Measuring Attentional Control Ability or Beliefs? Evaluation of the

J Psychopathol Behav Assess (2017) 39:742–754 DOI 10.1007/s10862-017-9617-7

Measuring Attentional Control Ability or Beliefs? Evaluation of the Factor Structure and Convergent Validity of the Attentional Control Scale Leanne Quigley 1 & Caitlin A. Wright 1 & Keith S. Dobson 1 & Christopher R. Sears 1

Published online: 21 July 2017 # Springer Science+Business Media, LLC 2017

Abstract The Attentional Control Scale (ACS; Derryberry and Reed 2002) has been widely used to measure individual differences in attentional control capacity, yet limited data exists on the factor structure and psychometric properties of the scale. Using confirmatory factor analysis with a sample of 125 undergraduate students, the present study evaluated and compared two different factor structures for the ACS reported in the literature. The convergent validity of the ACS was also explored by testing its associations with a behavioural measure of attentional control (the antisaccade task) and measures of working memory capacity. Confirmatory factor analysis supported a correlated two-factor model reflecting Bfocusing^ and Bshifting^ subscales that eliminated several underperforming items from each subscale. Contrary to predictions, there were no statistically significant correlations between the ACS and its subscales and the working memory and antisaccade task indices. In addition, it was found that the ACS and its subscales were negatively related to symptoms of anxiety and depression, whereas performance on the working memory and antisaccade tasks was unrelated to anxiety or depression. These findings suggest that the ACS may be a better measure of beliefs about attentional control capacity than ability per se, a possibility that requires further investigation.

Keywords Attentional control scale . Attentional control . Working memory . Factor analysis . Antisaccade task

* Leanne Quigley [email protected]

1

Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada

Attentional control refers to the ability to control attention voluntarily, including the abilities to focus and shift attention (Derryberry and Reed 2002). In recent years there has been increased focus on the relationship between attentional control and affective disorders (e.g., Derryberry and Reed 2002; Eysenck et al. 2007). Several studies have documented a link between reduced attentional control and symptoms of anxiety and depression (Judah et al. 2014; Ólafsson et al. 2011; Reinholdt-Dunne et al. 2013). Deficits in attentional control have also been observed in clinical samples diagnosed with generalized anxiety disorder and obsessive-compulsive disorder (Armstrong et al. 2011) and personality disorders (Claes et al. 2009). Based on these findings, it has been suggested that reduced ability to control one’s attention may increase vulnerability to several emotional disorders, conceivably through the contribution of low attentional control to the perseverative and intrusive negative thought processes that characterize these disorders (Armstrong et al. 2011). Research on the links between attentional control and psychopathology has commonly employed the Attentional Control Scale (ACS; Derryberry and Reed 2002) to measure self-reported individual differences in attentional control. The ACS was formed by combining two scales originally intended to measure the abilities to focus attention (i.e., attentional focusing) and to shift attention between stimuli or tasks (i.e., attentional shifting; Derryberry and Reed 2002; Derryberry and Rothbart 1988). Although conceived as relying on separate neural mechanisms, Derryberry and Rothbart (1988) reported a substantial correlation between self-reported attentional focusing and shifting in an undergraduate student sample (r = .54), suggesting that there may be a general attentional control ability. Attentional control is thought to be related to the functioning of the anterior attentional system (Derryberry and Reed 2002), also known as the executive attention network (Posner and Rothbart 2007). The executive attention

J Psychopathol Behav Assess (2017) 39:742–754

network involves frontal cortical systems, and particularly the anterior cingulate cortex, and carries out volitional attentional tasks (Derryberry and Reed 2002; Posner and Rothbart 2007). The executive network regulates the more involuntary or reactive attentional systems, including the alerting and orienting systems (Posner and Rothbart 2007). For example, the executive attention network is involved in the inhibition of prepotent responses, the detection of incorrect responses, and the resolution of conflict between task-relevant and taskirrelevant stimuli (Derryberry and Reed 2002; Posner and Rothbart 2007). In their description of the ACS, Derryberry and Reed (2002) proposed that the 20-item scale is composed of three correlated subfactors that reflect the abilities to a) focus attention; b) shift attention between tasks; and c) flexibly control thought. However, the factor analysis upon which this proposition was based was not published. Derryberry and Reed (2002) also cited a number of unpublished studies on the convergent validity of the ACS with behavioral tasks thought to reflect the function of the executive or anterior attentional system. Attempts to validate the ACS and explicate its underlying factor structure and psychometric properties have only recently begun to appear in the literature (e.g., Ólafsson et al. 2011. The purpose of the present study was to build and expand upon this recent research, by testing competing factor structures proposed in the literature using confirmatory factor analysis (CFA), and by evaluating the convergent validity of the ACS and its subscales through comparison to an objective behavioural measure of attentional control – the antisaccade task.

Factor Structure and Psychometric Properties of the ACS In the first published factor analysis of the ACS using an Icelandic translation of the scale, Ólafsson et al. (2011) reported a factor structure consisting of two correlated factors, which were labelled attentional Bfocusing^ (the ability to control attention in the face of distraction) and attentional Bshifting^ (the abilities to shift attention between different tasks and flexibly control the content of one’s thoughts), based on the content of the items that loaded significantly on each factor. Item 9 (BWhen concentrating I ignore feelings of hunger or thirst^) was dropped from the factor analysis because it was found to have non-significant correlations with the majority of the other scale items. Following the exploratory factor analysis (EFA), five items had factor loadings less than .40 (items 12, 14, 17, 18, and 20) and two items had loadings of similar magnitude on both factors (items 12 and 18), which implies that these items may be poorer and/or less specific indicators of their intended constructs. Ólafsson et al. conducted a CFA of this factor structure in an independent sample,

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which produced a model with reasonable fit, but in the model two items had factor loadings less than .30 (items 10 and 20) and an additional four items had factor loadings less than .40 (items 12, 14, 15, and 16). Thus, although Ólafsson et al.’s factor analysis provided important information about the twofactor structure of the ACS and the constructs represented by those factors (i.e., focusing and shifting), the presence of several underperforming items suggests that the scale can be further refined. Judah et al. (2014) examined the English version of the ACS using EFA and CFA. Their results also suggested a two-factor model with correlated factors that corresponded to the constructs of focusing and shifting. These investigators eliminated several items from each factor that did not meet the factor loading cut-off of .40 in their EFA. Accordingly, they retained a focusing subscale consisting of seven items (items 1, 2, 3, 6, 7, 8, and 12) and a shifting subscale consisting of five items (items 10, 13, 17, 18, and 19). In a second sample, Judah et al. used CFA to evaluate both the model produced by their EFA as well as the factor model reported by Ólafsson et al. (2011). Their results indicated that their model was a good fit to the data, whereas the model reported by Ólafsson et al. produced a poor fit. Examination of the content of the items eliminated from the Judah et al. model provides some insight into why these items may not have fit well. Specifically, item 4 (BMy concentration is good even if there is music in the room around me^) and item 5 (BWhen concentrating, I can focus my attention so that I become unaware of what’s going on in the room around me^), eliminated from the focusing subscale, may not be highly endorsed even among those with high self-reported focusing ability. The three items eliminated from the shifting subscale, namely, item 11 (BIt takes me a while to get really involved in a new task^, item 16 (BI have a hard time coming up with new ideas quickly^), and item 20 (BIt is hard for me to break from one way of thinking about something and look at it from another point of view^), appear to reflect flexible thought as originally proposed as a separate factor by Derryberry and Reed (2002) rather than shifting. Likewise, item 14 (BIt is easy for me to read or write while I’m also talking on the phone^) and item 15 (BI have trouble carrying on two conversations at once^) appear to reflect the ability to carry out multiple tasks simultaneously rather than shifting between tasks. Based on their results, Judah et al. suggested that researchers employing the English version of the ACS should use their proposed model. However, this model has yet to be evaluated or compared to the Ólafsson et al. model by other investigators and thus its generalisability is unknown. The convergent validity of the ACS has been evaluated by testing its associations with behavioural measures of attentional control in three studies of adult samples, and these have yielded equivocal results. Reinholdt-Dunne et al. (2009) found no correlation between the full 20-item ACS and

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attentional control as measured by the Attention Network Task (ANT; Fan et al. 2002), an index that reflects the ability to respond to a target stimulus while ignoring distractor stimuli (i.e., inhibit processing of distracting information). They did not test correlations with the separate focusing and shifting subscales, but in a follow-up study, Reinholdt-Dunne et al. (2013) tested the correlations between the ACS, as well as the focusing and shifting subscales based on the Ólafsson et al. (2011) factor model, and the attentional control index of the ANT. They found a small correlation (r = .16) between the focusing subscale and the ANT measure of attentional control. No statistically significant associations were found between performance on the ANT and either the shifting subscale or the full ACS (both including and excluding item 9). The association between the focusing subscale and the attentional control index of the ANT is consistent with the component of attentional control proposed to underlie both measures (i.e., focusing attention in the presence of distracting stimuli). On the other hand, the magnitude of the association suggests that although there is a small amount of shared variance between these measures (3%), they largely assess separate constructs. Judah et al. (2014) tested the associations between the full ACS, in addition to the shortened ACS and the focusing and shifting subscales derived from their factor analyses, and behavioural measures of working memory and attentional control. The working memory measure was a letter-number sequencing task that requires participants to repeat a mixed alpha-numeric series so that the numbers are listed first in numerical order, followed by the letters in alphabetical order. The attentional control measure was a mixed antisaccade task (Hallett 1978), in which participants are required to make either a prosaccade (eye movement toward a target stimulus) or an antisaccade (eye movement away from a target stimulus) as directed by a cue that precedes the onset of the target stimulus. Prosaccade trials are thought to involve a prepotent or reflexive saccade toward a salient peripheral cue (Hutton and Ettinger 2006), whereas antisaccade trials require inhibition of the prepotent response in order to initiate a goal-directed saccade to the location opposite the cue. Greater ability to control attention in the presence of distracting stimuli is reflected in shorter correct antisaccade latencies and fewer errors on the antisaccade task (Hutton and Ettinger 2006). In the mixed version of the antisaccade task, however, the cues are randomized throughout the trials, which places demands on attentional control for both antisaccade and prosaccade trials (Judah et al. 2014). Judah et al. (2014) reasoned that the ACS shifting subscale should correlate with performance on the working memory task because shifting between tasks requires one to hold multiple task goals in working memory, whereas the focusing subscale should correlate with the mixed antisaccade task because the task requires top-down control


over gaze behaviour (i.e., the ability to inhibit prepotent responses and make purposeful cue-directed saccades). Specifically, they hypothesized that higher focusing scores would be associated with longer saccade latencies for both the prosaccade and antisaccade trials due to intentional delay of prepotent responses. It should be noted that this hypothesis is at odds with the antisaccade task literature, in which greater attentional control ability is indexed by shorter antisaccade latencies (Derakshan et al. 2009; Hutton and Ettinger 2006). Judah et al. also hypothesized that higher focusing scores would be associated with fewer errors (i.e., better performance) on antisaccade trials. In addition, they hypothesized that the shifting subscale would correlate with better performance on switch trials (i.e., trials in which the cues signalled a switch from a prosaccade trial to an antisaccade trial or vice versa). The results provided mixed support for these hypotheses, with moderate correlations observed between the letternumber sequencing task and the full 20-item ACS (r = .27), and between the letter-number sequencing task and the ACS shifting subscale (r = .34). Contrary to their predictions, there was no significant correlation between antisaccade latencies and either the full ACS or the focusing subscale. Longer prosaccade latencies were positively correlated with the full 20-item ACS (r = .35) and the focusing subscale (r = .35), whereas antisaccade performance correlated with the short version of the ACS (r = .33) and the focusing subscale (r = .32). Consistent with their predictions, the shifting subscale was positively correlated with performance on switch trials (r = .31). The extent to which these findings support the convergent validity of the ACS is uncertain. On the one hand, the significant relationships between the ACS and its subscales with certain indices on the working memory and antisaccade tasks suggests that the scale shares some variance with behavioural measures of working memory and attentional control. On the other hand, the magnitude of these relationships (with all the correlations less than .40) indicates dissimilarity of the constructs assessed by the ACS and the behavioural tasks. Further, it is difficult to interpret the positive correlation between prosaccade latencies and the full ACS and the focusing subscale, as well as the absence of a correlation between antisaccade latencies and the ACS, given the results of other studies that have used these tasks (Derakshan et al. 2009; Hutton and Ettinger 2006).

The Present Study Few studies have examined the factor structure and validity of the ACS and they have yielded inconsistent results. Further evaluation of the ACS is therefore required to support its continued use as a measure of individual differences in attentional control. The present study used confirmatory


factor analysis to evaluate the factor models reported by Ólafsson et al. (2011) and Judah et al. (2014). A second goal was to assess the relationships between the ACS, including the specific constructs of focusing and shifting, and antisaccade and working memory tasks. The present study was designed to avoid potential limitations of previous research. Specifically, a blocked version of the antisaccade task was used, such that each block of trials consisted of only one trial type (i.e., either antisaccade trials or prosaccade trials). In the blocked version of the antisaccade task, interpretation of the task indices is straightforward because participants are not required to continuously maintain and switch between two different task sets. Thus, blocking the prosaccade and antisaccade trials creates a purer separation between the attentional processes underlying antisaccade and prosaccade performance. Prosaccade trials then involve the reflexive response and require minimal attentional control, as compared to antisaccade trials which require suppression of the dominant prepotent response and control over gaze behaviour. In addition, two measures of working memory capacity were used: modified versions of both the Operation Span Task (OSPAN; Turner and Engle 1989) and the Reading Span Task (RSPAN; Daneman and Carpenter 1980). Both tasks require participants to answer questions (i.e., solve math operations in the OSPAN task and indicate whether a sentence makes sense in the RSPAN task) while simultaneously attempting to remember a series of unrelated words or letters. Using two measures of working memory capacity ensured that our results were not affected by the limitations of any one measure. Given that the antisaccade task requires voluntary control over attention, we predicted that performance on this task would be associated with the ACS and the focusing subscale in particular, which measures the ability to focus attention and resist distraction. Specifically, we predicted negative correlations between the ACS full scale and focusing subscale and antisaccade latencies and errors, such that individuals with better self-reported attentional control and focusing ability would have shorter antisaccade latencies and make fewer errors on antisaccade trials. We did not expect any relationship between the ACS or its subscales and prosaccade latencies or errors, given the minimal demands of prosaccade trials on attentional control. Based on the notion that shifting involves the ability to hold multiple task sets in working memory (Judah et al. 2014), we predicted that performance on the working memory tasks would be positively associated with ACS shifting, which reflects taskswitching and attention flexibility. To facilitate comparisons with prior research, we also included self-report measures of anxiety and depression and expected to replicate previous findings of moderate negative correlations between the ACS, as well as its focusing and shifting subscales, and anxiety and depression symptoms.

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Method Participants The data for this study were collected as part of a larger study on the relationships among trait anxiety, working memory capacity, and attentional control (Wright et al. 2014). The final sample (see Data Preparation section) consisted of 125 undergraduate students who were enrolled in one or more psychology courses, with ages ranging from 18 to 42 (M = 21.64, SD = 3.22). The majority of the students were female (75%) and they were a mixture of junior and senior students. With respect to ethnicity, the majority of the students identified as Caucasian (66%), and smaller proportions identified as Asian (18%), East Indian (9%), Other (6%), and Black or African American (2%). To be eligible to participate, students were required to have normal or corrected-to-normal vision and speak English as a first language. The students received bonus credit in a psychology course for their participation in the study. The study was approved by the institutional research ethics board. Measures Attentional Control Scale (ACS; Derryberry and Reed 2002) The ACS is a 20-item self-report measure of attentional control. Each item is rated on a 4-point Likert scale, from 1 = almost never to 4 = always, with 11 items that are reverse-scored. The items are summed to create a total score, with higher scores indicating greater control over attention. The 20-item scale has been determined to have good internal consistency (α = .84; Ólafsson et al. 2011); however, previous studies have commonly eliminated item 9 from the total score given low observed inter-item correlations between this item and the other items of the scale (Ólafsson et al. 2011; Reinholdt-Dunne et al. 2013; Judah et al. 2014). As described previously, two previous factor analyses (Ólafsson et al. 2011; Judah et al. 2014) have supported the existence of two subscales assessing different components of attentional control, namely, focusing (e.g., BMy concentration is good even if there is music in the room around me^) and shifting (e.g., BAfter being interrupted or distracted, I can easily shift my attention back to what I was doing before^). However, the items comprising the focusing and shifting factors differed between the two studies. Items 1, 2, 3, 4, 5, 6, 7, 8, and 12 comprised the focusing factor and items 10, 11, 13, 14, 15, 16, 17, 18, 19, and 20 comprised the shifting factor in Ólafsson et al. (2011), whereas Judah et al. (2014) retained items 1, 2, 3, 6, 7, and 8 for the focusing factor and items 10, 13, 17, 18, and 19 for the shifting factor, and generated a total ACS score that was a sum of the shortened focusing and shifting subscales. To permit comparison with prior

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research, total and subscale scores were calculated according to the methods of both Ólafsson et al. and Judah et al. Cronbach’s alpha values for the ACS in the present study are shown in Table 1. State-Trait Anxiety Inventory (STAI; Speilberger et al. 1970) The STAI was administered to assess self-reported state and trait anxiety. While not directly related to the present hypotheses, its administration allowed evaluation of the relationships among trait anxiety, state anxiety, the ACS, and behavioural measures of working memory and attentional control, as well as comparison to previous research that examined these relationships (e.g., Judah et al. 2014; Ólafsson et al. 2011; Reinholdt-Dunne et al. 2013). The STAI consists of a 20-item trait anxiety scale that assesses one’s general propensity to experience anxiety, and a 20-item state anxiety scale that assesses one’s current level of anxiety. Items on each scale are rated on a 4-point Likert scale, with 10 items on each scale that are reverse-scored. Items are summed to create a total trait anxiety and total state anxiety score, with higher scores indicating greater levels of anxiety. Both the trait and state anxiety scales have demonstrated high levels of internal reliability (α > .89; Barnes et al. 2002). Factor analysis of the trait anxiety scale found that it measures both anxiety and depression and can be divided into a 7-item trait anxiety subscale and a 13item depression subscale, which possess adequate reliability (α = .78 for the anxiety subscale and α = .88 for the depression subscale; Bieling et al. 1998). Bieling et al. (1998) found that the STAI depression subscale correlated significantly higher with the depression subscale of the Depression Anxiety Stress Scale (DASS; Lovibond and Lovibond 1995) than did the STAI anxiety subscale. The reverse pattern was found for the DASS anxiety subscale, as the STAI anxiety subscale had a significantly higher correlation than the STAI depression subscale. Judah et al. (2014) and Reinholdt-Dunne et al. (2013) found differential relationships between the STAI anxiety and depression subscales and the ACS focusing and shifting subscales, with STAI anxiety showing a unique negative correlation with ACS focusing and STAI depression showing a unique negative correlation with ACS shifting. Cronbach’s alpha values for the STAI in the present study are shown in Table 1. Antisaccade Task The antisaccade task (Hallett 1978) consisted of four blocks of 80 trials for a total of 320 trials, which alternated blocks of prosaccade and antisaccade trials. Each block of 80 trials was preceded by eight practice trials to familiarize participants with the task. The order of the blocks (i.e., prosaccade block followed by antisaccade block, or vice versa) was counterbalanced across participants. Each trial began with the presentation of a fixation marker in the centre of the


computer display and two white square cues positioned horizontally 11° of visual angle to the left and right of the fixation marker. The fixation marker and cues were presented for a randomly determined duration of between 600 and 2200 ms (in 200 ms increments). Subsequently, one of the two cues flickered for 400 ms. The determination of which cue flickered on each trial was random, and the flickering cue was equally likely to appear to the left or right of the fixation cross within each 80-trial block. Participants were instructed to shift their gaze to the flickering cue in the prosaccade blocks and to the cue location opposite the flickering cue in the antisaccade blocks. Participants were instructed to shift their gaze to the appropriate location as quickly and as accurately as possible. Immediately after the offset of the flickering cue, a target (a ↑ or a ↓) appeared at one of two cue locations: at the location of the flickering cue in the prosaccade task or at the location opposite the flickering cue in the antisaccade task. A doubleheaded arrow (↕) appeared in the non-target location to prevent the participant from using an empty space to determine where the target was presented. Participants were instructed to indicate (by key press) the direction of the target arrow (Bup^ or Bdown^), as quickly and as accurately as possible. The target was displayed until the participant made a response. The target identification task was intended to motivate participants to perform well on the prosaccade and antisaccade tasks (Wright et al. 2014); however, the target identification data are not relevant to the present study hypotheses and are not analyzed or reported here. Working Memory Tasks Modified versions of the Operation Span task (OSPAN; Turner and Engle 1989) and the Reading Span task (RSPAN, Daneman and Carpenter 1980) were administered to assess working memory capacity. For the OSPAN task, participants are instructed to solve a series of math operations while they attempt to remember a set of unrelated words. The task begins when participants are shown a math problem and a to-be-recalled word (e.g., BIs (9/3) – 2 = 1? DOG^). The participant is asked to read the math problem aloud and say aloud whether the equation is correct (Byes^ or Bno^); then the participant reads the world aloud (Bdog^). After the word is read aloud, the next math problem and to-be-recalled word are displayed (e.g., BIs (8 × 4) + 2 = 34? HOLE^). This procedure is repeated until a prompt appears in the display that instructs the participant to recall the words in the order in which they were presented (i.e., Bdog^, Bhole^). For the RSPAN task, participants are presented with a coherent or nonsensical sentence and a to-be-recalled word (e.g., BWe were 50 lawns out at sea before we lost sight of land? M^). Half of the sentences are nonsensical, with the nonsensical word (e.g., Blawns^) appearing equally often at the beginning, middle, or end of the sentence. Similar to the OSPAN

−.03

−.02

.10

.18

−.46** −.44** −.50** 29.69 (5.58) .85

.00

.11

.17

−.45** −.43** −.50** 46.53 (8.08) .87

−.44** −.39** −.52** 24.34 (4.37) .76

.19

.14

.03

_ .61** .88** −.03 .08 .12

4

−.40** −.45** −.42** 18.03 (3.75) .80

.11

.05

−.04

_ .53** −.07 −.09 .09

5

−.42** −.30* −.46** 11.66 (2.61) .78

.22

.14

.01

_ −.05 .09 .11

6

−.07 .01 −.13 15.02 (7.96) .01 .04 −.06 16.72 (8.36)

−.13

−.04

.08 .01 .10 439.83 (62.16)

.02

.69**

−.06

.04

_

9

−.04

_ −.09

8

−.13

−.13

_ .44** −.06

7

.02 .02 .05 11.04 (9.49)

.51**

−.10

_

10

−.01 −.07 −.03 358.49 (61.35)

.09

_

11

−.13 −.08 −.06 3.25 (4.15)

_

12

_ .65** .75** 35.95 (11.22) .95

13

15

_ .68** _ 14.91 (3.73) 27.76 (6.52) .79 .90

14

ACS = Attentional Control Scale; ACS total = ACS full scale (19 items, excluding item 9; Ólafsson et al. 2011); ACS short = reduced ACS full scale (12 items; Judah et al. 2014); Focusing = ACS focusing subscale (9 items; Ólafsson et al.); Shifting = ACS shifting subscale (10 items; Ólafsson et al.); Focusing short = reduced ACS focusing subscale (7 items; Judah et al.); Shifting short = reduced ACS shifting subscale (5 items; Judah et al.); OSPAN = Operation Span Task; RSPAN = Reading Span Task; State anxiety = State Trait Anxiety Inventory (STAI) state anxiety scale (20 items); Trait anxiety = STAI trait anxiety subscale (7 items; Bieling et al. 1998); Depression = STAI depression subscale (13 items; Bieling et al.). *p < .01; **p < .001

−.38** −.40** −.39** 22.19 (4.57) .82

.13

.07

_ .64** .97** .57** −.07 −.09 .10

3

_ .92** .82** .92** .82** −.07 −.02 .11

2

_ .96** .91** .90** .88** .80** −.05 −.01 .12

1

Correlations, means, standard deviations, and internal reliability of the variables

1. ACS total 2. ACS short 3. Focusing 4. Shifting 5. Focusing short 6. Shifting short 7. OSPAN 8. RSPAN 9. Antisaccade latency (ms) 10. Antisaccade errors (%) 11. Prosaccade latency (ms) 12. Prosaccade errors (%) 13. State anxiety 14. Trait anxiety 15. Depression Mean (SD) Cronbach’s α

Table 1

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task, the participant reads the sentence aloud, indicates whether the sentence makes sense (Byes^ or Bno^), and then reads the letter aloud. The procedure is repeated until a recall cue appears, prompting the participant to recall the letters in the order in which they were presented within the set. For both the OSPAN and RSPAN tasks, a set of trials is defined by a number of trials (between two and five) that is followed by a recall prompt. The order of the trials within each set was random, and the different set sizes (two, three, four, or five trials) were presented randomly in order to prevent participants from knowing the number of words or letters to be recalled prior to the prompt. A total of 42 trials were presented for each of the OSPAN and RSPAN tasks (with three iterations of the two, three, four, and five set sizes for each task). For both tasks, the working memory capacity (WMC) score is the sum of recalled words (OSPAN) or letters (RSPAN) for all sets in which the entire set is recalled in the correct serial order. An accuracy of at least 85% on the processing component of the task (Byes^ / Bno^) is required to ensure that participants are performing the task as intended (see Conway et al. 2005). Participants who do not meet the 85% criterion are excluded from all analyses. The OSPAN and RSPAN tasks have been shown to have adequate internal reliability, with alphas ranging from .70 to .90 (Conway et al. 2005). The OSPAN and RSPAN tasks correlate moderately with one another (between .40 and .60; Conway et al. 2005) and both tasks predict performance on a large number of higher order cognitive tasks (see Ilkowska and Engle 2010). Equipment and Procedure Participants’ eye movements during the antisaccade task were tracked and recorded using an EyeLink 1000 eyetracking device (SR Research Ltd). The device consists of a desk-mounted camera and infrared illuminator that tracks the pupil and corneal reflections of one eye at a rate of once per millisecond (1000 Hz). Chin and forehead rests were used to position the head approximately 65 cm away from the computer display and to minimize head movements throughout the task in order to optimize tracking accuracy. Participants completed the study protocol in the laboratory individually. Following the provision of informed consent, participants completed the four blocks of prosaccade and antisaccade trials. Next, participants completed the ACS and STAI on a separate computer. The OSPAN and RSPAN tasks were then administered in a counterbalanced order across participants. Data Preparation Seven participants had one missing value on the ACS, which was replaced with the participant’s mean score. Mean substitution was also used to replace missing values for five


participants who had one missing value on the STAI trait scale and five participants who had one missing value on the STAI state scale. For the antisaccade task, a fixation was defined as an eye movement that was stable for 100 ms and was not preceded or followed by a blink. For each trial, the first saccade after the onset of the cue was examined. Trials in which the first saccade was made in the appropriate direction were classified as correct. Prosaccade trials were correct if the participant made a first saccade toward the cue, and antisaccade trials were correct if the participant made a first saccade toward the cue location opposite the cue. Trials were classified as incorrect if the participant made a first saccade in the inappropriate direction or outside of the two cue areas. If no eye movement was made the trial was categorized as missing. Mean saccade latencies for each saccade type by participant (averaged across trials and blocks) were calculated using only correct trials. Seven participants had missing data on the antisaccade task due to problems with the eye tracking system. A further seven participants were excluded because they had missing data (i.e., made no eye movements) on more than 45% of the trials. The average proportion of missing trials for the remaining participants was 6%; these missing trials were removed from the dataset. For correct trials, a non-recursive outlier identification procedure was used to identify and remove outliers (Van Selst and Jolicoeur 1994). For each participant, trials with saccade latencies 2.5 SD away from the mean of all trials, averaged across the four blocks of trials, were classified as outliers and removed from the data (1.7% of all trials). Subject outliers were defined as participants who had mean latencies or percent errors for either the antisaccade or prosaccade task that were greater than 3 SD from the sample means. Based on these criteria, eight participants were excluded from the analyses as subject outliers; two were outliers for latencies and six were outliers for errors. The criterion for subject outliers (3 SD) was more conservative than the criterion for trial outliers (2.5 SD) because more data are lost through removal of subject outliers than trial outliers. Trial and subject outliers for target identification latencies (for correct trials only) were identified and removed using the same procedure and criteria described above. Trials with latencies 2.5 SD away from the mean of all trials were removed from the data (2.1% of all target identification trials). Eight participants were removed as subject outliers because their mean latencies or percent errors for either the antisaccade or prosaccade target identification task were greater than 3 SD away from the sample means. Two participants did not meet the 85% accuracy cut-off on the OSPAN task and another five participants did not meet the 85% accuracy cut-off on the RSPAN task; these participants were removed from the dataset. One additional participant was missing RSPAN data due to computer malfunctioning and was removed. No outliers (> 3 SD away from the sample


means) on the WMC scores for either the OSPAN or RSPAN were identified. In the end, a total of 38 participants were removed from the dataset due to missing or outlying data on the antisaccade or working memory tasks, resulting in a final sample of 125 participants. All analyses were conducted on this final sample for consistency. Independent groups t-tests were conducted to test whether participants with outlying data on the antisaccade or working memory tasks differed from the remaining sample on the ACS or STAI. The participants who had missing data on the antisaccade or working memory tasks due to equipment or computer malfunction were not included in these analyses. Overall, outliers (n = 30) had slightly higher attentional control scores than non-outliers (n = 125), t(153) = 2.25, p = .026 (50.13 vs. 46.53). Categorized by type of outlier, mean total ACS scores were 43.86 (SD = 7.08) for missing data outliers on the antisaccade task (n = 7); 56.00 (SD = 1.41) for saccade latency outliers on the antisaccade task (n = 2); 50.00 (SD = 6.99) for saccade error outliers on the antisaccade task (n = 6); 51.00 (SD = 7.79) for target identification latency outliers on the antisaccade task (n = 4); 52.25 (SD = 2.36) for target identification error outliers on the antisaccade task (n = 4); and 53.14 (SD = 6.28) for the participants who did not meet the 85% accuracy cut-off on the working memory tasks (n = 7). These results indicate that participants who were excluded from the analyses did not have poorer attentional control, which could have biased the dataset to make it more difficult to detect associations between the ACS and performance on these tasks (in fact, the opposite was true, with the outliers having slightly higher ACS scores). Outliers and nonoutliers did not differ significantly on trait or state anxiety as measured by the STAI, t(153) = 1.09, p = .279 and t(153) = 0.53, p = .600, respectively.

Results Factor Structure of the ACS Confirmatory factor analysis (CFA) using Mplus version 7 (Muthén and Muthén 1998–2012) with maximum likelihood estimation was conducted to evaluate and compare the fit of the two factor models reported in Ólafsson et al. (2011) and Judah et al. (2014). Model fit was evaluated using the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI), with values greater than .90 indicating acceptable fit and values greater than .95 indicating close fit, and the Root Mean Square Error of Approximation (RMSEA), with values less than .08 indicating acceptable fit and values less than .05 indicating close fit (Kline 2011). The Akaike Information Criterion (AIC) was used to compare the two models, with the lowest value indicating better fit and higher likelihood of replicability (Kline 2011). The model chi-square is reported,

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but was not used to evaluate fit given that it is highly influenced by sample size and tends to be significant in large samples regardless of model fit (Kline 2011). The model reported by Ólafsson et al. (2011) was a correlated two factor model, with ACS items 1, 2, 3, 4, 5, 6, 7, 8, and 12 as indicators of the focusing factor and ACS items 10, 11, 13, 14, 15, 16, 17, 18, 19, and 20 as indicators of the shifting factor. Item 9 was excluded from the model given that it did not show significant correlation with 15 of the other ACS scale items and had weak correlations with the other four items in Ólafsson et al.’s analyses. Similar to Ólafsson et al., our results revealed that ACS item 9 did not correlate significantly with 16 of the other scale items and had the lowest item-total correlation of all of the ACS items (r = .18), thus supporting its exclusion from the model. The Ólafsson et al. model also allowed correlated error terms between items 4 and 5, 17 and 18, 3 and 6, and 7 and 8, and thus this error structure was specified in the tested model. The results indicated a poor fit to the data, χ2(147) = 274.34, p < .001, CFI = .82, TLI = .79, RMSEA = .08 (90% CI = .068–.098), AIC = 5031.55. Three items (items 14, 15, and 20) had factor loadings less than .40. Inspection of modification indices suggested that the largest improvement in model fit would result from allowing correlated error terms between items 1 and 4. Item 1 (BIt’s very hard for me to concentrate on a difficult task when there are noises around^) and item 4 (BMy concentration is good even if there is music in the room around me^) both pertain to ability to concentrate when there is noise, and thus common residual variance between these two items seems theoretically reasonable. Even so, modification of the model to include correlated error terms between items 1 and 4 still produced a poor fitting model, χ2(146) = 256.37, p < .001, CFI = .84, TLI = .82, RMSEA = .08 (90% CI = .062–.093), AIC = 5015.58. Modification indices indicated three additional modifications to improve model fit: allowing item 8 to load on to factor two, and allowing the error terms to correlate between items 8 and 11 and between items 15 and 16. However, allowing item 8 to load on both factors would change the structure of the scale and reduce the distinctiveness of its two subscales, and the suggested correlated error terms did not seem theoretically justified based on item content. Thus, these modifications were not implemented. The model reported by Judah et al. (2014) was also a correlated two factor model, but with several items omitted from each factor. ACS items 1, 2, 3, 6, 7, 8, and 12 were indicators of the first factor and items 10, 13, 17, 18, and 19 were indicators of the second factor. The CFA results produced a poor fit to the data, χ2(53) = 103.75, p < .001, CFI = .89, TLI = .86, RMSEA = .09 (90% CI = .062–.112), AIC = 3068.98, although the lower AIC value indicated improvement over the previous two models. Examination of modification indices suggested that the model could be improved by allowing correlated error terms between item 1 (BIt’s very hard for me to concentrate on

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a difficult task when there are noises around^) and item 6 (BWhen I am reading or studying, I am easily distracted if there are people talking in the same room^). Given the similar content of these items (i.e., the ability to concentrate in the presence of noise), it was determined that the correlation between the error terms was theoretically justified, and the model was modified appropriately. The modified model fit acceptably to the data, χ2(52) = 87.28, p = .002, CFI = .92, TLI = .90, RMSEA = .07 (90% CI = .045–.100), AIC = 3054.52, and the AIC value was the lowest of the four models tested. No other modification indices above the minimum value (10.00) were indicated. Therefore, this was used as the final model. All factor loadings were greater than .40 and the two factors (focusing and shifting) were significantly correlated, r = .66, p < .001.

measured by the OSPAN and RSPAN tasks. There were also no significant correlations between the working memory tasks and the ACS full scale or focusing subscale. The OSPAN and RSPAN scores were moderately positively correlated (r = .44, p < .001), which confirms that these measures assess similar capacities related to working memory. This correlation is consistent with the correlations of between .40 and .60 reported by Conway et al. (2005). To evaluate the possibility that current emotional state or symptomatology may confound the relationship between ACS scores and the working memory and antisaccade task indices, the correlations were duplicated, controlling successively for state anxiety, trait anxiety, and trait depression as assessed by the STAI. All correlations remained non-significant, thus confirming the bivariate results.

Reliability and Convergent Validity of the ACS

Associations with Anxiety and Depression Symptoms Bivariate correlations were calculated between the ACS and its subscales and state anxiety, trait anxiety, and trait depression as assessed by the STAI (see Table 1). ACS full scale scores, as well as focusing and shifting subscale scores, were significantly negatively correlated with STAI state anxiety, STAI trait anxiety, and STAI depression (all ps < .01), with correlations ranging from −.30 to −.52. That is, individuals who reported poorer attentional control and focusing and shifting abilities scored higher on self-reported state anxiety, trait anxiety, and depression. Table 1 also shows the bivariate correlations between the working memory and antisaccade task indices and state anxiety, trait anxiety, and trait depression. As can be seen in Table 1, no significant correlations were observed between any of the working memory tasks (i.e., OSPAN and RSPAN) or antisaccade task indices (i.e., antisaccade latency, antisaccade errors, prosaccade latency, prosaccade errors) and state anxiety, trait anxiety, and trait depression, suggesting that performance on these behavioural tasks is unrelated to symptoms of anxiety or depression.

Descriptive statistics and Cronbach’s alpha values for the measures are listed in Table 1. According to the convention of alpha values greater than .70 indicating acceptable reliability and alpha values greater than .80 indicating good reliability (DeVellis 1991), the internal consistency of the full ACS scale was good (α = .87 for the 19-item scale and α = .85 for the shortened 12item scale). Internal consistency values for the separate focusing and shifting subscales fell within the acceptable to good range (α = .82 for the 9-item focusing subscale and α = .80 for the 7-item focusing subscale; α = .76 for the 10-item shifting subscale and α = .78 for the 5-item shifting subscale). Power analyses were conducted using the G*Power 3.1 software (Faul et al. 2007) to determine the statistical power of the correlational analyses described below (i.e., correlations between the ACS and the working memory and antisaccade tasks). A moderate correlation of r = .30 was assumed, based on the findings of Judah et al. (2014). To protect against Type I errors given that multiple correlations were conducted, the alpha level was set at .01 (two-tailed). Results indicated that for a conventional power level of 80%, a sample size of 122 participants would be required to detect this correlation; thus, our sample size of 125 participants was sufficient to detect moderate correlations. Associations with Working Memory and Antisaccade Tasks Bivariate correlations were calculated between the ACS and its subscales and antisaccade latencies and errors, prosaccade latencies and errors, the OSPAN task, and the RSPAN task (see Table 1). Contrary to predictions, there were no significant correlations between ACS total scores or focusing scores and antisaccade latencies or errors. Prosaccade latencies and errors were also not significantly related to any of the ACS total or subscale scores. Also contrary to predictions, there was no significant correlation between the ACS shifting subscale (calculated according to either Ólafsson et al. 2011, or Judah et al. 2014) and working memory capacity as

Discussion The purpose of this study was to advance the limited empirical literature on the factor structure and validity of the Attentional Control Scale (ACS; Derryberry and Reed 2002). Using confirmatory factor analysis (CFA), we evaluated and compared two different factor models proposed in the literature (Judah et al. 2014; Ólafsson et al. 2011). The results of the CFA provided support for the shortened two-factor model reported by Judah et al., which eliminated several underperforming items from each of the focusing and shifting factors. To provide better understanding of the construct validity of the ACS, we tested associations between the ACS and its focusing and shifting subscales and behavioural measures of attentional


control and working memory. Our analyses did not show evidence of the predicted associations between the ACS and behavioural measures of attentional control and working memory. These results suggest that the construct measured by the ACS differs from one’s capacity to control attention as assessed by behavioural tasks. If this interpretation is correct, then this conclusion has important implications for research employing the ACS. Factor Structure of the ACS The present study is the only independent evaluation of the two different factor models determined through exploratory factor analysis (Ólafsson et al. 2011; Judah et al. 2014). Ólafsson et al. (2011) reported a factor analysis of an Icelandic translation of the ACS, with their analyses indicating that the scale is comprised of two correlated factors that assess the constructs of attentional focusing (the ability to control attention in the presence of distracting information) and attentional shifting (the abilities to shift attention between different tasks and flexibly control the content of one’s thoughts). Although the results of our CFA provided support for a correlated two-factor model incorporating focusing and shifting factors, the model reported by Ólafsson et al., which included all of the ACS items except for item 9, proved to be a poor fit to our data. Instead, the model reported by Judah et al. (2014), which eliminated several items with low factor loadings and/or substantial cross-loading, fit our data reasonably well after modifying the model to allow the residual variance of items 1 and 6 to correlate. This modification appears theoretically sound based on the item content and it did not substantively alter the factor model. All fit indices were at or above the level of acceptable model fit and all of the items had factor loadings greater than .40, supporting the stability of the factor structure. The present CFA thus corroborates and supports Judah et al.’s recommendation that the shortened focusing and shifting subscales and total scale score be used in future research using the English version of the ACS, as the retained items appear to provide more precise measurement of these constructs. As proposed in the Introduction, the eliminated items may represent infrequently endorsed items and/or items that reflect constructs other than focusing and shifting, such as flexible thought and multitasking. It is unknown whether the Ólafsson et al. model would provide a better fit to data collected with the Icelandic version of the ACS; future studies should evaluate the factor structure of the Icelandic and other translations of the ACS to determine whether the factor structure of the ACS generalizes across different languages and cultures. One interesting difference between the results of our CFA and the CFA reported by Judah et al. (2014) was the magnitude of the correlation observed between the focusing and shifting factors. For our data there was a large correlation

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(r = .66) between these two factors, whereas Judah and colleagues reported a relatively small correlation (r = .25). The magnitude of the correlation between the focusing and shifting factors is relevant to the question of whether the factor model should be conceptualized as a hierarchical model with focusing and shifting loading on to a higher order factor of attentional control, or whether the focusing and shifting factors are largely independent. A correlation between focusing and shifting factors of r = .25 indicates that the factors have only 6% shared variance. When the shared variance is this low one can argue against combining the factors to create an overall measure of attentional control (which has been common in previous research), as such disparate subscales may produce a heterogeneous, incoherent total score. In contrast, a correlation between focusing and shifting factors of r = .66, as found in our study, reflects 44% shared variance between the factors. In this case, the factors would appear to represent distinct, but related constructs that likely share variance due to a higher order construct of attentional control, in which case the use of a summed total score in addition to the separate subscale scores would seem appropriate. Additional studies that evaluate the correlation between the latent factors of focusing and shifting will be required to accurately interpret the nature of this association. Convergent Validity of the ACS Our analyses did not support convergent relationships between self-report and behavioural indices of attentional control. We found no significant correlations between either the ACS full scale or the focusing subscale and latencies or errors on the antisaccade task. Of course, this is surprising given that the antisaccade task is widely regarded in the literature as a measure of attentional control (Hutton and Ettinger 2006). That is, greater attentional control capacity reported on the ACS should be reflected in better performance on an objective experimental task that requires suppression of prepotent responses toward a salient stimulus in order to initiate goaldirected attentional behaviour. In addition, contrary to the expectation that ACS shifting should be associated with working memory capacity (given that shifting requires the ability to hold multiple task sets in working memory), no significant correlations between the shifting subscale and the working memory tasks were observed. Our statistical power analysis indicated that given the sample size of this study (n = 125), it is unlikely that the design was simply underpowered to detect these associations (i.e., the power to detect a moderate correlation of r = .30 with an alpha level of .01 exceeded 80%). In addition, recall that we found that participants who were excluded from these analysis as outliers due to latencies and errors on the antisaccade and working memory tasks (i.e., those who had poorer performance in these tasks) had slightly higher ACS scores than the rest of the sample. The small

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number of outliers forces one to interpret this outcome with caution, but it also suggests a lack of convergence between self-reported attentional control and performance on the behavioural measures. We should note that several other studies did not observe robust relationships between self-reported and behavioural measures of attentional control in adult samples. One study reported no correlation (Reinholdt-Dunne et al. 2009) and two studies reported only weak correlations between either the ACS or its subscales and various indices of attentional control (Judah et al. 2014; Reinholdt-Dunne et al. 2013). Two additional studies with child samples also concluded that there was little overlap between the ACS and behavioural measures of attentional control (Muris et al. 2008; Verstraeten et al. 2010). There are at least two possible explanations for this outcome. First, the ACS item content encompasses a broader conceptualization of attentional control than is measured by a single behavioural task, and thus the antisaccade and working memory tasks may be too narrow in their scope to provide a valid measure of attentional control capacity. Further, methodspecific variance tends to decrease the relationship between self-report and behavioural measures of the same construct (Campbell and Fiske 1959). On the other hand, even so, one would expect that the ACS and its subscales would correlate at least moderately with behavioural measures of the lower-level attentional control processes that are thought to underlie broader attentional control (i.e., antisaccade latencies). Second, and more plausible in our opinion, the ACS may reflect perceptions or beliefs about attentional control ability more so than actual levels of attentional control. What has been largely underemphasized in the literature is that the ACS shares more variance with self-report measures of anxiety and depression than with behavioural measures of attentional control. Indeed, in examinations of the validity of the ACS, the strongest and most consistent associations with the ACS and its subscales have been observed for anxiety and depression symptoms. This was true in our study and in previous research (Judah et al. 2014; Ólafsson et al. 2011; Reinholdt-Dunne et al. 2009, 2013). More specifically, individuals reporting greater anxious and depressive symptomatology endorse poorer attentional control (with correlations ranging from .30 to −.52 in this study). Researchers have documented that individuals with heightened levels of anxiety and depression tend to hold distorted and negative beliefs about themselves and their abilities (e.g., Beck et al. 1979; Chambless and Gillis 1993), which may include metacognitive distortions regarding their attentional control abilities. There is an analogous finding in some of the research on obsessive compulsive disorder (OCD), which has found OCD to be more consistently associated with distrust in perception, attention, and memory than with genuine deficits in these cognitive processes (Hermans et al. 2003; Hermans et al. 2008). Thus, while a heightened state of


anxiety has been shown to reduce efficiency of performance on tasks that require resisting distraction, especially when the distracting stimuli are threat-related (see Eysenck et al. 2007, for a review), trait anxious or depressed individuals may not have global deficits in attentional control on tasks involving neutral stimuli, outside of especially anxious states. This proposition is further strengthened by the lack of correlation between trait anxiety or depression and performance on behavioural measures of attentional control in our study, which was also the case in the studies by Reinholdt-Dunne et al. (2013) and Judah et al. (2014). It may be the case that individuals with emotional disorders have reduced confidence in their cognitive abilities, including attentional control, and although this perception may not affect their actual attentional control performance, this distrust may contribute to the symptomatology of the disorder. The results of our study clearly point to a need for additional research to foster a better understanding of the relationships between affective disorders and attentional control confidence and ability. Limitations and Considerations for Future Research Our analyses are limited by their reliance on an undergraduate student sample, comprised of mostly young women, and thus it is unclear whether our findings can be generalized to other populations. Future research should employ more diverse community samples to overcome this limitation. In addition, only the two factor models of the ACS that have been previously published in the literature were evaluated and compared using CFA. The results supported the correlated two-factor model reported by Judah et al. (2014) that eliminated several underperforming items from each factor, and this resulted in relatively small subscales, with seven items included in the focusing subscale and five items included in the shifting subscale (Judah et al. 2014). Although the shortened full ACS scale and subscales demonstrated adequate to good internal reliability, there may be other viable models that include more of the ACS items, and future research should examine this possibility. Future studies should also aim to include a wider range of behavioural measures of attentional control, including those that assess a broader conceptualization of attentional control in addition to more narrowly defined measures such as the antisaccade task. Inclusion of a range of measures would provide a more comprehensive evaluation of the construct validity of the ACS. Finally, the STAI anxiety and depression subscales were used for consistency and to permit comparison with prior studies that have used these subscales to examine relationships among anxiety, depression, and ACS focusing and shifting (Judah et al. 2014; Reinholdt-Dunne et al. 2013). Although there is some evidence for the structure and validity of these subscales (Bieling et al. 1998), the STAI was not specifically designed to distinguish between anxiety and depression, and it is not widely used for this purpose in the


literature. Future studies should employ other psychometrically established measures of anxiety and depression (e.g., Beck Anxiety Inventory, Beck and Steer 1990; Beck Depression Inventory-II, Beck et al. 1996).

Conclusions Our results have important implications for research employing the ACS. Despite widespread use of the ACS, few studies have examined its factor structure and validity. Our analyses provide the first independent confirmation of the factor model put forth by Judah et al. (2014) and thus we recommend that researchers use the shortened versions of the focusing and shifting subscales as specified by this model. We did not find significant associations between the ACS and the antisaccade task, a widely used behavioural measure of attentional control, nor between the shifting subscale and working memory tasks, despite the ostensible similarity in the abilities underlying shifting and working memory. Given the evidence that the ACS correlates more strongly with measures of anxiety and depression than with performance-based measures of attentional control (Judah et al. 2014; Reinholdt-Dunne et al. 2009, 2013), and given that anxiety and depression are characterized by distorted negative evaluations of one’s abilities (Beck et al. 1979; Chambless and Gillis 1993), it is very possible that the ACS captures beliefs about attentional control rather than attentional control ability per se. If this interpretation is corroborated by additional research, then the ACS may be most useful as a measure of confidence in one’s capacity for attentional control in studies that examine the relationship between affective disorders and metacognitive beliefs.

Compliance with ethical standards Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Funding This research was supported by a Vanier Canada Graduate Scholarship from the Social Sciences and Humanities Research Council (SSHRC) and a graduate scholarship from Alberta Innovates-Health Solutions (AIHS) to L. Quigley, a graduate scholarship from SSHRC to C. A. Wright, and grants from the Natural Sciences and Engineering Research Council (NSERC) and AIHS to C. R. Sears. Conflict of Interest Leanne Quigley, Caitlin A. Wright, Keith S. Dobson, and Christopher R. Sears declare that there is no conflict of interest. Experiment Participants For all subjects, written informed consent was obtained.

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