Running head: NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 1
NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY
IN HIGH TRAIT ANXIOUS INDIVIDUALS
A THESIS
SUBMITTED TO THE GRADUATE SCHOOL
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE
MASTER OF ARTS
IN CLINICAL PSYCHOLOGY
BY
RICHARD T. WARD
DR. STEPHANIE SIMON-DACK – ADVISOR
BALL STATE UNIVERSITY
MUNCIE, INDIANA
JULY 2017
NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 2
Neural Correlates of Attentional Control Theory in High Trait Anxious Individuals
Trait anxiety, as defined by Spielberger (1966), involves the experience of excessive worry, nervousness, and apprehension for a prolonged period of time. Individuals displaying maladaptive high levels of trait anxiety have demonstrated a variety of impairments across multiple cognitive domains including working memory. Attentional Control Theory provides an explanation as to how maladaptive high trait anxiety can impact working memory through the impairment of central executive functions (Eysenck & Derakshan, 2011), and a number of behavioral investigations have provided evidence in support of this theory (Eysenck, Paynea, &
Derakshan., 2005; Derakshan & Eysenck, 2009; Ansari & Derakshan, 2010; Johnson, 2009;
Ansari, Derakshan, & Richards, 2008; Murray & Jannell, 2003). However there is currently a lack in understanding of the underlying neural processes that give rise to Attentional Control
Theory’s components. Therefore, the goal of the current study was to investigate the underlying neural correlates of the tenets of Attentional Control Theory. By understanding the neural mechanisms of maladaptive high trait anxiety, we gain a greater knowledge of the underlying processes of trait anxiety, and how it impacts the neural mechanisms involved with central executive functions.
Trait Anxiety
Spielberger (1966) defined trait anxiety as a personality trait, in which individuals high it are more prone to experience intense feelings of apprehension, tension, and excessive worry for a prolonged duration. Individuals high on trait anxiety also demonstrate worry behaviors, as well as patterns of avoidance of potential threats due to their induction of negative emotionality
(Sylvers, Lilienfield, & LaPrarie, 2011). In addition, trait anxiety has been shown to influence an individual’s level of state anxiety, or the negative emotionality an individual experiences when NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 3 placed in a situation they perceive as threatening (Spielberger, 2010). State anxiety involves a temporary experience of negative emotions, such as worry, whereas trait anxiety involves a consistent pattern of these emotions. Both constructs are viewed as a dimensional nature, with maladaptive extremes at both poles of their respective spectrums (Endler & Kocovski, 2001).
Maladaptive high levels of trait anxiety has also been shown to be one of the primary factors associated with the diagnosis of anxiety disorders, such as Generalized Anxiety Disorder
(Kennedy, Schwab, Morris, & Beldia, 2001). Maladaptive high levels of trait anxiety often produce a profound number of impairments for individuals experiencing them. For example, individuals diagnosed with anxiety disorders, which involve high maladaptive levels of trait anxiety, report financial struggles due to reduced productivity (DuPont, Rice, Miller, Shiraki,
Rowland, & Hardwood, 1998). Highly anxious individuals have also demonstrated sleep deficits
(Rosa, 1983), and impairment in decision making abilities (Miu, Heilman, & Houser, 2008).
Other areas, such as attention (Najmi, Kuckertz, & Amir, 2012), and interpersonal relationships
(Strauss, Frame, & Forehand, 1987) also appear to be affected by high levels of anxiety. Worry associated with high trait anxiety has also been shown to be related to impairment in the overall quality of life (Strine, Chapman, Kobau, & Balluz, 2005).
Attentional Control Theory
Many of the above mentioned domains, such as attention, are closely related to working memory functions that are known to be negatively affected by factors such as stress resulting from high levels of anxiety (Bogdanov & Schwabe, 2016). Baddeley and Hitch (1974) proposed a working memory model in which the central executive, often considered to be analogous to executive functioning (Jurado & Rosselli, 2007), serves as an overarching component, controlling the sub-components of the visuospatial sketchpad, phonological loop, and episodic NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 4 buffer (Baddeley, 2000). The central executive is primarily involved with the allocation of attentional resources, including the shifting of and inhibition of attention. The central executive drives the entire working memory system, deciding which information is attended to and which sections of working memory (e.g. phonological loop, episodic buffer, and visuospatial sketchpad) are allocated as resources to perform specific functions. Through these functions, the central executive has often been implied as being heavily involved with goal planning and decision making (Baddeley, 1992). A meta-analysis by Moran (2016) provided evidence that highly anxious individuals demonstrate impairments on overall working memory capacity across a variety of tasks. It is likely that the impairment of working memory, resulting from high trait anxiety, may indirectly be a factor involved with several of the effects described above
(Anderson, 1994). Specifically, this impairment of attentional resources in working memory is likely due to the influence high trait anxiety has on Baddeley and Hitch’s (1974) central executive component of working memory.
To account for the influences of high anxiety on efficiency of the central executive,
Eysenck and Calvo (1992) proposed the idea of Attentional Control Theory. Originally known as
Processing Efficiency Theory, Eysenck and Calvo (1992) argued that the high trait anxiety would cause worry, and that the experience of worry would impair the processing efficiency of the central executive on tasks that require high attentional demands. However, a review of subsequent research examining Processing Efficiency Theory suggested that this impairment in central executive efficiency was likely due to decreases in attentional control. Specifically, these adverse effects are due to the central executive’s impairment in the ability to inhibit and shift attentional resources (Eysenck, Santos, & Calvo, 2007). These findings led to the reformation of
Processing Efficiency Theory into Attentional Control Theory. NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 5
Attentional Control Theory attempts to explain the effects of high trait anxiety on attentional resource allocation, as well as overall working memory effects (Eysenck &
Derakshan, 2011). The theory holds four primary tenets. First, Attentional Control Theory hypothesizes high trait anxiety will impair the central executive resource control center. Second,
Attentional Control Theory suggests high trait anxiety will impair the inhibition function that normally acts to dismiss task-irrelevant stimuli. This implies individuals high on trait anxiety are likely to struggle disengaging attention from anxiety-inducing stimuli. Third, Attentional Control
Theory proposes that high trait anxiety will impact working memory by impairing the shifting set function. This idea refers to the notion that individuals are unable to fully shift their attentional engagement from one stimulus to another. Fourth, Attentional Control Theory proposes that high trait anxiety causes greater impairment in processing efficiency than actual performance effectiveness. This suggests that individuals with high trait anxiety will show similar performance outcomes as low trait anxious individuals, but require a greater duration of time to complete a task. It is important to note that although high and low trait anxious individuals perform similarly on cognitive tasks, this impaired processing in high trait anxious individuals can potentially lead to negative outcomes for high trait anxious individuals. Specifically, high trait anxious individuals may engage in self-comparison with their peers who perform tasks more quickly, which can potentially lead to the enhancement of their worry behavior, and thus increase their levels of trait anxiety.
A number of studies support the four tenets proposed by Attentional Control Theory. For example, in line with the first tenet, Eysenck and colleagues (2005) demonstrated high trait anxiety was related to impairment on central executive tasks but not tasks assessing other components of working memory. Results from this study help support the notion that high trait NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 6 anxiety seems to affect the central executive but has little effect on the specific subordinate components, such as the visuospatial sketchpad, the episodic buffer, or the phonological loop.
Additional evidence suggests the central executive includes inhibitory control and cognitive flexibility, functions that are sub-specific components of executive functioning. A latent-variable analysis yielded the underlying executive functions of inhibition, shifting set, and updating
(Miyake, Friedman, Emerson, Witzki, Howerter, & Wager, 2000). These sub-components of executive functioning are related to the functions of the central executive described in
Baddeley’s working memory model (Diamond, 2013). Therefore, there exists several sub- components of executive functioning that appear to be a part of an overarching system. This system is commonly referred to as executive function, which shares similarities with the description of the central executive from Baddeley’s working memory model.
Empirical research has also supported the second tenet of Attentional Control Theory, which proposes that individuals with high trait anxiety have difficulty with inhibition. In a review of recent published literature examined by Derakshan and Eysenck (2009), individuals with high trait anxiety were more likely to be distracted by anxiety provoking stimuli, or have difficulty inhibiting their attention, than individuals with low anxiety. Ansari and Derakshan
(2010) further supported this notion of high trait anxiety impairing inhibition by demonstrating how individuals with high trait anxiety displayed longer latency periods of antisaccades during antisaccade tasks. This phenomenon is normally associated with the impairment of inhibition of efficient top-down attention regulation.
A review of the literature shows support for the third tenet of Attentional Control Theory, in which highly trait anxious individuals have impaired shifting set functions. For example,
Johnson (2009) reported that individuals with high trait anxiety took longer to shift from a NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 7 neutral to emotional task than individuals with low trait anxiety on a task-shifting paradigm involving the emotional processing of facial expressions. Ansari and colleagues (2008) also found results to support the notion of high trait anxiety’s impairment in one’s ability to shift attentional resources. Using a mixed anti and prosaccade task, the researchers had participants complete standard one task and task-switching conditions, in which highly trait anxious individuals were found to have significantly less shifting functional efficiency than individuals categorized as having low anxiety. Results from studies such as these support the tenet that high trait anxiety impairs the functional ability to shift attention between tasks.
Impaired processing efficiency in highly trait anxious individuals, the fourth tenet to
Attentional Control Theory, has also been supported in research. For example, Murray and
Jannell (2003) found that individuals high in trait anxiety showed similar performance as individuals low in trait anxiety, however the high trait anxiety condition yielded significantly greater response times on a dual-task auto racing simulation. These results imply an impairment in the processing speeds of individuals with high trait anxiety, thus clearly supporting the tenet of
Attentional Control Theory that high anxiety impairs processing efficiency, but not performance effectiveness.
While there is current literature supporting Attentional Control Theory, little research has investigated the underlying neural mechanisms that give rise to these impairments. However, there has been some electrophysiological work surrounding the hypothesis dealing with inhibition. For example, other researchers have demonstrated through electrophysiological recordings of event-related potentials (ERPs) the impacts of high anxiety on processing efficiency due to impairment of inhibition (Righi, Mecacci, & Viggiano, 2009). Specifically, they found that individuals high in trait anxiety demonstrated greater N2 responses than low trait NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 8 anxious individuals on NoGo trials, but not on Go trials for the Sustained Attention Task. Since larger amplitudes of waveforms such as the N2 indicate more resources are being recruited to complete a task, these findings reflect an impairment in the processing efficiency of inhibition functioning. In addition, other work has also shown that individuals with high trait anxiety had impaired efficiency of inhibition (Savostyanov, Tsaia, Lioua, Levinb, Leea, Yurganovb, &
Knyazeyb, 2009). These results showed that individuals with high trait anxiety displayed greater
EEG desynchronization compared to low trait anxious individuals for Stop and Go conditions in a similar task. Furthermore, their findings demonstrated that highly trait anxious individuals displayed greater EEG desynchronization specifically after the warning signal, indicating that high trait anxiety causes impairment in the efficiency of inhibition functioning. This relates to prior evidence (Knyazev, Levin, & Savostyanov, 2008) suggesting that greater neuronal synchronization after the stop signal onset reflects motor response inhibition.
Despite this work, there still exists a gap in the literature involving the identification of underlying quantitative EEG correlates associated with Attentional Control Theory. Therefore, while the tenets of Attentional Control Theory have been supported and replicated through behavioral studies in the literature, there is relatively little information regarding what neural correlates and mechanisms underlie this phenomenon.
Neural Correlates of Working Memory and Anxiety
Previous research has investigated neural correlates in associated brain regions underlying trait anxiety and working memory tasks. As such, it may be possible to link the tenets of Attentional Control Theory to neural activity based on these findings using electroencephalography methodology (EEG). EEG methodology involves the recording of the specific neuronal electrical activity, or frequencies, in respective brain regions (Niedermeyer & NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 9
Da Silva, 1999). This is achieved by using non-invasive scalp electrodes over an individual’s head. Specific techniques, such as quantitative electroencephalography (QEEG), are often incorporated in order to observe the electrical activity of specific frequency bands, such as theta, alpha, and beta (Evans & Abarbanel, 1999).
A number of researchers have performed work with EEG methodology in order to understand the neural correlates and associated brain regions underlying high trait anxiety. For example, prior research has demonstrated links between high trait anxiety and activity specifically within right frontal brain regions (Thibodeau, Jorgensen, & Kim, 2006). These researchers performed a meta-analysis examining respective EEG activity in high trait anxious and depressed individuals, and found a link between resting right frontal EEG activity in high trait anxious individuals. Specifically, the meta-analysis examined the magnitude of effects across multiple EEG studies for resting frontal asymmetry and anxiety, depression, and comorbid anxiety and depression. Their results found greater resting frontal asymmetry for individuals with general anxiety and depression.
This notion of right frontal activity related to high trait anxiety was supported by research showing greater activation in the right frontal brain regions when high trait anxious individuals are exposed to fearful faces (Avram, Baltes, Miclea, & Miu, 2010). By implementing a Stroop
Task, they demonstrated that high trait anxious individuals showed a cognitive bias toward fearful faces with increased right frontal activation, while happy faces induced left frontal activation. Furthermore, functional magnetic resonance imaging (fMRI) has demonstrated that individuals high in trait anxiety also display greater brain activation within the right dorsolateral prefrontal cortex (dlPFC; Telzer, Mogg, Bradley, Mai, Ernst, Pine, & Monk, 2008). As indicated, the current literature suggests that highly trait anxious individuals display a pattern of brain NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 10 activity, specifically seen in the right frontal regions. Therefore, it is likely this asymmetrical brain activity will be observed in high trait anxious individuals for all tenets of Attentional
Control Theory.
In order to obtain a greater understanding of the underlying neural activity of Attentional
Control Theory involving anxiety and working memory, the specific EEG band frequencies must also be understood. EEG band frequencies are described as patterns of brain activity resulting from the synchronized electrical pulses from masses of neurons communicating with one another. These electrical pulses are measured from peak to peak and normally range from 0.5 to
100 µV in amplitude, yielding the delta frequency band (0.5 to 4 Hz), theta band frequency (4 to
8 Hz), alpha band frequency (8 to 13 Hz), and beta band frequency (13 Hz or more) (Teplan,
2002). Currently, alpha band frequency is the most commonly studied and understood pattern of brain activity in human participants. Hammond (2011) described alpha band activity, with a frequency range between 8 and 12 Hz, as being associated with a state of relaxation, or meditation. Specifically, mental relaxation is associated with neural inhibition indexed by high alpha activity. Alpha activity has been shown to decrease as individuals engage in attentional or cognitive activities, suggesting that alpha activity has an inverse relationship with alertness. This would imply that higher levels of alpha band activity are related to a more relaxed mental state, and that lower levels of alpha band activity are associated with an alert mental state.
Researchers have performed a closer analysis of the specific frequency band activity in responsible brain regions in highly trait anxious individuals. In one example, researchers demonstrated higher alpha band activity in individuals who scored high in trait anxiety
(Knyazev, Savostyanov, & Levin, 2004). These individuals showed positive correlations with alpha activity, while exhibiting reductions in delta activity. Such findings were consistent with NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 11 prior studies (Knyazev, Slobodskaya, & Wilson, 2002; Knyazev et al., 2003; Knyazev &
Slobodskaya, 2003) that also showed this negative correlation between alpha-delta activity that is associated with behavioral inhibition and individuals high in trait anxiety. This high alpha-low delta activity was explained by Robinson (1999, 2000, 2001) as reflecting a negative relationship between the thalamus and reticular activation system associated with arousal and inhibition.
Researchers have also investigated the underlying neural correlates of working memory, in order to better understand the physiological processes involved during performance on specific tasks. Working memory tasks involve the use of cortical resources, implicated in attentional processing. This processing is also associated with functions involving task switching and inhibition. Klimesch (1997) first investigated the neural correlates of retrieval and encoding, finding that individuals with better memory elicited greater alpha activity, which suggests a role in attentional processing. Additional research further investigated the role alpha activity plays in working memory, specifically examining how cognitive load affects alpha activity (Gevins,
Smith, McEvoy, & Yu, 1997). The results showed that alpha activity decreased in the central and parietal brain regions, suggesting that alpha activity is inversely related to cortical resource availability. This supports the notion that the parietal brain regions are likely involved in tasks demanding attentional processing. Further research supports this notion that increased cognitive load on working memory tasks results in desynchronization of alpha activity (Stipacek, Grabner,
Neuper, Fink, & Neubauer, 2003; Fink, Grabner, Neuper, & Neubauer, 2005). Other work has investigated frequency activity involved with task switching, which found alpha activation in the posterior brain region. (Sauseng, Klimesch, Freunberger, Pecherstorfer, Hanslmayr, &
Doppelmayr, 2006). Klimesch, Sauseng, & Hanslmayr (2007) showed that event-related synchronization in alpha appear to play a role in inhibitory control, indicating that alpha activity NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 12 may be responsible for the inhibition of irrelevant information during working memory tasks.
Additional evidence supports this relation between frontal alpha synchronization and top-down processing (Benedek, Bergner, Könen, Fink, & Neubauer, 2011). Specifically, alpha is likely to be involved in the cortical control of lower level internal processing of information, with decreases in alpha activity reflecting more effortful and engaging processing. Because of this, it is implied that higher alpha activity is clearly involved with multiple processes, such as the inhibition of information and ability to switch tasks, in addition to alpha having an inverse relationship with the amount of cortical resources available. Therefore, it is likely that highly trait anxious individuals, despite showing higher frontal alpha activity, will also show lower alpha activity within non-frontal regions. This difference in spatial alpha activation is likely reflective of high trait anxious individuals demonstrating heightened distractibility, seen by higher frontal activity, and impaired attentional inhibition, displayed by lower non-frontal activity. These activity patterns should be reflective of the four tenets of Attentional Control
Theory.
Current Study
The current review demonstrates several impairments in the sub-components of inhibition and shifting set of the central executive, which result from high levels of trait anxiety.
Attentional Control Theory may provide an explanation to the impairing effects on the central executive experienced by high trait anxious individuals; however, the neural correlates of each of the theory’s tenets are currently poorly understood. As such, the current study was designed to investigate the underlying neural correlates for the respective sub-components of the central executive behind each tenet in individuals with high levels of trait anxiety. By identifying the neural correlates underlying Attentional Control Theory, we will be able to gain a better NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 13 understanding of the physiological mechanisms involved in anxiety and how the experience of anxiety impacts different aspects of working memory.
In order to achieve this goal, I investigated the underlying neural mechanisms, specifically involving the alpha frequency band, behind Attentional Control Theory and observed the effects of high trait anxiety on the central executive sub-components of inhibition and shifting set. First, participants were screened for their individual levels of trait anxiety by answering a state-trait anxiety inventory. While having their EEG recorded, participants were asked to perform two separate central executive tasks associated with shifting set and inhibition functions. Respective quantitative EEG frequencies were recorded during completion of the tasks, and low trait anxious and high trait anxious individuals were compared.
I propose that all four tenets of Attentional Control Theory will display an identical neural alpha frequency in individuals high in trait anxiety compared to those low in trait anxiety when completing the central executive tasks. This is due to the central executive’s overarching function with inhibition, shifting set, and processing efficiency being affected for such tasks.
Specifically, an impairment in the central executive should result in the impairment of the processing efficiency of both inhibition and shifting set tasks. Following this logic, this impairment of the overarching component of the central executive should demonstrate similar quantitative EEG alpha frequency band activity for both shifting set and inhibition tasks, as well as reflect an impairment in the processing efficiency of these tasks. Therefore, I propose two similar hypotheses for the completion of shifting set and inhibition tasks by individuals with high trait anxiety compared to those with low trait anxiety. First, individuals high in trait anxiety will show higher centralized frontal alpha activity and lower centralized parietal alpha activity compared to individuals low in trait anxiety during completion of an inhibition task. Second, NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 14 individuals high in trait anxiety will show higher centralized frontal alpha activity and lower centralized parietal alpha activity compared to individuals low in trait anxiety during completion of a shifting set task. This hypothesized higher frontal alpha activity for high trait anxious individuals completing both the shifting set and inhibition tasks was expected to represent an increase in their distractibility. In addition, the hypothesized lower parietal alpha activity was expected to be associated with the impairment of their attentional inhibition. This neural activity pattern was expected to be associated with the observed impairment in inhibition and shifting set processing efficiency in high trait anxious individuals, reflecting the components of Attentional
Control Theory. Finally, it was hypothesized that individuals high in trait anxiety will display significantly longer response times than individuals low in trait anxiety for both the inhibition and shifting set tasks. This increased response time will demonstrate the impairment high trait anxious individuals experience when completing central executive tasks. In addition, Attentional
Control Theory postulates that individuals in high and low trait anxiety display similar performance effectiveness for completion of shifting set and inhibition central executive functions. Therefore, I hypothesized that individuals high in trait anxiety will not demonstrate significant differences in accuracy compared to individuals low in trait anxiety for both the inhibition and shifting set tasks
Although there is evidence to suggest inhibition and shifting set are related to the overarching construct of the central executive (Miyake et al., 2000), there is still a degree of independence that exists between the two sub-component tasks. Therefore, although I hypothesized that both inhibition and shifting set tasks should demonstrate similar relationships to underlying neural frequencies, it is entirely possible that they may show respectively different neural frequencies. In addition, I hypothesized that the average performance accuracy for both NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 15 the shifting set and inhibition central executive functions should display a significant association with one another. By including both types of central executive tasks I was able to detect if such differences exist. If the two types of tasks indicated similar or different neural frequencies, the results will create several implications for the theory, as well as the understanding of how anxiety may have different impacts for different central executive tasks.
The results from this study will help shed light on the underlying neural mechanisms behind the tenets of Attentional Control Theory. Specifically, we now have a greater understanding of the alpha frequency band involved with the interaction anxiety has on impairment of the central executive. By understanding these processes, a foundation for future physiological research of Attentional Control Theory can thus be formed, and consequently, subsequent work investigating the impact of high trait anxiety on the central executive.
Method
Participants
Four hundred and thirteen participants were recruited at a large Midwestern university.
Participants were recruited from introductory psychology and marketing courses. Participants first completed an online Qualtrics survey through the department’s SONA research pool to determine individual level of trait anxiety, which granted one credit of the required research credits. Next, individuals meeting the specific cut off criteria for low and high trait anxiety were invited to participate in the study. These cutoff scores were one standard deviation above the gendered normative mean for high trait anxiety, and below one standard deviation below the gendered normative mean for low trait anxiety from the Spielberger’s State-Trait Anxiety
Inventory Form-Y (See Appendix A; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983).
This criteria was based on the gendered normative data for male and female college students NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 16 provided in the State-Trait Anxiety Inventory Form-Y manual. Thirty four (23 females, 11 males, 32 Caucasian, 1 African American, 1 Asian, ages 18 to 23) participants invited to the laboratory received an additional one and a half credits, and five dollars for completion of this portion of the study. Each participant was screened for any prior neurological issues, or medications that may affect the validity of their brain activity patterns prior to invitation to enter the laboratory for the study. Concussions, history of seizures, and medications affecting serotonin (SSRI SNRI) and benzodiazepines resulted in exclusion from the study. This exclusion criteria was used to ensure participants were not on any medications or had brain damage that might alter neurotransmitter activity, thus reflecting alteration of alpha activity patterns. Post- cleaning of BIOSemi EEG files resulted in sixteen (6 males and 10 females) participants in the high anxiety condition, and fourteen (6 males and 8 females) participants in the low anxiety condition that were used for data analysis of the Eriksen Flanker Task. Post-cleaning of
BIOSemi EEG files resulted in sixteen (6 males and 10 females) participants in the high anxiety condition, and thirteen (6 males and 7 females) participants in the low anxiety condition used for data analysis of the Berg Wisconsin Card Sorting Test-64.
Materials
The study utilized Spielberger’s State-Trait Anxiety Inventory Form-Y (See Appendix A;
Spielberger et al., 1983). The State-Trait Anxiety Inventory (STAI) is a self-report screening measure for state and trait anxiety (Spielberger et al., 1983). The STAI is comprised of 40 total items: 20 items assessing level of state anxiety, and 20 items assessing level of trait anxiety. Trait anxiety items were utilized for scoring, and state anxiety items were ignored. Trait anxiety items assess the chronic psychological trait of anxiety using questions such as, “I feel that difficulties are piling up so that I cannot overcome them”, “I worry too much over something that doesn’t NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 17 really matter”, and “I am a steady person”. STAI items are weighted and specific items are reverse scored to obtain a total score ranging from 20 – 80, with higher scores reflecting greater trait anxiety. The STAI has demonstrated acceptable test-retest reliability (r = 0.65 to 0.75) over a two month interval, and good internal consistency (α = 0.86 to 0.95; Spielberger et al., 1983).
STAI scores have also displayed content validity (Spielberger, 1989), seen where the instrument measures all facets of Spielberger’s trait anxiety. In addition, the STAI has demonstrated concurrent validity as measured in comparison with scores on the Taylor Manifest Anxiety Scale
(r = 0.73; Taylor, 1953) and Cattell and Scheier's Anxiety Scale Questionnaire (r = 0.85; Cattell
& Scheier, 1963). Scores on the STAI have also demonstrated the ability to distinguish between high and low reports of anxiety for both state and trait scales in college undergraduate populations (Metzger, 1976).
Individual inhibition central executive abilities were measured using the Eriksen Flanker
Task (See Figure 1; Eriksen & Eriksen, 1974). Participants were presented a target stimuli surrounded by other non-target stimuli (i.e., flankers) that correspond in either the same directional response as the target stimuli, or the opposite response or to neither. Consistent corresponding non-target stimuli are considered congruent flankers, opposite responding non- target stimuli are considered incongruent flankers, and neither corresponding or opposite responding non-target stimuli are considered neutral flankers. Correct responses are based on the accuracy of participants selecting congruent non-target stimuli. Previous research has supported the psychometric properties of the Eriksen Flanker Task as a measure of response inhibition.
Attentional processes are commonly assessed by the flanker interference, seen in the similar
Attentional Network Task (Fan, McCandliss, Sommer, Raz, Posner, 2002). Flanker interference is associated with response inhibition (Friedman & Miyake, 2004), supporting the validity of the NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 18
Eriksen Flanker Task for assessing inhibition. Processing efficiency performance was based on the average response time for the total number of trials. Performance accuracy was based on the averaged correct selection for non-congruent target stimuli for the total number of trials.
Individual shifting set central executive abilities were measured using the Berg
Wisconsin Card Sorting Test-64 from the Psychology Experiment Building Langauge (PEBL;
See Figure 2; Mueller, 2011). The procedure is based on the outline provided by Berg’s (1948) original measure of cognitive flexibility. Participants were presented with four piles of cards varying in color, number of objects on the card, and the shapes on each card. An additional card with unique characteristics is placed to the side, and participants are required to place that card in one of the four piles based on the rule for the characteristics of the stimuli. This rule for correctly placing the card into the appropriate file changes, and the participant must switch their strategy based on the shape, color, or number of objects on the card. Participants’ ability to shift sets is based on overall perseverative error. Perseverative error was scored based on responses made in which the earlier rule is incorrectly employed. This task procedure (Berg, 1948) was designed to assess cognitive flexibility, specifically with shifting set. Age-related differences between the standard Wisconsin Card Sorting Test (Haaland, Vranes, Goodwin, & Garry,1987; Strauss,
Sherman, & Spreen, 2006) and the Berg Card Sorting Test have shown to be congruent with one another (Piper, Li, Eiwaz, Kobel, Benice, Chu, Olsen, Rice, Gray, & Mueller, 2013). The Berg
Wisconsin Card Sorting Test-64 is also conceptually similar to current computerized card sorting tasks, such as the Intra-Extra Dimensional Set Shift (Huizinga, Dolan, & Van Der Molen, 2006) and Wisconsin Card Sorting Test distributed by Psychological Assessment Resources (Heaton,
Chelune, Talley, Kay, & Curtiss, 2003). These tasks are common measures of the shifting set central executive function (Snyder, Miyake, & Hankin, 2015). Low correlations between the NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 19
Berg Card Sorting Test and other central executive functioning tasks (e.g., Trail Making Test and
Tower of London) is congruent with sub-component specificity of central executive domains
(Miyake et al., 2000; Smith, Need, Cirulli, Chiba-Falek, & Attix, 2013), providing evidence for discriminant validity. Additional evidence (e.g., Piper, Mueller, Geerken, Dixon, Kroliczak,
Olsen, & Miller, 2015) has demonstrated the functional equivalence between the current
Wisconsin Card Sorting Test and the Berg Card Sorting Test. This was indicated by five correlations of other tests showing significance and similar magnitude for both and the Berg
Card Sorting Test and Wisconsin Card Sorting Test. Moderate test-retest reliability was observed for the Berg Card Sorting Test (r > 0.3; Piper et al., 2015). Processing efficiency performance was based on the average response time for the total number of trials. Performance accuracy was based on the averaged correct selection based on the card sorting rule for the total number of trials.
Procedure
Participants completed an informed consent for the study and a brief medical questionnaire to assess their history of medications and seizures. Next, participants completed the State-Trait Anxiety Inventory in order to assess individual levels of trait anxiety. These questionnaires were administered online via Qualtrics. Participants that met a designated cut off score (i.e., +/- 1 standard deviations from each individual’s gendered normed mean) were contacted and asked to enter the laboratory for the study. This cut-off was used to ensure an appropriate sample size was obtained in order to complete the study and analyze differences between the two groups (high and low trait anxiety). Upon entry to the laboratory, participants were attached to a 72-channel electrode EEG to assess their respective neural activity patterns. NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 20
After ensuring proper electrode readings via BIOSemi reading software, participants were instructed to remain as still as possible during task completion in order to prevent interference.
Participants were first asked to remain still for a period of three minutes to record resting baseline frequencies. Next, participants were randomly assigned to complete one of the two tasks, in order to counterbalance and eliminate any order effects. Participants completed the Berg
Wisconsin Card Sorting Test-64 in order to assess ability of shifting attentional resources.
Participants also completed the Eriksen Flanker Task in order to assess the inhibition of attentional resources toward irrelevant information. Two separate BIOSemi data sets were saved for each of the two tasks.
After completion of these central executive tasks, EEG equipment was removed, and participants were given a brief debriefing of the experiment. Afterward, participants’ respective files were saved and stored for further statistical analyses.
Apparatus and Stimuli
Participants performed the Berg Wisconsin Card Sorting Test-64 from the Psychology
Experiment Building Language (PEBL; Mueller, 2011), and the Eriksen Flanker Task (Eriksen
& Eriksen, 1974) paradigms on a Viewsonic Aurora R_4 computer running Windows 7. The monitor was a 17” LCD screen with a 60 Hz refresh rate. The Berg Wisconsin Card Sorting Test-
64 paradigm was programmed using the PEBL (Mueller, 2011), and the Eriksen Flanker Task
(Eriksen & Eriksen, 1974) paradigm was programmed using E-Prime 2.0 software
(Psychological Software Tools, Pittsburgh, Pennsylvania, USA). Participants were seated at a viewing distance of 57 cm from the monitor. The EEG recording was taken with 64-recording channels using silver chloride electrodes with a BioSemi Active Two system
(http://www.biosemi.com; BioSemi B.V., Amsterdam, Netherlands) configured to the 10–20 NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 21 system. Participants responded to targets via a standard keyboard. All EEG data was collected by an Alienware X51_R2 synchronized to receive behavioral response from the computer running the task.
Data Cleaning & Processing
Initial BIOSemi files for all three conditions (resting baseline, Berg Wisconsin Card
Sorting Test-64, and Eriksen Flanker Task) were converted into MATLAB for individual electrode readings. Electrodes were considered invalidated based on sweat or electrical interference. Data were filtered with a fifth-order Butterworth filter of 0.5 – 55 Hz and referenced digitally to averaged mastoids. Data files were visually inspected and hand cleaned for further artifact rejection and cleaning based on any electrical interference. Invalidated electrodes were interpolated for estimated activity.
After cleaning, the overall alpha band frequency activity for each electrode was assessed.
This was done via spectral analysis, in which alpha activity was represented by frequency ratings between 8 and 12 Hz (Hammond, 2011). The average frequency band for alpha (8 – 12 Hz) was recorded for each task, and used for data analysis. Next, a natural log transformation of these alpha frequency bands was conducted and used for statistical analyses.
Spectral Analyses
For the data collection interval at a 1024 Hz sample rate and down sampled to 512 hz for analyses, a 3 second Hanning tapered window with a 75% overlap was applied to extract contiguous artifact-free quantitative data and a FFT (Fast Fourier Transformation) was used to calculate the average spectral power (ASP) of the peak alpha EEG frequency (see: https://www.mathworks.com/; MATLAB and Statistics Toolbox Release R2014a, The
MathWorks, Inc., Natick, MA, USA.). Alpha frequency windows were defined as 8-12 Hz NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 22
(Hammond, 2011). In this way the average ASP (μV2) value for each frequency was obtained per participant across the entire recording interval, excluding data rejected due to blinks and artifacts. Specific electrode clusters were examined to represent frontal (AFz, Fz, F1, F2) and parietal (CPz, Pz, P1, P2) brain regions across the scalp. This criteria was selected based on the spatial regions these electrodes covered across the scalp, and based on similar locations used to assess frontal (Quiroga, Sakowitz, Basar, & Schürmann, 2001) and parietal (Rushsow, Grön,
Reuter, Spitzer, Hermle, & Kiefer, 2005) brain regions in past literature.
Results
Eriksen Flanker Task Electroencephalography Results
A 2 x 2 x 2 mixed ANOVA examining task (baseline and Eriksen Flanker Task) by topographical region (frontal and parietal) by anxiety level (high anxiety and low anxiety) was conducted in order to examine the differences in alpha band frequency activity in central frontal and central parietal brain regions for baseline resting state and the Eriksen Flanker Task for individuals high and low in trait anxiety. This test was conducted to determine if individuals high in trait anxiety displayed higher centralized frontal activity and lower parietal activity compared to individuals low in trait anxiety during the completion of an inhibition central executive task.
The results of this analysis showed a significant interaction in alpha activity for task by anxiety
2 type [F (1, 28) = 6.045, p < 0.05, ηp = 0.178; see Figure 3). Individuals high in trait anxiety demonstrated higher alpha band activity during their resting state (M = -2.79, SD = 0.40) compared to those low in trait anxiety (M = -2.94, SD = 0.23). However, during completion of the Eriksen Flanker Task, individuals high in trait anxiety yielded similar levels of alpha activity
(M = -2.85, SD = 0.33) compared to individuals low in trait anxiety (M = -2.90, SD = 0.24). The results also yielded a significant main effect for topographical region, such that there was a NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 23 difference in alpha band frequency activity between frontal and parietal brain regions [F (1, 28) =
2 20.488, p < 0.001, ηp = 0.423]. Frontal brain regions (M = -2.92, SD = 0.29) displayed a lower degree of alpha activity than parietal brain regions (M = -2.80, SD = 0.38) during the resting state. Frontal brain regions (M = -2.95, SD = 0.25) also displayed lower levels of alpha activity than parietal brain regions (M= -2.80, SD = 0.32) for the Eriksen Flanker Task. The three-way interaction between task, topographical region, and anxiety level was not examined as no hypothesis was given regarding this effect. Individual means for each condition can be seen below in the appendices (Table 2). Individual topographical and spectral data for resting state
(Figure 4) and completion of the Eriksen Flanker Task (Figure 5) can be seen below.
Berg Wisconsin Card Sorting Test-64 Electroencephalography Results
A 2 x 2 x 2 mixed ANOVA examining task (baseline and Berg Wisconsin Card Sorting
Test-64) by topographical region (frontal and parietal) by anxiety level (high anxiety and low anxiety) was also conducted in order to examine the differences in alpha band frequency activity in central frontal and central parietal brain regions for baseline resting state and the Berg
Wisconsin Card Sorting Test-64 for individuals high and low in trait anxiety. This test was conducted to determine if individuals high in trait anxiety displayed higher centralized frontal activity and lower parietal activity compared to individuals low in trait anxiety during the completion of a shifting set central executive task. The results of this analysis showed an
2 interaction in alpha activity for task by topographical region [F (1, 27) = 9.604, p < 0.01, ηp =
0.262] (Figure 6). Individuals in both high and low trait anxiety displayed significantly different levels of alpha band frequency activity in their frontal and parietal brain regions. Specifically, frontal brain regions (M = -2.921, SD = 0.30) demonstrated significantly lower levels of alpha activity than parietal brain regions (M = -2.81, SD = 0.39) during the resting state. However, NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 24 during completion of the Berg Wisconsin Card Sorting Test-64, both frontal (M = -3.05, SD =
0.18) and parietal brain regions (M = -3.03, SD = 0.22) demonstrated similar levels of alpha activity. The results also yielded a significant main effect for topographical region [F (1, 27) =
2 9.098, p < 0.01, ηp = 0.252]. Frontal brain regions displayed a significantly lower degree of alpha activity than in the parietal brain regions across all conditions. In addition, the results
2 yielded a significant main effect for task [F (1, 27) = 11.777, p < 0.01, ηp = 0.304]. Regardless of individual level of trait anxiety level, all individuals demonstrated a significant decrease in alpha band frequency activity for the Berg Wisconsin Card Sorting Test-64 (M = -3.04, SD =
0.20) compared to the resting state (M = -2.86, SD = 0.34). The three-way interaction between task, topographical region, and anxiety level was not examined as no hypothesis was given regarding this effect. Individual means for each condition can be seen below in the appendices
(Table 2). Figures representing the topographical and spectral data during the resting state and completion of the Berg Wisconsin Card Sorting Test-64 were omitted. This omission was due to the nature of the Berg Wisconsin Card Sorting Test-64 eliciting a significant number of saccades.
Because of this, a more precise algorithm in MATLAB was utilized to extract these data, resulting in the inability to provide specific figures in EEGLab for the topographies for this task.
Eriksen Flanker Task and Berg Wisconsin Card Sorting Test-64 Behavioral Results
Two independent samples t-tests were conducted to assess the differences in average response time for the Eriksen Flanker Task and Berg Wisconsin Card Sorting Test-64 between individuals high in trait anxiety and individuals low in trait anxiety. These were conducted to replicate prior behavioral studies and demonstrate that individuals high in trait anxiety would display a significantly greater duration of average response time while completing these central executive tasks compared to individuals low in trait anxiety. The results of these analyses yielded NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 25 non-significant differences in average response time between high and low trait anxious individuals for the Eriksen Flanker Task [t (31) = -0.220, ns] and the Berg Wisconsin Card
Sorting Test-64 [t (32) = -0.428, ns]. Two additional independent samples t-tests were conducted to examine percentage of accuracy for the Eriksen Flanker Task and Berg Wisconsin Card
Sorting Test-64. These were conducted to replicate prior behavioral studies and demonstrate that individuals in high and low trait anxiety would show similar performance on these central executive tasks. The results of these analyses yielded non-significant differences in percent accuracy between high and low trait anxious individuals for the Eriksen Flanker Task [t (31) = -
0.314, ns] and Berg Wisconsin Card Sorting Test [t (32) = -0.050, ns]. Individual means for each condition can be seen below in the appendices (Table 1).
A correlational analysis was conducted for average performance accuracy for completion of the Eriksen Flanker Task and Berg Wisconsin Card Sorting Test-64. This analysis was utilized to assess the degree of association between these two sub-components of central executive tasks to determine that the two tasks demonstrated some independence from one another, while also having a small association between the two types of tasks. A significant small positive correlation was found for average performance accuracy on the Berg Wisconsin Card Sorting
Test-64 and average performance accuracy on the Eriksen Flanker Task [r (33) = 0.394, p <
0.05]. This suggests that the average performance accuracy for completion of these two central executive tasks share some association with one another, but also demonstrate some independence from one another (Figure 7). Individual means for each condition can be seen below in the appendices (Table 1).
Discussion NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 26
Attentional Control Theory postulates that individuals high in trait anxiety experience an impairment in working memory compared to individuals low in trait anxiety (Eysenck &
Derakshan, 2011). This impairment involves the impediment in processing efficiency of central executive tasks involving shifting set and inhibition. Currently, various studies have supported this theory through behavioral findings that low trait anxious individuals demonstrate significantly lower response times than individuals high in trait anxiety for these central executive tasks (Eysenck et al., 2005; Derakshan & Eysenck, 2009; Ansari & Derakshan, 2010;
Johnson, 2009; Ansari et al., 2008; Murray & Jannell, 2003). Despite the abundance of behavioral research surrounding Attentional Control Theory, relatively little work has investigated the underlying neurological mechanisms involved with this impairment experienced by high trait anxious individuals. Current work has found resting asymmetrical differences in brain activity between low and high trait anxious individuals (Thibodeau et al., 2006; Telzer et al., 2008; Avram et al., 2010). In addition, prior work has demonstrated that high trait anxious individuals displayed greater N2 ERP responses than low trait anxious individuals on an inhibition task, indicating greater impairment of the inhibition in high trait anxious individuals
(Righi et al., 2009). Furthermore, high trait anxious individuals exhibit greater EEG desynchronization compared to low trait anxious individuals on inhibition tasks, indicating an impairment in inhibition functioning (Savostyanov et al., 2009). Despite this work, there currently exists a gap in the literature surrounding the underlying neural frequencies of
Attentional Control Theory, specifically examining alpha activity, which is involved with attentional and cognitive tasks. Specifically, alpha activity reflects differences in cognitive engagement and arousal states. Because of the paucity of cognitive neuroscience research in this area, the current study sought to investigate the underlying neural correlates of Attentional NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 27
Control Theory by specifically examining the alpha band frequency differences in high and low trait anxious individuals completing both inhibition and shifting set central executive tasks.
Electroencephalography Alpha Band Frequency Activity and the Eriksen Flanker
Inhibition Task
I hypothesized that individuals high in trait anxiety would display higher centralized frontal alpha activity and lower centralized parietal alpha activity compared to low trait anxious individuals during completion of an inhibition central executive task. My results did not support this hypothesis. High trait anxious individuals demonstrated similar levels of overall alpha activity across frontal and parietal brain regions during the completion of the Eriksen Flanker
Task (inhibition task). However, differences in alpha activity were observed between resting state and the Eriksen Flanker Task between high and low trait anxious individuals. This implies that high trait anxious individuals experience the same level of alertness as low trait anxious individuals during the completion of an inhibition central executive task, but are likely to be less alert during their resting state than during completion of such a task. Additionally, the equivalent resting state level of alpha in low trait anxious individuals at rest and at task suggests that these individuals may contain a more stable attentional state overall. This may indicate that low trait anxious individuals may be more prepared to engage in a task from “at rest” than high trait anxious individuals. This observed alpha activity difference at resting versus active state may individual differences between low and high trait anxious individuals, and reflect pre-existing factors for these individuals. It is also interesting to note that the results of the current study indicated a difference between frontal and parietal brain regions for both the resting state and the inhibition task, regardless of the level of trait anxiety. Specifically, frontal brain regions displayed less alpha band activity than parietal brain regions for both resting state and the NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 28
Eriksen Flanker Task, indicating that both high and low anxious individuals demonstrated low levels of resting alpha in frontal executive cognitive processing across both the resting state and the completion of an inhibition task. This demonstrates that frontal brain regions are experiencing cognitive excitation across both the resting state and inhibition task. This is due to the lower alpha activity present in frontal brain regions, which suggests that both high and low trait anxious individuals are experiencing less cognitive inhibition during the course of the study.
The observed differences in alpha activity between resting state and the Eriksen Flanker
Task between high and low trait anxious individuals in the current study somewhat replicates prior work (Knyazev et al., 2002; Knyazev & Slobodskaya, 2003; Knyazev et al., 2004). This higher alpha activity present within high trait anxious individuals in their resting state as compared to during task performance may reflect a lower degree of attentional resources to use compared to low trait anxious individuals. Furthermore, this higher alpha activity in high trait anxious individuals may reflect underlying activity between the thalamus and reticular activating system (Robinson 1999, 2000, 2001). Specifically, this would indicate differences in arousal state and sensory processing in high trait anxious individuals compared to low trait anxious individuals. It may be likely that high trait anxious individuals are experiencing a lower arousal state compared to low trait anxious individuals, possibly due to an internal focus, and therefore must martial their resources when presented with an inhibition task. Therefore, high trait anxious individuals are likely to experience a lack of excitation during their resting state, as well as decreased cognitive alertness as compared to when they perform such a task. This pattern of neural activity represents differences in arousal and inhibition between high trait anxious individuals and low trait anxious individuals. Because EEG is limited in the spatial representation of subcortical structures, future work should investigate this association. NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 29
Specifically, researchers should utilize techniques that demonstrate greater spatial mapping abilities, such as fMRI, to examine the differences in the thalamic and reticular activating system activity between individuals low and high in trait anxiety. These findings could help increase the understanding of the neurological mechanisms of Attentional Control Theory, and provide greater implications of the degree of arousal and inhibition present for high trait anxious individuals compared to low trait anxious individuals.
The observed results did not show a decrease in overall alpha activity between the resting state and Eriksen Flanker task across participants. These results are inconsistent with prior work demonstrating that increased cognitive load, which is observed during the completion of working memory tasks, such as the Eriksen Flanker Task, result in the decrease of alpha band activity
(Gevins et al., 1997; Stipacek et al., 2003; Fink et al., 2005). It is possible that the Eriksen
Flanker Task may not have recruited the same degree of cognitive load as other inhibition tasks used in previous work. A possible explanation for this result is that the specific form of Eriksen
Flanker Task used in the current study was not difficult enough to necessitate additional attentional resources, as indicated by the ceiling effect observed in individuals completing the task (i.e., the minimum accuracy percentage score was 94.7% for the Eriksen Flanker Task for all participants). However, it is important to note that high anxious individuals did experience an overall change in alpha band activity, specifically showing a decrease in overall alpha activity.
Alpha desynchronization is an important indicator for overall alertness and activation processes, indicating that high trait anxious individuals may have experienced cognitive changes as they began to engage in the task. Specifically, these cognitive changes experienced likely involved an increase in attentional processing in high anxious individuals as they shifted from their resting state to completion of the Eriksen Flanker Task. Additionally, this implies that high trait anxious NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 30 individuals were experiencing an increase in cognitive load. The current work lays the foundation for a better understanding of the neural frequencies underlying Attentional Control
Theory and potential individual differences demarcated by these frequencies in the population of interest.
Interestingly, the observed results demonstrated that individuals in both low and high trait anxiety experienced lower frontal alpha band activity compared to parietal alpha band activity across both their resting state and during the completion of the Eriksen Flanker Task. Higher alpha within the parietal region likely indexes a state of attentional relaxation regardless of the change in cognitive task. This reiterates that the Eriksen Flanker Task may not have been difficult to complete, which resulted in a less taxed cognitive engagement system during its completion. In addition, the lower alpha activity within frontal regions indexes a greater degree of cognitive engagement. This implies that individuals may have become cognitively engaged by the presence of a novel situation (e.g. being in the experiment), and did not require attentional alertness. Because of this, further work should incorporate a more difficult form of the Eriksen
Flanker Task, or a more cognitively demanding inhibition central executive task. This would allow for the direct examination of alpha band activity changes in the frontal and parietal brain regions between resting state and an inhibition task between high and low trait anxious individuals. Such results would further complete our understanding of the underlying mechanisms involved in Attentional Control Theory.
Electroencephalography Alpha Band Frequency Activity and the Berg Wisconsin Card
Sorting Test-64 Shifting Set Task
I hypothesized that individuals high in trait anxiety would display higher centralized frontal alpha activity and lower centralized parietal alpha activity compared to low trait anxious NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 31 individuals during completion of a shifting set central executive task. My results did not support this hypothesis. However, the results yielded a significant main effect for the task. Specifically, both high and low trait anxious individuals experienced a change in overall alpha activity between their resting state and the Wisconsin Card Sorting Test-64. Overall alpha band activity decreased significantly during the completion of the Wisconsin Card Sorting Test-64. This implies an overall change in mental arousal state during the completion of this shifting set task.
Furthermore, this suggests that this shifting set task did ultimately differ from the resting state.
This finding is consistent with prior work that also showed an increase in cognitive load for working memory tasks resulting in the desynchronization of alpha band activity (Gevins et al.,
1997; Stipacek et al., 2003; Fink et al., 2005). In addition, the observed results also displayed a significant main effect of topographical regions across both the resting state and completion of the Berg Wisconsin Card Sorting Test-64. Specifically, frontal brain regions displayed a significantly lower degree of alpha activity compared to parietal brain regions across all conditions. This topographical difference in alpha activity suggests there are differences in the cognitive elements of conscious processing across the resting state and Berg Wisconsin Card
Sorting Test-64. This result further replicates the above finding that individuals, regardless of the level of trait anxiety, are likely to experience cognitive engagement, while also requiring a large degree of attentional alertness.
These results demonstrated that individuals low and high in trait anxiety displayed higher levels of alpha activity in parietal brain regions than frontal brain regions during a resting state. However, during completion of the Berg Wisconsin Card Sorting Test-64, both frontal and parietal brain regions demonstrated similar levels of alpha activity, regardless of level of anxiety.
This suggests that individuals fully engaged both frontal-cognitive and parietal-attentional NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 32 networks during the completion of the Berg Wisconsin Card Sorting Test-64. Specifically, this can be seen by the significant decrease in alpha activity in the parietal brain region between the resting state and Berg Wisconsin Card Sorting Test-64. This ultimately suggests that individuals’ attentional systems became more engaged during completion of the Berg Wisconsin Card
Sorting Test-64. In addition, both high and trait anxious individuals experienced similar alpha activity in frontal and parietal brain regions during the completion of the Berg Wisconsin Card
Sorting Test-64. These results indicate that both high and low trait anxious individuals are likely to experience a similar level of cognitive engagement for the completion of the Berg Wisconsin
Card Sorting Test-64. Furthermore, the significant decrease in parietal activity observed during the completion of the Berg Wisconsin Card Sorting Test-64 compared to the parietal level of alpha activity observed during resting state likely reflects an increase in the level of arousal experienced by both high and low trait anxious individuals. This decrease in alpha during task completion has also been observed in prior work investigating alpha band activity during the completion of other attentionally engaging tasks (Klimesch, 1997, Klimesch, Doppelmayr,
Russegger, Pachinger, & Schwaiger, 1998). Others have also demonstrated a decrease in parietal alpha activity as cognitive load increased for working memory tasks (Gevins et al., 1997;
Stipacek et al., 2003; Fink et al., 2005).
Processing Efficiency for the Eriksen Flanker Task and Berg Wisconsin Card Sorting Test-
64
I hypothesized that individuals high in trait anxiety would demonstrate significantly longer average response times than individuals low in trait anxiety for both the Eiksen Flanker
Task, and Berg Wisconsin Card Sorting Test-64. My results were unable to support this hypothesis. Both individuals high and low in trait anxiety demonstrated similar average response NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 33 times for both the Eriksen Flanker Task and Berg Wisconsin Card Sorting Test-64. These obtained results are inconsistent with previous behavioral research investigating Attentional
Control Theory. Specifically, researchers have demonstrated an impairment in high trait anxious individuals for processing efficiency on central executive tasks involving inhibition (Derakshan
& Eysenck, 2009; Righi et al., 2009; Ansari & Derakshan, 2010). However, in the current study only thirty-four participants participated, which may explain the lack of significant results observed regarding differences in processing efficiency between low and high trait anxious individuals for the inhibition and shifting set tasks. For example, Ansari and colleagues (2008) reported impairments in processing efficiency for high trait anxious individuals on a shifting set task in a study that incorporated fifty-nine participants. My lack of statistical power can be supported by an examination of the mean scores obtained (Table 1). Specifically, low trait anxious individuals (M = 504.24) demonstrated a lower average response time than high trait anxious individuals (M = 511.54) for the Eriksen Flanker Task. Additionally, low trait anxious individuals (M = 1124.30) demonstrated a lower average response time than high trait anxious individuals (M = 1172.22) for the Berg Wisconsin Card Sorting Test-64. A number of other possibilities could potentially explain these inconsistent findings between my obtained results and prior research. First, it is possible that the Eriksen Flanker Task was too easy to complete.
This explanation is due to the ceiling effect observed for this task’s completion (i.e., the minimum accuracy percentage score was 94.7%). However, this rational cannot be used to explain the lack of significant differences in processing efficiency observed with the Berg
Wisconsin Card Sorting Test. This task demonstrated an average accuracy score of approximately 77.8%, which would indicate that the task required increased cognitive engagement during its completion. Therefore, although the Eriksen Flanker Task’s ceiling effects NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 34 may serve as an explanation for the lack of effect on processing efficiency differences between low and high trait anxious individuals, this same reasoning cannot be applied to the lack of effect observed with the Berg Wisconsin Card Sorting Test-64. Another explanation for this lack of significant differences in impaired processing efficiency effect in high trait anxious individuals could result from the criteria of cut off scores appointed in the current study. I chose to utilize the gendered normalized scores of college students from Spielberger’s State-Trait Anxiety Inventory
Form-Y manual (Spielberger et al., 1983). Specifically, I chose to recruit individuals who had scored +/- one standard deviation from their respective mean (male or female means). This resulted in high trait anxious males requiring a score of 47.48 or higher, and high trait anxious females requiring a score of 50.55 or higher. Low trait anxious males required a score of 29.12 or lower, and low trait anxious females required a score of 30.25 or lower. This cut off criteria may not have fully captured individuals who are exclusively high or low in trait anxiety. For example, perhaps a stricter cut off score of +/- two standard deviations would have allowed for a more valid representation of individuals low and high in trait anxiety. The selected criteria may have also led to the recruitment of individuals who had higher or lower than average trait anxiety, but did not fall into the maladaptive high or low range of trait anxiety. Additionally, this criteria requirement led to the categorization of the variable of trait anxiety, which is viewed on a dimensional level. This categorization may have led to a loss in the recruitment of the entire range of individuals in trait anxiety, and thus a loss in total variance. It is also important to note the differences in gendered mean criteria for both low and high trait anxiety categorization. This led to different trait anxiety score requirements to be categorized as either low or high trait anxiety for males and females. This use of gendered means to create groups for high and low anxiety may have led to null results due to using a non-gendered statistical analysis. Finally, NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 35 another possible explanation of the lack of significant effect in impaired processing efficiency in high trait anxious individuals was due to the nature of the tasks used. This effect was seen on other inhibition tasks such as, the emotional stroop task (Bar-Haim, Lamy, Pergamin,
Bakermans-Kranenburg, Van Ijzendoorn, 2007), and antisaccade task (Derakshan, Ansari,
Hansard, Shoker, & Eysenck, 2009). Perhaps the Eriksen Flanker Task utilized with this study was not appropriate for demonstrating the expected impairment in inhibition. This impairment of processing efficiency in high trait anxious individuals was also seen on shifting set tasks such as a mixed antisaccade task (Ansari et al., 2008). It is possible that the Berg Wisconsin Card
Sorting Test-64 with was not appropriate for demonstrating the expected impairment in shifting set. Future research should address this concern by further examining the possible differences in impaired processing efficiency on various inhibition and shifting set tasks for individuals high in trait anxiety.
The obtained null results for processing efficiency differences between low and high trait anxious individuals on the Eriksen Flanker Task and the Berg Wisconsin Card Sorting Test-64 may be due to several other causes. The participants recruited were college educated students.
This may indicate that the individuals categorized as high trait anxious in this sample may be capable of adapting to and overcoming several of the negative and maladaptive behaviors commonly exhibiting by individuals high in trait anxiety. This would imply that these high trait anxious individuals would not display an impairment in processing efficiency on attentionally demanding cognitive tasks. Another possible explanation for these null results is that the previous research that identified impairments in processing efficiency may have only found such significant differences between high and low trait anxiety in specific populations. It is entirely possible that other non-published research has also found a null effect for these differences, NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 36 implying that Attentional Control Theory’s postulates regarding the impairment in processing efficiency may not be an entirely correct and/or valid representation of the underlying processes between low and high trait anxiety.
I hypothesized that individuals low and high in trait anxiety would demonstrate similar performance effectiveness for the Eriksen Flanker Task and Berg Wisconsin Card Sorting Test-
64. As expected, my results supported this notion of similar behavioral performance on shifting set and inhibition tasks between low and high trait anxious individuals. This finding is consistent with prior work demonstrating that individuals low and high in trait anxiety display similar performance effectiveness on shifting set and inhibition tasks (Eysenck et al., 2005; Derakshan
& Eysenck, 2009; Ansari & Derakshan, 2010; Johnson, 2009; Ansari et al., 2008; Murray &
Jannell, 2003). However, it is important to note that this result should be interpreted with caution, specifically regarding the Eriksen Flanker Task. This is due to the ceiling effect observed for individuals completing this task. Future research should further investigate this finding by examining the performance effectiveness between low and high trait anxious individuals completing the Eriksen Flanker Task.
Finally, I hypothesized that both the performance accuracy for the Eriksen Flanker Task and Berg Wisconsin Card Sorting Test would demonstrate a significant relationship with one another. My results supported this hypothesis, showing a small significant association between average performance accuracy for inhibition and shifting set tasks. This suggests that these two constructs are related to one another, but also demonstrate some degree of independence. This finding further supports the notion that these two sub-components of central executive tasks are a part of the overarching construct of the central executive (Miyake et al., 2000).
Future Directions NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 37
These results provide several useful implications for future research, and therapeutic techniques involving neurofeedback. Specifically, I have demonstrated alpha band frequency differences between low and high trait anxious individuals between their resting state and completion of an inhibition central executive task. This could indicate that high trait anxious individuals may have fewer online attentional resources for attentional engagement compared to low trait anxious individuals. This may provide useful differential information describing the presence of anxiety in therapeutic treatments, and provide a physiological measurement for the degree of anxiety an individual may be experiencing. In addition, it can also provide the framework for future physiological measurements in determining the effectiveness of different therapeutic treatments of anxiety disorders in addition to acting as a neural marker for individual differences in high and low trait anxiety individuals.
This work provides some novel framework in the understanding of Attentional Control
Theory, specifically involving alpha band frequency activity. Future work should focus in on the role of alpha oscillations in Attentional Control Theory for high and low trait anxious individuals completing inhibition and shifting set central executive tasks. In addition, other neural frequencies, such as theta band frequency and beta band frequency, should be investigated to investigate their involvement in Attentional Control Theory. Researchers should also address several of the aforementioned concerns in order to obtain a more valid and holistic representation of the underlying neural frequencies association with Attentional Control Theory. Because recent work has demonstrated that working memory capacity may serve as a buffer for the impairment observed in high trait anxious individuals (Wright, Dobson, & Sears, 2014), future research should further investigate the underlying neural frequencies involved with this attenuating effect.
Conclusion NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 38
I found that individuals high in trait anxiety displayed differences in alpha band frequency activity compared to low trait anxious individuals between resting state and the
Eriksen Flanker Task. This might suggest that high trait anxious individuals may have difficulties with engaging concentration and attentional control when required to complete cognitively demanding tasks. These individuals high in trait anxiety were required to engage in attentional resources during task completion compared to low trait anxious individuals who were already prepared for the task. This finding provides a framework for future neurocognitive research underlying Attentional Control Theory. In addition, this may serve as a useful indicator for the presence of anxiety in therapeutic treatments. However, it should be noted that both high and low trait anxious individuals appeared to display a similar level of overall alpha activity during the completion of the Eriksen Flanker Task. Furthermore, both high and low trait anxious individuals demonstrated a similar level of alpha activity during the completion of the Berg
Wisconson Card Sorting Test-64. These results provide useful insights into the nature of high and low trait anxiety, the impact they have on the completion of shifting set and inhibition central executive tasks, and add to the literature involving Attentional Control Theory. It is important to note that I was able to replicate prior findings that demonstrated similar performance effectiveness between high and low trait anxious individuals on central executive tasks involving inhibition and shifting set (Eysenck et al., 2005; Derakshan & Eysenck, 2009;
Ansari & Derakshan, 2010; Johnson, 2009; Ansari et al., 2008; Murray & Jannell, 2003).
Acknowledgements
This work was made possible by Ball State University’s Hollis Fund grant.
NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 39
References
Anderson, J. R. (1994). Learning and Memory: An Integrated Approach. New York: Wiley.
Ansari, T. L., Derakshan, N., & Richards, A. (2008). Effects of anxiety on task switching:
Evidence from the mixed saccade task. Cognitive, Affective, & Behavioral Neuroscience,
8, 229-238. doi: 10.3758/CABN.8.3.229
Ansari, T. L., & Derakshan, N. (2010). Anxiety impairs inhibitory control but not volitional
action control. Cognition and Emotion, 24, 241-254. doi: 10.1080/02699930903381531
Avram, J., Baltes, F. R., Miclea, M., & Miu, A. C. (2010). Frontal EEG Activation Asymmetry
Reflects Cognitive Biases in Anxiety: Evidence from an Emotional Face Stroop Task.
Applied Psychophysiology and Biofeedback, 35, 285-292. doi: 10.1007/s10484-010-
9138-6
Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J., & Van Ijzendoorn, M. H.
(2007). Threat-related attentional bias in anxious and nonanxious individuals: a meta-
analytic study. Psychological bulletin, 133, 1. doi: 10.1037/0033-2909.133.1.1
Baddeley, A. D., & Hitch, G. (1974). Working Memory. In G. H. Bower, Psychology of
Learning & Motivation: Version 8 (pp. 47-87). New York: Academic Press.
Baddeley, A. (1992). Working memory. Science, 255, 556-559. doi: 10.1126/science.1736359
Baddeley, A. (2000). The episodic buffer: a new component of working memory? Trends in
Cognitive Neuroscience, 4, 417-423. doi: 10.1016/S1364-6613(00)01538-2
Benedek, M., Bergner, S., Könen, T., Fink, A., & Neubauer, A. C. (2011). EEG alpha
synchronization is related to top-down processing in convergent and divergent
thinking. Neuropsychologia, 49, 3505-3511. doi:
10.1016/j.neuropsychologia.2011.09.004 NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 40
Berg, E. A. (1948). A simple objective technique for measuring flexibility in thinking. The
Journal of general psychology, 39, 15-22. doi: 10.1080/00221309.1948.9918159
Bogdanov, M., & Schwabe, L. (2016). Transcranial Stimulation of the Dorsolateral Prefrontal
Cortex Prevents Stress-Induced Working Memory Deficits. The Journal of Neuroscience,
36, 1429-1437. doi: 10.1523/JNEUROSCI.3687-15.2016
Cattell, R. B., & Scheier, I. H. (1963). Handbook for the IPAT Anxiety Scale Questionnaire: Self
Analysis Form. Institute for Personality & Ability Testing.
Demakis, G. J. (2003). A meta-analytic review of the sensitivity of the Wisconsin Card Sorting
Test to frontal and lateralized frontal brain damage. Neuropsychology, 17, 255. doi:
10.1037/0894-4105.17.2.255
Derakshan, N. & Eysenck, M. W. (2009). Anxiety, Processing Efficiency, and Cognitive
Performance: New Developments from Attentional Control Theory. European
Psychologist, 14, 168-178. doi: 10.1027/1016-9040.14.2.168
Derakshan, N., Ansari, T. L., Hansard, M., Shoker, L., & Eysenck, M. W. (2009). Anxiety,
inhibition, efficiency, and effectiveness: An investigation using the antisaccade
task. Experimental psychology, 56, 48-55. doi: 10.1027/1618-3169.56.1.48
Diamond, A. (2013). Executive functions. Annual review of psychology, 64, 135-168. doi:
10.1146/annurev-psych-113011-143750
DuPont, R. L., Rice, D. P., Miller, L. S., Shiraki, S. S., Rowland, C. R., & Hardwood, H. J.
(1996). Economic costs of anxiety disorders. Anxiety, 2, 167-172. doi:
10.1002/(SICI)1522-7154(1996)2:4<167::AID-ANXI2>3.0.CO;2-L
Endler, N. S., & Kocovski, N. L. (2001). State and trait anxiety revisited. Journal of anxiety
disorders, 15, 231-245. doi: 10.1016/S0887-6185(01)00060-3 NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 41
Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a
target letter in a nonsearch task. Perception & psychophysics, 16, 143-149. doi:
10.3758/BF03203267
Evans, J. R., & Abarbanel, A. (Eds.). (1999). Introduction to Quantitative EEG and
Neurofeedback. San Diego, CA: Academic Press.
Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency
theory . Cognition and Emotion, 6, 409-434. doi: 10.1080/02699939208409696
Eysenck, M., Paynea, S., & Derakshan, N. (2005). Trait anxiety, visuospatial processing, and
working memory. Cognition and Emotion, 19, 1214-1228. doi:
10.1080/02699930500260245
Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive
performance: Attentional control theory . Emotion, 7, 336-353. doi: 10.1037/1528-
3542.7.2.336
Eysenck, M. W., & Derakshan, N. (2011). New perspectives in attentional control theory.
Personality and Individual Differences, 50, 955-960. doi: 10.1016/j.paid.2010.08.019
Fan, J., McCandliss, B. D., Sommer, T., Raz, A., & Posner, M. I. (2002). Testing the efficiency
and independence of attentional networks. Journal of cognitive neuroscience, 14, 340-
347. doi: 10.1162/089892902317361886
Fink, A., Grabner, R. H., Neuper, C., & Neubauer, A. C. (2005). EEG alpha band dissociation
with increasing task demands. Cognitive Brain Research, 24, 252-259. doi:
10.1016/j.cogbrainres.2005.02.002 NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 42
Friedman, N. P., & Miyake, A. (2004). The relations among inhibition and interference control
functions: a latent-variable analysis. Journal of experimental psychology: General, 133,
101. doi: 10.1037/0096-3445.133.1.101
Gevins, A., Smith, M. E., McEvoy, L., & Yu, D. (1997). High-resolution EEG mapping of
cortical activation related to working memory: effects of task difficulty, type of
processing, and practice. Cerebral Cortex, 7, 374-385. doi: 10.1093/cercor/7.4.374
Haaland, K. Y., Vranes, L. F., Goodwin, J. S., & Garry, P. J. (1987). Wisconsin Card Sort Test
performance in a healthy elderly population. Journal of Gerontology, 42, 345-346. doi:
10.1093/geronj/42.3.345
Hammond, D. C. (2011). What is Neurofeedback: An Update. Journal of Neurotherapy:
Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 15, 305-
336. doi: 10.1080/10874208.2011.623090
Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, G. G., & Curtiss, G. (2003). Computerised
Wisconsin Card Sort Task Version 4 (WCST). Psychological Assessment Resources, Inc.
Huizinga, M., Dolan, C. V., & Van Der Molen, M. W. (2006). Age-related change in executive
function: Developmental trends and a latent variable analysis. Neuropsychologia, 44,
2017-2036. doi: 10.1016/j.neuropsychologia.2006.01.010
Johnson, D. R. (2009). Emotional attention set-shifting and its relationship to anxiety and
emotion regulation. Emotion, 9, 681-690. doi: 10.1037/a0017095
Jurado, M. B., & Rosselli, M. (2007). The elusive nature of executive functions: A review of our
current understanding . Neuropsychology Review, 17, 213-233. doi: 10.1007/s11065-007-
9040-z NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 43
Kennedy, B. L., Schwab, J. J., Morris, R. L., & Beldia, G (2001). Assessment of State and Trait
Anxiety in Subjects with Anxiety and Depressive Disorders. Psychiatric Quarterly, 72,
263-276. doi: 10.1023/A:1010305200087
Klimesch, W. (1997). EEG-alpha rhythms and memory processes. International Journal of
Psychophysiology, 26, 319-340. doi: 10.1016/S0167-8760(97)00773-3
Klimesch, W., Doppelmayr, M., Russegger, H., Pachinger, T., & Schwaiger, J. (1998). Induced
alpha band power changes in the human EEG and attention. Neuroscience letters, 244,
73-76. doi: 10.1016/S0304-3940(98)00122-0
Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition–
timing hypothesis. Brain Research Reviews, 53, 63-88. doi:
10.1016/j.brainresrev.2006.06.003
Knyazev, G. G., Slobodskaya, H. R., & Wilson, G. D. (2002). Psychophysiological correlates of
behavioural inhibition and activation. Personality and Individual Differences, 33, 647-
660. doi: 10.1016/S0191-8869(01)00180-5
Knyazev, G. G., & Slobodskaya, H. R. (2003). Personality trait of behavioral inhibition is
associated with oscillatory systems reciprocal relationships. International Journal of
Psychophysiology, 48, 247-261. doi: 10.1016/S0167-8760(03)00072-2
Knyazev, G. G., Savostyanov, A. N., & Levin, E. A. (2004). Alpha oscillations as a correlate of
trait anxiety. International Journal of Psychophysiology, 53, 147-160. doi:
10.1016/j.ijpsycho.2004.03.001
Knyazev, G. G., Levin, E. A., & Savostyanov, A. N. (2008). A failure to stop and attention
fluctuations: An evoked oscillations study of the stop-signal paradigm. Clinical
Neurophysiology, 119, 556-567. doi: 10.1016/j.clinph.2007.11.041 NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 44
Metzger, R. L. (1976). A reliability and validity study of the state-trait anxiety inventory.
Journal of Clinical Psychology, 32, 276-278. doi: 10.1002/1097-4
Miu, A. C., Heilman, R. M., & Houser, D. (2008). Anxiety impairs decision-making:
Psychophysiological evidence from an Iowa Gambling Task. Biological Psychology, 77,
353-358. doi: 10.1016/j.biopsycho.2007.11.010.
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D.
(2000). The unity and diversity of executive functions and their contributions to complex
“frontal lobe” tasks: A latent variable analysis. Cognitive psychology, 41, 49-100. doi:
10.1006/cogp.1999.0734
Mizuhara, H., & Yamaguchi, Y. (2007). Human cortical circuits for central executive function
emerge by theta phase synchronization. NeuroImage, 36, 232-242. doi:
10.1016/j.neuroimage.2007.02.026
Moran, T. P. (2016). Anxiety and Working Memory Capacity: A Meta-Analysis and Narrative
Review. Psychological Bulletin. doi: 10.1037/bul0000051
Mueller, S. T. (2011). PEBL's Berg Card Sorting Test-64 (PBCST-64). Computer software
retrieved from http://pebl.sf.net/battery.html
Murray, N. P., & Janelle, C. M. (2003). Anxiety and performance: A visual search examination
of the processing efficiency Theory. Journal of Sports and Exercise Pscyhology, 25, 171-
187. doi: 10.1123/jsep.25.2.171
Najmi, S., Kuckertz, J. M., & Amir, N. (2012). Attentional impairment in anxiety: inefficiency in
expanding the scope of attention. Depression and Anxiety, 29, 243-249. doi:
10.1002/da.20900 NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 45
Niedermeyer, E., & Da Silva, F. L. (Eds.). (1999). Electroencephalography: Basic principles,
clinical applications, and related fields. Philadelphia, PA: Williams & Wilkins.
Piper, B. J., Li, V., Eiwaz, M. A., Kobel, Y. V., Benice, T. S., Chu, A. M., Olsen, R. H. J., Rice,
D. Z., Gray, H. M., & Mueller, S. T. (2013). Executive function on the Psychology
Experiment Building Language tests. Behavior Research Methods, 44, 110-123. doi: 10.
3758/s13428-011-0096-6
Piper, B. J., Mueller, S. T., Geerken, A. R., Dixon, K. L., Kroliczak, G., Olsen, R. H., & Miller,
J. K. (2015). Reliability and validity of neurobehavioral function on the Psychology
Experimental Building Language test battery in young adults. Peer J, 3, e1460. doi:
10.7717/peerj.1460
Psychology Software Tools, Inc. [E-Prime 2.0]. (2012). Retrieved from http://www.pstnet.com.
Quiroga, R. Q., Sakowitz, O. W., Basar, E., & Schürmann, M. (2001). Wavelet transform in the
analysis of the frequency composition of evoked potentials. Brain Research Protocols, 8,
16-24. doi: 10.1016/S1385-299X(01)00077-0
Righi, S., Mecacci, L., & Viggiano, M. P. (2009). Anxiety, cognitive self-evaluation and
performance: ERP correlates. Journal of Anxiety Disorders, 23, 1132-1138. doi:
10.1016/j.janxdis.2009.07.018
Robinson, D. L. (1999). The technical, neurological and psychological significance of ‘alpha’,
‘delta’ and ‘theta’ waves confounded in EEG evoked potentials: a study of peak latencies.
Clinical Neurophysiology, 110, 1427-1343. doi: 10.1016/S1388-2457(99)00078-4
Robinson, D. L. (2000). The technical, neurological, and psychological significance of ‘alpha’,
‘delta’ and ‘theta’ waves confounded in EEG evoked potentials: a study of peak NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 46
amplitudes. Personality and Individual Differences, 28, 673-693. doi: 10.1016/S0191-
8869(99)00130-0
Robinson, D. L. (2001). How brain arousal systems determine different temperament types and
the major dimensions of personality. Personality and Individual Differences, 31, 1233-
1259. doi: 10.1016/S0191-8869(00)00211-7
Rosa, R. R. (1983). The relationship of sleep and anxiety in anxious subjects. Biological
Psychology, 16, 119-126. doi: 10.1016/0301-0511(83)90058-3
Ruchsow, M., Grön, G., Reuter, K., Spitzer, M., Hermle, L., & Kiefer, M. (2005). Error-related
brain activity in patients with obsessive-compulsive disorder and in healthy
controls. Journal of Psychophysiology, 19, 298-304. doi: 10.1027/0269-8803.19.4.298
Sauseng, P., Klimesch, W., Freunberger, R., Pecherstorfer, T., Hanslmayr, S., & Doppelmayr,
M. (2006). Relevance of EEG alpha and theta oscillations during task switching.
Experimental Brain Research, 170, 295-301. doi: 10.1007/s00221-005-0211-y
Savostyanov, A. N., Tsaia, A. C., Lioua, M., Levinb, E. A., Leea, J., Yurganovb, A. V., &
Knyazevb, G. G. (2009). EEG-correlates of trait anxiety in the stop-signal paradigm.
Neuroscience Letters, 449, 112-116. doi: 10.1016/j.neulet.2008.10.084
Smith, P. J., Need, A. C., Cirulli, E. T., Chiba-Falek, O., & Attix, D. K. (2013). A comparison of
the Cambridge Automated Neuropsychological Test Battery (CANTAB) with
“traditional” neuropsychological testing instruments. Journal of clinical and
experimental neuropsychology, 35, 319-328. doi: 10.1080/13803395.2013.771618
Snyder, H. R., Miyake, A., & Hankin, B. L. (2015). Advancing understanding of executive
function impairments and psychopathology: bridging the gap between clinical and
cognitive approaches. Frontiers in psychology, 6. doi: 10.3389/fpsyg.2015.00328 NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 47
Spielberger, C. D. (1966). Anxiety and Behavior. New York: Academic Press.
Spielberger, C. C., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). Manual for
the State–Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press
Spielberger, C. D. (1989). State-trait anxiety inventory: a comprehensive bibliography.
Consulting Psychologists Press.
Spielberger, C. D. (2010). State‐Trait anxiety inventory. John Wiley & Sons, Inc.
Stipacek, A., Grabner, R. H., Neuper, C., Fink, A., & Neubauer, A. C. (2003). Sensitivity of
human EEG alpha band desynchronization to different working memory components and
increasing levels of memory load. Neuroscience Letters, 353, 193-196. doi:
10.1016/j.neulet.2003.09.044
Strauss, C. C., Frame, C. L., & Forehand, R. (1987). Psychosocial Impairment Associated With
Anxiety in Children. Journal of Clinical Child Psychology, 16, 235-239. doi:
10.1207/s15374424jccp1603_8
Strauss, E., Sherman, E. M., & Spreen, O. (2006). A compendium of neuropsychological tests:
Administration, norms, and commentary. Oxford University Press, USA.
Strine, T. W., Chapman, D. P., Kobau, R., & Balluz, L. (2005). Associations of self-reported
anxiety symptoms with health-related quality of life and health behaviors. Social
Psychiatry and Psychiatric Epidemiology, 40, 432-438. doi: 10.1007/s00127-005-0914-1
Sylvers, P., Lilienfield, S. O., & L., LaPrarie, J.L. (2011). Differences between trait fear and trait
anxiety: Implications for psychopathology. Clinical Psychology Review, 31, 122-137. doi
:10.1016/j.cpr.2010.08.004
Taylor, J. A. (1953). A personality scale of manifest anxiety. The Journal of abnormal and social
psychology, 48, 285. doi: 10.1037/h0056264 NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 48
Telzer, E. H., Mogg, K., Bradley, B. P., Mai, X., Ernst, M., Pine, D. S., & Monk, C. S. (2008).
Relationship between trait anxiety, prefrontal cortex, and attention bias to angry faces in
children and adolescents. Biological Psychology, 79, 216-222. doi:
10.1016/j.biopsycho.2008.05.004
Teplan, M. (2002). Fundamentals of EEG measurement. Retrieved from
http://www.edumed.org.br/cursos/neurociencia/MethodsEEGMeasurement.pdf
Thibodeau, R., Jorgensen, R. S., Kim, S. (2006). Depression, anxiety, and resting frontal EEG
asymmetry: A meta-analytic review. Journal of Abnormal Psychology, 115, 715-729. doi:
10.1037/0021-843X.115.4.715
Wright, C. A., Dobson, K. S., & Sears, C. R. (2014). Does a high working memory capacity
attenuate the negative impact of trait anxiety on attentional control? Evidence from the
antisaccade task. Journal of Cognitive Psychology, 26, 400-412. doi:
10.1080/20445911.2014.901331
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Table 1: Means of average response time and percent correct for Eriksen Flanker Task and Berg Wisconsin Card Sorting Test-64 between low and high trait anxious individuals.
Low Trait Anxiety High Trait Anxiety Task Variable N Mean SD N Mean SD Berg Wisconsin Card 15 1124.30 344.43 19 1172.22 70.71 Sorting Test-64 Average Response Time
Eriksen Flanker Task 15 504.25 27.56 19 511.54 19.54 Average Response Time
Berg Wisconsin Card 15 77.60% 17.0% 19 77.88% 14.7% Sorting Test-64 Average Percent Correct
Eriksen Flanker Task 15 97.78% 1.84% 19 97.97% 1.54% Average Percent Correct
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Table 2: Means of alpha band frequency in frontal and parietal brain regions for the resting state, Eriksen Flanker Task, and Berg Wisconsin Card Sorting Test-64 between low and high trait anxious individuals.
Low Trait Anxiety High Trait Anxiety Alpha Band Activity N Mean SD N Mean SD Resting State Frontal 14 -2.99 0.18 16 -2.85 0.36
Resting State Parietal 14 -2.88 0.29 16 -2.73 0.45
Eriksen Flanker Task 14 -2.97 0.21 16 -2.94 0.30 Frontal
Eriksen Flanker Task 14 -2.84 0.28 16 -2.77 0.36 Parietal
Berg Wisconsin Card 13 -3.07 0.16 16 -3.03 0.20 Sorting Test-64 Frontal
Berg Wisconsin Card 13 -3.05 0.17 16 -3.03 0.26 Sorting Test-64 Parietal
NEURAL CORRELATES OF ATTENTIONAL CONTROL THEORY 51
Figure 1: Eriksen Flanker Task.
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Figure 2: Berg Wisconsin Card Sorting Test-64.
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Figure 3: Line graph of alpha band frequency differences between individuals low and high in trait anxiety for the resting state and Eriksen Flanker Task [F (1, 28) = 6.045, p < 0.05).
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Figure 4: Topographical and spectral plots (Frontal/Parietal) for resting state between low and high trait anxious individuals.
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Figure 5: Topographical and spectral plots (Frontal/Parietal) for Eriksen Flanker Task between low and high trait anxious individuals.
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Figure 6: Line graph of alpha band frequency differences between frontal and parietal brain regions for the resting state and Berg Wisconsin Card Sorting Test-64 [F (1, 27) = 9.604, p < 0.01].
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Figure 7: Scatterplot of average performance accuracy for the Eriksen Flanker Task and Berg Wisconsin Card Sorting Test-64 [r (33) = 0.394, p < 0.05].
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Appendix A: State-Trait Anxiety Inventory-Form Y.
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