understanding the development of anxiety disorders, as well as emerging treatments for these conditions.
Experiencing elevated levels of anxiety affects how individuals see and respond to their world. As a result, a considerable amount of research has been completed to increase our understanding of how anxiety affects cognitive processes. This literature has indicated that individuals with elevated anxiety devote their attention toward threatening images and words, at the expense of ongoing activities (Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & IJzendoorn, 2007). In addition, emotional information is maintained in our thoughts, which can lead to impaired concentration (Eysenck, Derakshan, Santos, & Calvo, 2007), as well as deficits in our ability to notice and correct any resulting mistakes (Olvet & Hajcak, 2009). Therefore, elevated levels of anxiety (i.e., high trait anxiety), as well as anxiety disorders, are characterized by biases in both automatic and strategic ways in how one perceives their world (Bar-Haim et al., 2007). These biases are displayed to stimuli relevant to their fears. For example, individuals with social anxiety disorder are characterized by biases related to social rejection, and individuals with posttraumatic stress disorder (PTSD) are associated with biases related to trauma-relevant stimuli (e.g., Yiend, 2010).
In addition, anxiety is characterized by biases in later, more elaborative processes. For example, anxious individuals will interpret neutral information as threatening (e.g., Eysenck, Mogg, May, Richards, & Mathews, 1991). Furthermore, research has found that nonanxious individuals tend to interpret ambiguous information as positive, whereas anxious individuals do not display this positivity bias (e.g., Hirsch & Mathews, 2000). Anxiety also appears to lead to biases in recalling information from long-term memory (Hertel & Mathews, 2011). In these studies, individuals are asked to learn lists of stimuli with positive and negative valence, and then after a delay, are asked to recall or recognize these stimuli. Based on these studies, anxious individuals tend to display a bias for recalling information related to threat (Mitte, 2008). Therefore, this research broadly suggests that anxious individuals tend to allocate their attention initially to threatening stimuli, interpret neutral stimuli as negative, and are subsequently more likely to remember these stimuli. Furthermore, many theoretical models have suggested that these biases play a causal role in the development of clinically significant levels of psychopathology (e.g., MacLeod, Campbell, Rutherford, & Wilson, 2004). Broadly, this literature also suggests that anxiety may be characterized by deficits in the control of attention, or cognitive control, which has implications for the development and treatment of these conditions.
As a result, recent research has begun to evaluate how these biases can be directly targeted in treatment. This literature is based on a seminal paper by MacLeod and colleagues (2002), which evaluated whether attentional biases could be manipulated. Among a sample of individuals with average levels of trait anxiety, the authors used a reaction time task that reinforced individuals to direct their attention either toward threat or toward neutral information. The results indicated that where individuals direct their attention could be manipulated, such that they could be trained to either focus attention on neutral or threatening stimuli. Furthermore, those individuals who were trained to attend to negative stimuli displayed an increase in negative mood during a subsequent stress task, whereas individuals trained to attend to neutral stimuli displayed no change in mood (MacLeod et al., 2002). This suggests that attention biases to negative information may contribute to negative mood states associated with anxiety.
More recent research has used these findings to develop additional treatment approaches for anxiety. Specifically, a growing literature has evaluated whether manipulating attention away from negative and threatening stimuli would lead to a decrease in anxious symptoms. Research evaluating these attention bias modification (ABM) treatments (e.g., Amir, Beard, Burns, & Bomyea, 2009; Schmidt, Richey, Buckner, & Timpano, 2009) suggests that manipulating attention can actually reduce subsequent anxious symptoms. Although metaanalytic reviews indicate that there is a small to moderate effect with ABM treatments (Hakamata et al., 2010), as well as cognitive bias modification (CBM) treatments more broadly (Hallion & Ruscio, 2011), more work is still needed to understand the factors that affect attentional biases. For example, Hakamata and colleagues (2010) note that the effects of ABM on anxiety may be smaller than other empirically supported treatments, such as medication and cognitive behavioral therapy. Furthermore, more research is needed to understand the specific mechanisms of CBM procedures. For example, researchers have suggested that ABM may help participants learn how to avoid threatening images or words, which can maintain psychopathology over time (e.g., Cisler & Koster, 2010; Koster et al., 2010).
Thus, understanding the factors that affect these early, automatic attentional processes has important implications for understanding the development and treatment of anxiety disorders. One factor that is likely associated with cognitive biases broadly is cognitive control. Cognitive control has been defined as the ability to adapt our attention and responses in order to respond appropriately to the environment. Cognitive control is thought of as a limited resource system that directs our perceptions, mental imagery, and responses in order to perform complex tasks (e.g., Botvinick, Braver, Barch, Carter, & Cohen, 2001; MacDonald, Cohen, Stenger, & Carter, 2000). This system allows humans to engage in extended goal-directed behavior. Control systems are typically thought of as the ability to focus on specific activities via top-down or goal-directed attention, and are contrasted with automatic responses (Botvinick & Cohen, 2014). Therefore, the ability to stay focused on a particular task while inhibiting distractions is necessary for completing ongoing activities. However, responding to salient and potentially threatening information, which relies on bottom-up (automatic) or stimulus-driven processing, is also important. Therefore, cognitive control is the ability to direct attention and cognitive resources to achieve one’s goals while also navigating changes that occur in the environment.
Cognitive control is a broad construct that reflects many underlying processes. One of the first frameworks developed to understand cognitive control arose from working memory perspectives. Working memory is the ability to hold and manipulate information in one’s current consciousness (e.g., Baddeley, 2003). This can include rotating images in one’s mind or randomly generating letters or words. In their original model of working memory, Baddeley and Hitch (1974) described the central executive, which regulates where an individual is paying attention. This component, therefore, allows individuals to use top-down mechanisms to control their initial orientation to stimuli and manipulation of mental images of these stimuli, as well as to select an appropriate response. Several models of working memory have subsequently been developed, which differ to the extent that they distinguish between executive control of attention and lower-order storage systems. For example, Baddeley (2012) describes lower-order storage systems that can process verbal or visual information, as well as a central executive that controls these systems. In contrast, Cowan (2005) suggests that working memory involves information from long-term memory that is activated in one’s current attentional focus. Therefore, at the descriptive level, these two models appear quite different in their accounts of working memory. However, they also make several similar hypotheses, and the specific differences may simply be about emphasis and terminology (Baddeley, 2012).
Other perspectives of working memory have focused on delineating specific executive skills, or functions, that allow an individual to engage in more complex behavior. For example, Miyake, Friedman, Emerson, Witzki, and Howerter (2000) had 137 undergraduate students perform several tasks aimed at evaluating specific abilities of the central executive, as well as more complex tasks that rely on these skills. Results of statistical modeling suggested that there are three executive functions: inhibition, shifting, and updating. Inhibition refers to the ability to inhibit automatic or prepotent responses. Shifting refers to the ability to switch among different tasks or mental operations. Finally, the last executive function refers to the ability to update and monitor the contents of working memory. Miyake et al. (2000) further evaluated how these functions relate to each other and more complex behaviors. Their results found that correlations between these factors were in the moderate range, and that the executive functions related differentially to more complex tasks. For example, the ability to generate numbers randomly appears to rely on both inhibition and updating (Miyake et al., 2000). These results have been highly influential in understanding cognitive control, as well as how emotional factors can affect cognitive processing (e.g., Eysenck et al., 2007).
Based on these perspectives, Eysenck et al. (2007) developed a model for describing how anxiety affects basic cognitive processing. Although this framework focuses on trait anxiety, more recent studies have found evidence that these predictions also relate to diagnosable levels of anxiety (e.g., Najmi, Amir, Frosio, & Ayers, 2015), as well as more specific types of anxiety (e.g., Judah, Grant, Mills, & Lechner, 2013). As a result, recent research suggests that attentional control has a negative relationship with symptomatology among various anxiety disorders (Armstrong, Zald, & Olatunji, 2011) and may have broad implications for the development of anxiety symptoms across these disorders (e.g., Mills et al., 2016). Generally, this perspective focuses on the control of attention via effortful (i.e., top-down, goal-driven) processes or more automatic (i.e., bottom-up, stimulus-driven) processes (Corbetta & Shulman, 2002). Although these two systems are not completely independent, a healthy balance between top-down and bottom-up information processing allows an individual to engage in behaviors to move toward their goals (top-down) while managing salient and unexpected stimuli when appropriate (bottom-up).
Attentional control theory posits that anxiety disrupts the balance between these two attentional systems (Eysenck et al., 2007). Specifically, individuals high in trait anxiety devote excessive resources to the detection of potential threat, directing their attention toward possible signs of threat at the cost of goal-driven, higher-order processes. This can affect performance effectiveness, or quality of performance; and processing efficiency, or the number of resources used to maintain effectiveness. Eysenck and colleagues (2007) suggest that anxiety decreases efficiency because anxiety takes up some attentional resources that otherwise would be utilized to respond to task demands. However, anxiety only affects performance in situations where this anxiety consumes more resources than an individual can recruit for the current task. This framework also suggests that anxiety typically affects processes that draw on the inhibition and shifting executive functions, rather than updating. Research has found strong support for this theory and its predictions using a wide range of methodologies, including reaction time (Derakshan, Smyth, & Eysenck, 2009), functional magnetic resonance imaging (fMRI; Basten, Stelzel, & Fiebach, 2012), and event-related potentials (ERPs; Ansari & Derakshan, 2011).
Therefore, these models provide a framework to evaluate the effects of anxiety on cognitive control processes. First, the ability to regulate attentional processes to specific stimuli is an important part of managing ongoing task goals (Diamond, 2013). A wide variety of studies have documented how anxiety affects these attentional processes. Second, working memory, including capacity of working memory and specific executive functions, is highly important to managing and manipulating information in one’s consciousness (D’Esposito & Postle, 2015). Research has indicated that anxiety can affect the normal operation of these working memory processes, internal representations, and ongoing tasks. Third, actively monitoring and adjusting one’s behavior is a requirement for effective performance. Several models have been developed to form predictions about how performance is monitored during a specific activity. Research indicates that electrophysiological approaches (i.e., ERPs) are useful in this respect (e.g., Botvinick & Cohen, 2014). In addition, a considerable body of literature has developed suggesting that anxiety affects this performance-monitoring system. Therefore, due to the strong associations with anxiety and these three aspects of attentional control, the current article will review the selective attention, working memory, and performance monitoring literatures. We will take a broad, dimensional approach to anxiety, incorporating data from clinical and subclinical populations, as well as state and trait anxiety.
Within the cognitive psychology literature, attention is thought of as a process that regulates perception, memory, and responses of the nervous system to incoming stimuli (e.g., Luck & Vecera, 2002). There are several aspects or mechanisms involved in attention, and it likely affects task goals at all levels of information processing (Chun, Golomb, & Turk-Browne, 2011). Selective attention can be defined as what occurs when cognitive resources are utilized to engage in enhanced processing of certain incoming stimuli (Hillyard, Vogel, & Luck, 1998; Luck & Kappenman, 2012). That is, when individuals focus on a specific sensory input (e.g., objects, sounds), they are using selective attention. Research has suggested that this leads to increased activity in sensory areas of the brain (e.g., Posner & Dehaene, 1994). The result is that cognitive resources are focused on certain stimuli, while irrelevant stimuli are ignored. Therefore, selective attention is highly important to any goal-directed behavior, in that an individual needs to focus on the ongoing task while ignoring task-irrelevant stimuli.
Research in this area has found that several factors relevant to emotion and processing of emotional stimuli can affect where individuals devote their attentional resources. For example, studies have found that emotional stimuli capture our attention (e.g., Mogg & Bradley, 1999; Ӧhman et al., 2001), particularly for individuals with elevated anxiety, using a variety of methodologies, such as the attentional blink (e.g., Anderson & Phelps, 2001), visual search tasks (e.g., Eimer & Kiss, 2007), and Stroop tasks (e.g., Grant & Beck, 2006). In particular, research has found that anxious individuals are primed to attend to threatening images and words over neutral or positive words, whereas nonanxious individuals focus primarily on neutral or positive images and words (Bar-Haim et al., 2007; Hakamata et al., 2010; Yiend, 2010). Furthermore, a metaanalysis indicated that selective attention biases were a robust phenomenon associated with anxiety (Bar-Haim et al., 2007).
One common methodology used within this literature is the dot-probe. In this task, participants see two task-irrelevant stimuli (typically faces) that are displayed for varying onsets, one of which is followed by a probe stimulus (e.g., a letter or an asterisk). Participants are asked to respond as fast as possible to the probe. Participants’ response times should be faster when the probe replaces facial stimuli that they were attending to, compared to facial stimuli that they were not attending to (Mogg & Bradley, 1999). Using this methodology, researchers have been able to evaluate whether anxious individuals display hypervigilance for threat (via faster response times following emotional faces) or avoidance of these stimuli (via faster response times following neutral faces). Early research using this methodology was mixed, with some studies finding evidence of hypervigilance (e.g., Mogg & Bradley, 1999) and others finding evidence of avoidance (Mansell, Clark, Ehlers, & Chen, 1999).
As a result, Mogg, Philippot, and Bradley (2004) developed the vigilance-avoidance hypothesis, which suggests that anxious individuals initially orient their attention toward threatening stimuli and subsequently focus attention away from these stimuli. Several studies have found support for this hypothesis, particularly for early vigilance (Carlson & Reinke, 2008; Koster, Crombes, Verschuere, Van Damme, & Wiersema, 2006). However, although some studies have supported subsequent avoidance (Koster, Verschuere, Crombes, & Van Damme, 2005; Mogg et al., 2004), others indicate that anxious individuals display faster response latencies to threatening stimuli, indicating difficulty with disengaging attention (Amir, Elias, Klumpp, Przeworski, 2003; Yiend & Mathews, 2001). Recent studies also have increased our knowledge in this area by measuring eye movements to assess selective attention. For example, one study used eye-tracking to evaluate the effects of state and trait anxiety during a passive viewing task of positive, neutral, and threatening images (Quigley, Nelson, Carriere, Smilek, & Purdon, 2012). Results found that regardless of trait anxiety levels, state anxiety was associated with increased viewing time and likelihood of first fixation toward threatening images. Other eye-tracking studies have found biases in selective attention across a wide range of paradigms (e.g., Holas, Krejtz, Cypryanska, & Nezlek, 2014; Schofield, Johnson, Inhoff, & Coles, 2012). Schofield et al. (2012) evaluated the time course of eye movements during a dot-probe task for individuals diagnosed with social anxiety disorder and nonclinical controls. Their results found that over time, controls were more likely to focus their attention on positive or neutral images, whereas those with social anxiety disorder attended to all types of images. Similarly, a recent metaanalysis of eye-tracking found support for anxiety to be characterized by initial vigilance for threatening stimuli, although results were mixed for avoidance and difficulty with disengaging attention (Armstrong & Olatunji, 2012). Clearly, further research is needed in this area.
Another methodology that can advance our understanding of selective attention processes is event-related potentials (ERPs), which can break reaction time into explicit stages of information processing. ERPs represent segments of brain waves, derived from electroencephalography (EEG), that assess specific cognitive processes (Luck, 2005). ERPs use an averaging technique on the overall EEG in order to isolate the specific brain activity involved in sensory perception, cognitive processes (e.g., determining the identity of a stimulus), and signals initiating motor movement, with high temporal resolution (Luck, 2005). Therefore, ERPs are extremely advantageous to the examination of the time course of cognitive activity. They are particularly useful in assessing two specific processes related to identifying a stimulus in the environment: amount of neural resources devoted to the stimulus (i.e., amplitude); and the latency, or the time that it takes to identify, discriminate, or respond to the stimulus.
Capitalizing on these strengths, studies using ERPs have furthered our understanding of how anxiety leads to biases within specific stages of attention. Selective attention biases can occur due to early perceptual responses to stimuli, overt and/or covert attention to stimuli, and more sustained processes. By varying which component is being evaluated, research has documented specific effects of anxiety at all three stages of processing. For example, early perceptual ERP components have been used to assess attentional resource allocation prior to conscious awareness of a stimulus (Luck & Hillyard, 1994). Studies assessing these components have found that anxiety is associated with early enhanced attention to aversive faces (Rossignol, Campanella, Bissot, & Philippot, 2013; van Peer, Spinhoven, & Roelofs, 2010), suggesting that trait anxiety can bias early perception.
Research also has used ERPs to evaluate later attention allocation (Holmes, Mogg, de Fockert, Nielsen, & Bradley, 2014; Kappenman, MacNamara, & Proudfit, 2015). For example, Kappenman et al. (2015) used two ERP components to measure whether unselected participants display biases in initial selective attention, as well as sustained attention to threatening stimuli during a dot-probe task. Results found evidence of participants displaying an initial shift of attention toward threatening images based on ERP data. However, no evidence was found for sustained biases with emotional faces, suggesting that individuals with normal levels of anxiety may initially direct attention toward emotional stimuli but are able to disengage this attention quickly. In contrast, individuals with elevated levels of anxiety have displayed biases at both perceptual stages. Recent studies indicate that individuals high in trait anxiety have been found to direct visual processing resources toward emotional stimuli rather than neutral stimuli (Judah, Grant, & Carlisle, 2016; Moran & Moser, 2015). In addition, researchers have found that anxiety is associated with sustained attentional processing for threatening stimuli for individuals with both subclinical anxiety (e.g., Grant, Judah, White, & Mills, 2015) and clinical levels of anxiety (e.g., Hajcak, Dunning, & Foti, 2009).
In sum, consistent evidence suggests that anxiety is associated with biases in selective attention for emotional stimuli (see Table 1 for overview of findings). Several studies also find that individuals with normal levels of anxiety initially pay attention to signs of threat. These results have consequences for later cognitive processes, such as whether stimuli are associated with maintenance of these biases in working memory. Furthermore, selective attention is highly related to working memory (Gazzaley & Nobre, 2012), and initial attentional shifts to threat may initiate later processes associated with the development and maintenance of anxiety. Therefore, understanding how anxiety affects attention likely has implications for working memory processes as well.
Table 1. Key Findings Across Domains of Cognitive Control.
Working memory involves the ability to temporarily store and manipulate information from the environment, our memories, or both (e.g., Baddeley, 2012; Cowan, 2005). It is a limited-capacity system involving the regulation and manipulation of information necessary to complete current goals (Miyake & Shah, 1999). One model of working memory asserts that storage systems consist of domain-specific components (i.e., phonological loop for verbal information, visuospatial sketchpad for visual information), and that manipulation of information is carried out by an active central executive component (e.g., Baddeley, 2012). Working memory resources are required for a wide variety of tasks, such as reading, engaging in mental math, and more complex actions, including planning future behavior and generating solutions to problems (Diamond, 2013). Therefore, research indicates that working memory capacity is associated with a wide variety of cognitive operations, such as intelligence (Kane, Hambrick, & Conway, 2005), multitasking (Hambrick, Oswald, Darowski, Rench, & Brou, 2010), and emotion regulation (Kleider, Parrott, & King, 2009).
Because working memory is limited in its resources, considerable research has evaluated how this system affects how we view and respond to our environment. There are several methodologies that have been used to study working memory, including complex span tasks, running span tasks, and visual arrays. These tasks typically present stimuli that participants must either memorize or manipulate in some fashion. For example, in a change detection task, participants are presented with several stimuli, and following a delay, are asked to determine whether one of the previously presented stimuli has changed. This methodology has helped document the capacity of visual working memory (Luck & Vogel, 1997), as well as participants’ ability to prevent (or filter) information from entering working memory. Working memory resources also have been linked with attentional control (Jonides, 1981; Redick & Engle, 2006). Shipstead, Lindsey, Marshall, and Engle (2014) used structural equation modeling to evaluate the relationship between attentional control and working memory capacity. Results indicated that performance on span tasks and visual arrays, two indicators of working memory capacity, were highly associated with attentional control. These methodologies also have helped us understand how anxiety affects these cognitive operations.
The effects of anxiety on working memory have been studied since the highly influential work of Michael W. Eysenck (1979). Early studies evaluated the extent that the cognitive (i.e., worry) and somatic (i.e., physiological sensations) components of anxiety affect working memory (Eysenck, 1985; Rapee, 1993). This research suggested that anxiety, and particularly worry, decrease working memory resources, primarily through their effects on the central executive, and to a lesser extent the phonological loop. More recent studies have elaborated on these effects. For example, Hayes, Hirsch, and Mathews (2008) evaluated differences in working memory capacity between high and low worriers while participants engaged in a random letter generation task during both worry and positive-thinking conditions. Random letter generation was used to assess residual working memory capacity, with the idea that if the thinking tasks (i.e., worry versus positive) differentially consume attentional resources, letters will be chosen less randomly during the more difficult condition. The results found that high worriers displayed decreased performance compared to low worriers, particularly when engaging in worry. These results support the hypothesis that worry takes up working memory resources among chronic worriers. Follow-up research has suggested that these effects are due to the verbal nature of worry (Leigh & Hirsch, 2011; Stefanopoulou, Hirsch, Hayes, Adlam, & Coker, 2014).
Research also has suggested that trait anxiety is associated with deficits with filtering threatening information from working memory (Judah, Grant, & Carlisle, 2016; Qi, Ding, & Li, 2014; Stout, Shackman, & Larson, 2013). Stout et al. (2013) measured an event-related potential (ERP) indexing visual working memory storage while participants completed a change detection task that consisted of neutral and emotional faces. Results found that anxious individuals had difficulty filtering task-irrelevant emotional faces from working memory, but no difficulties filtering neutral faces. Furthermore, another study found that biases in selective attention can persist into working memory (Judah, Grant, & Carlisle, 2016). Finally, some research has indicated that anxious individuals display reduced working memory capacity (e.g., Qi, Chen, et al., 2014). Given that working memory capacity predicts performance on a wide range of cognitive and real-world tasks (Engle, 2002), these results have implications for how anxiety affects executive control.
Executive processes involve manipulating the contents of working memory in order to engage in novel tasks, plan future behavior, and correct errors (Posner & DiGirolamo, 1998). Several studies have evaluated how anxiety affects the three executive functions outlined by Miyake et al. (2000). Specifically, research suggests that anxiety is associated with decreased efficiency (i.e., anxious individuals require more attentional resources to maintain performance) in central executive functioning (Eysenck & Derakshan, 2011). This is particularly true for the inhibition and shifting functions. Ansari and Derakshan (2011) evaluated this using an antisaccade task, in which participants have to inhibit the tendency to look toward stimuli presented in their peripheral vision. Results found that individuals high in trait anxiety had longer latencies for antisaccade trials than those low in trait anxiety, suggesting deficits in the inhibition function. ERP analyses corroborated these findings, demonstrating that anxious individuals had difficulty recruiting executive resources to inhibit prepotent responses (i.e., looking toward the stimulus).
Several other studies have found support for anxiety being associated with deficits in both the inhibition and shifting functions (e.g., Derakshan, Ansari, Hansard, Shoker, & Eysenck, 2009; Derakshan, Smyth, et al., 2009), with results typically not finding effects of anxiety on updating (e.g., Calvo & Eysenck, 1996). Research also has found that these executive function deficits may be related to specific symptoms of psychopathology. For example, deficits in inhibiting intrusive memories have been linked to posttraumatic stress disorder (PTSD) symptoms (Catarino, Küpper, Werner-Seidler, Dalgleish, & Anderson, 2015). Inhibition deficits also have been found for panic disorder, obsessive-compulsive disorder, generalized anxiety disorder, and social anxiety disorder (e.g., Judah, Grant, Mills, & Lechner, 2013; Price & Mohlman, 2007; Thomas, Gonsalvez, & Johnstone, 2014). Furthermore, deficits in shifting and cognitive inflexibility have been linked with anxiety symptoms (Bannon, Gonsalvez, Croft, & Boyce, 2006; Salters-Pedneault, Suvak, & Roemer, 2008). Salters-Pedneault et al. (2008) found that inducing participants to engage in worry led to poorer performance in a task that assessed task switching ability. Therefore, examining deficits in specific executive functions has important implications for understanding the consequences of anxiety disorders.
In sum, considerable research suggests that anxiety affects working memory, both in terms of capacity and specific functions of the central executive (see Table 1). This research is particularly consistent for showing that worry reduces working memory capacity. Therefore, more research is needed to evaluate the effects of different types of anxiety on working memory capacity. In addition, because anxiety has direct effects on executive functions, it will be important to evaluate how these deficits affect performance, as well as online evaluation of one’s performance, across a wide variety of tasks.
Successful performance requires awareness of mistakes and changing behavior to adapt to environmental demands (Holroyd & Coles, 2002). A significant aspect to cognitive control involves monitoring of performance and awareness of errors (Folstein & Petten, 2008). When trying to decide how to respond to stimuli in one’s environment, mental representations of appropriate behaviors are activated in working memory in order to select one’s response. Responding to the environment appropriately requires monitoring of these mental representations, as well as adjusting one’s behavior once an error has occurred. Basic research in the cognitive neuroscience literature has implicated the anterior cingulate cortex (ACC) in the monitoring of errors (e.g., Menon, Adleman, White, Glover, & Reiss, 2001). The ACC is an area of the cortex with connections to the cognitive control areas of the brain (i.e., prefrontal cortical areas), as well as areas involved in responding to reward and emotional stimuli (e.g., amygdala, basal ganglia; Allman, Hakeem, Erwin, Nimchinsky, & Hof, 2001; Posner & DiGirolamo, 1998). This literature suggests that the ACC is an important brain area in resolving conflicting responses within working memory and adjusting behavior following mistakes (Cohen, Botvinick, & Carter, 2000; Smith & Jonides, 1999). Although behavioral measures have been useful in evaluating performance monitoring, research has suggested that these measures may be contaminated with other effects not due to error detection (Dutilh et al., 2012). However, considerable knowledge has accumulated about performance monitoring from research using event-related potentials (ERPs).
When engaging in a basic reaction time task (e.g., Stroop tasks), following an error, a specific ERP, known as error-related negativity (ERN), can be derived from electroencephalography (EEG). The ERN is a sharp, negative waveform that occurs within 100 milliseconds after the error, and it appears to be generated in the ACC (Gehring, Liu, Orr, & Carp, 2012). It is elicited following mistakes on several types of tasks across visual, auditory, and tactile stimuli. Several theories have been developed regarding the nature of the ERN, which generally suggest that this component represents conflict between mental representations of different responses to the stimulus maintained in working memory (e.g., Coles, Scheffers, & Holroyd, 2001; Holroyd & Coles, 2002). Consequently, the ERN appears to index a neural measure of the initial alarm that a mistake has been made. That is, it represents the initial signal of an error within a host of processes aimed at recruitment of cognitive control in order to correct future behavior (e.g., Coles et al., 2001; Holroyd & Coles, 2002).
Therefore, ERN studies have been useful in increasing our understanding of the factors that influence learning and correcting errors. However, the specific significance of the ERN at present is unclear. Theories of the ERN suggest that ERN amplitude should be associated with correcting mistakes (Gehring, Goss, Coles, Meyer, & Donchin, 1993) or changing response strategy (Holroyd & Coles, 2002). Although some studies suggest that the ERN is related to error correction (e.g., Burle, Roger, Allain, Vidal, & Hasbroucq, 2008), others have not found this association (e.g., Rodrίguez-Fornells, Kurzbuch, & Mũnte, 2002). In addition, some research indicates that the amplitude of the ERN is associated with slowing response time to subsequent stimuli (Gehring et al., 1993; Rodrίguez-Fornells et al., 2002), although this finding has not always been replicated (Hajcak, McDonald, & Simons, 2003). These results have focused on average ERN and response time across individuals. However, data examining whether ERN amplitude is associated with differences after an error within individuals have been similarly equivocal (Debener et al., 2005; Weinberg, Riesel, & Hajcak, 2012). Some research evaluating whether ERN amplitude is related to conscious awareness of making an error also has been equivocal (Endrass, Reuter, & Kathmann, 2007; Wessel, Danielmeier, & Ullsperger, 2011). Therefore, research aimed at identifying the specific function of the ERN within cognitive control processes has begun to examine moderators of this ERP component.
One moderator that may affect the association between the ERN and behavior following errors may be anxiety (Moran, Bernat, Aviyente, Schroder, & Moser, 2015). Several studies have found evidence that ERN amplitude is enhanced in individuals with high trait anxiety, as well as anxiety disorders. For example, Weinberg, Olvet, and Hajcak (2010) evaluated whether individuals diagnosed with generalized anxiety disorder (GAD) would display increased error monitoring as assessed with the ERN, compared to individuals without a psychological diagnosis using a basic reaction time task. Results found that individuals with GAD displayed an enhanced ERN compared to healthy participants, and ERN amplitude was correlated with self-reported anxious symptoms. This suggests that individuals with GAD may devote excessive attentional resources toward errors, which may play a role in the phenomenology of the disorder (Weinberg et al., 2010). Several other studies have found evidence that anxiety disorders and high levels of trait anxiety are associated with increased error monitoring as assessed with ERPs. Specifically, research suggests that trait levels of worry (Hajcak et al., 2003; Moran, Taylor, & Moser, 2012), social anxiety (Judah et al., 2016), and obsessive-compulsive disorder (Gehring, Himle, & Nisenson, 2000; Hajcak, Franklin, Foa, & Simons, 2008) are associated with increased error monitoring. Furthermore, data indicate an enhanced ERN is characteristic of elevated worry, in contrast to somatic indicators of anxiety (e.g., Moser et al., 2013). Therefore, frameworks for understanding how anxiety affects error monitoring are beginning to be developed.
One perspective suggests that the increased ERN amplitude is a trait (e.g., Olvet & Hajcak, 2009). Specifically, errors are seen as threatening, and therefore activate defensive motivational systems in order to deal with the threat (Hajcak, 2012). Under this perspective, individuals displaying enhanced sensitivity to threat generally would display an increased ERN amplitude. Supporting this hypothesis, research has found that the amplitude of the ERN is affected by motivations and rewards (Weinberg & Hajcak, 2011). Furthermore, research suggests that the ERN continues to be enhanced following treatment (Hajcak et al., 2008), is elevated in first-degree relatives of individuals with obsessive-compulsive disorder (OCD) compared to those without OCD (Reisel et al., 2011), and is heritable (Anokhin et al., 2008). These findings are consistent with the hypothesis that the ERN may be a cognitive marker (endophenotype) for psychological disorders characterized by worry and anxious apprehension (e.g., GAD and OCD; Hajcak, 2012).
Another perspective hypothesizes that the enhanced ERN represents a compensatory process due to the cognitive load that occurs due to worry (Moser et al., 2013). This perspective is consistent with the findings that worry consumes executive resources and relies on basic theories of the ERN. For example, conflict monitoring theory suggests that the ERN reflects detection of the correct and incorrect responses in the ACC (Yeung & Cohen, 2006), whereas the reinforcement learning theory suggests that the ERN reflects the signaling of subcortical structures to the ACC when outcomes are worse than expected (Holroyd & Coles, 2002).
These perspectives broadly posit that the ERN is important to monitoring conflict due to particular responses being activated. Therefore, Moser et al. (2013) suggest that the enhanced ERN associated with anxiety occurs as a compensatory effort to maintain performance because worry consumes cognitive resources from top-down control mechanisms. Supporting this perspective, research has found that placing a cognitive load on participants increased ERN amplitude (Moran & Moser, 2012). Furthermore, a recent study suggested that worry is associated with reduced communication of brain regions involved in cognitive control, suggesting that worry increases initial error signaling but decreases subsequent processes to correct these errors (Moran et al., 2015).
Jointly, these perspectives help to delineate the factors that are associated with an increased ERN and performance monitoring. They also provide hypotheses about factors associated with anxiety that may affect cognitive functioning more broadly, including working memory loads created by worry, as well as motivational factors that affect perception and response to stimuli. However, when considering the ERN literature, it is important to note that the ERN represents one signal in an extensive network of performance monitoring across the brain. Several other ERPs have been identified that represent different aspects of performance monitoring, including components that index cognitive control, as well as components focused on processing feedback. The extent that these components are correlated with changes in behavior and modulated by emotions will help identify the consequences of anxiety on performance monitoring and adjusting responses more broadly.
Across these studies, it is clear that anxiety affects a broad array of cognitive control processes. This includes signs of threat biasing perceptual processes, consuming executive resources, and modulating performance monitoring indices. Researchers are beginning to examine how cognitive processes interact with each other, including among individuals with elevated levels of anxiety. Research has indicated that when biases in selective attention are activated, this can decrease resources toward ongoing tasks (e.g., Bishop, 2007), affecting performance. In addition, attentional processes are likely influenced by working memory capacity and/or loads on working memory (e.g., Peschard & Philippot, 2016). Therefore, understanding how anxiety affects working memory load also may increase our understanding of how anxiety affects other cognitive control processes. Recent examples in the literature suggest that working memory load (Judah, Grant, Lechner, & Mills, 2013) and trait attentional control (Taylor, Cross, & Amir, 2016) may influence attentional biases associated with social anxiety.
Therefore, selective attention biases may lead to difficulty filtering information from working memory or maintenance of threat within working memory, both of which may negatively affect monitoring of ongoing activities. Preliminary research has found support for these possibilities. Research has found that biases in selective attention can be maintained in working memory among anxious individuals (Judah et al., 2016). These data are important, as research has suggested that participants focusing their attention on images held within working memory (i.e., threatening or neutral aspects) also affects both neural and self-reported indices of emotional arousal (Thiruchselvam, Hajcak, & Gross, 2012). This emotional arousal may then bias the initial alarm signal of performance monitoring, either through increasing the salience of errors (Hajcak, 2012) or loading working memory (Moser et al., 2013).
These results also may have implications for attention bias modification (ABM) treatments. First, ABM researchers have typically used modified dot-probe tasks with relatively short presentation of the emotional stimuli (e.g., 500 milliseconds). However, there is evidence that anxiety disorders are characterized by selective attention biases at multiple stages of processing, including sustained attention. Thus, it may be useful to use training procedures that modify the attention biases across a wide range of presentation times.
Second, anxiety symptoms appear to reduce working memory capacity, as well as impairing the ability to inhibit threatening stimuli from entering working memory. Therefore, it will be important to assess whether ABM also leads to decreases in emotional material being maintained in working memory.
Third, anxious individuals devote excessive attentional resources to monitoring errors, but reduced ability to engage cognitive control in order to change behavior. Thus, it may be useful to evaluate the extent that ABM, cognitive bias modification (CBM), or both treatments affect cognitive control mechanisms (Cisler & Koster, 2010).
Finally, it is clear that a further understanding of how anxiety affects cognitive control also has consequences for CBM procedures. As noted by Hallion and Ruscio (2011), CBM treatments may be more likely to change processes that rely on effortful control rather than more automatic biases. Therefore, integration of how anxiety affects cognitive control across these (as well as other) executive processes has important implications for our understanding of the development and treatment of anxiety disorders.
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