510351
research-article2013
JADXXX10.1177/1087054713510351Journal of Attention DisordersBen Shalom et al.
Article
A Double Dissociation Between Inattentive and Impulsive Traits, on Tasks of Visual Processing and Emotion Regulation
Journal of Attention Disorders 201X, Vol XX(X) 1–11 © 2013 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1087054713510351 jad.sagepub.com
Dorit Ben Shalom1, Ziv Ronel1, Yifat Faran2, Gal Meiri1, Lidia Gabis3, and Kimberly A. Kerns4
Abstract Objective: To dissociate between inattentive and impulsive traits common in attention deficit hyperactivity disorder (ADHD) using a non-dichotomous measurment of these traits. Method: 120 university students who completed the Conner’s adult ADHD rating scales (CAARS) were also tested on the Microgenesis task which requires visual attention and on the Cyber Cruiser task which requires emotion regulation. Results show that a measure of inattention was specifically related to a measure of effortful visual processing condition. In addition, a measure of impulsivity was specifically related to the tendency to fail in refueling one’s car on time, although this relation was opposite to the predicted direction. Furthermore, by using exploratory and confirmatory factor analyses, the CAARS’ factor structure was confirmed to be relevant to an Israeli population. Discussion: The current experiment supports the idea that visual attention may play a part in inattentive symptoms, and that emotion regulation may play a part in impulsivity symptoms. Keywords ADHD subtypes, adults, visual attention, emotion regulation, CAARS
Inattention and Impulsivity and Deficits in Visual Processing and Emotion Regulation ADHD is a neurodevelopmental disorder that typically begins in childhood and often persists into adulthood. It is characterized by inappropriate development levels of inattention and hyperactivity resulting in functional impairment in academic, family, and social life (American Psychiatric Association [APA], 1994). As a disorder that typically begins in childhood, ADHD has been found to affect between 3% and 7% of all children, a finding which was found to remain stable across many different countries (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007). Between 71% and 83% of children diagnosed with ADHD retain this diagnosis when older (Barkley, Fischer, Smallish, & Fletcher, 2002), and the prevalence of ADHD in adults is estimated to be between 2% (Simon, Czobor, Bálint, Mészáros, & Bitter, 2009) and 4% (Kessler et al., 2006). The Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; APA, 2000) divides ADHD criteria into two main groups of symptoms: Nine inattention symptoms that mainly include problems with noticeable details, problems with order and organization, and problems with following instructions. The second group is
composed of nine hyperactivity-impulsivity symptoms, which can be further divided into six hyperactivity symptoms that mainly include problems with sitting still and staying in one place, and three impulsivity symptoms that mainly include problems in waiting for one’s turn when talking or playing with others. It is important to note that the main line of studies that aim to dissociate inattention and hyperactivity/impulsivity have done so through the use of the diagnostic subtypes of the disorder. According to the DSM IV-TR, the two symptom groups manifest themselves in three clinically distinct subtypes: predominantly inattentive subtype (ADHD-PI) defined as having six or more inattention symptoms, but five or less hyperactivity/impulsivity symptoms; predominantly hyperactive/impulsive subtype (ADHD-HI) defined as having six or more hyperactivity/impulsivity symptoms, but five or less inattention symptoms; and combined subtype 1
Ben-Gurion University of the Negev, Beer Sheva, Israel Ashkelon College, Israel 3 Shiba Hospital, Tel Hashomer, Israel 4 University of Victoria, British Columbia, Canada 2
Corresponding Author: Dorit Ben Shalom, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer Sheva, Israel. Email: [email protected]
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(ADHD-C) defined as having six or more inattention and six or more hyperactivity/impulsivity symptoms. A major problem with this approach is that the impulsive/hyperactive subtype seems to be very rare, with a prevalence that drops to near zero when diagnosing any population that is older than preschool age (Barkley et al., 2002; Dulcan, 1997; Murphy, Barkley, & Bush, 2002; Riley et al., 2008; Van Voorhees, Hardy, & Kollins, 2011). This means that studies that have focused on ADHD in adolescents and adults often compare only the ADHD-PI and ADHD-C subtypes on various tasks, and have so far found no consistent differences between the two groups (Epstein et al. 2011; Paternite, Loney, & Roberts, 1996; Pritchard, Neumann, & Rucklidge, 2008; Riccio, Homack, Jarratt, & Wolfe, 2006; Solanto, Schulz, Fan, Tang, & Newcorn, 2009). In addition, studies that have tried to dissociate the ADHD-C and the ADHD-HI subtypes in children have been unsuccessful as well (Riley et al., 2008; Tucha et al., 2006). While the question of whether the different subtypes should be considered as separate remains open, there is little debate regarding the distinction between the symptom groups. Despite the limitations that have been listed, when reviewing the literature of ADHD studies, it is possible to draw a relation between problems of impulsivity and deficits in emotion regulation, and between problems of inattention, and deficits in visual selective attention. These possible relations stem from studies that have either found single dissociations (i.e., findings of only one of the subtypes to be specifically impaired compared with a control group) or specific relations between symptom measures and specific task scores.
Impulsivity and Emotion Regulation Emotional dysregulation can be defined as a failure to engage in self-regulatory actions, including self-soothing, refocusing attention, moderating the initial emotion, and organizing for coordinated action in the service of goaldirected behavior (Mitchell, Robertson, Anastopolous, Nelson-Gray, & Kollins, 2012). Building on this definition, the relation between ADHD and emotion liability has been often reported (Anastopoulos et al., 2011; Brotman et al., 2006; Martel, 2009; Retz, Stieglitz, Corbisiero, RetzJunginger, & Rösler, 2012; Skirrow, McLoughlin, Kuntsi, & Asherson, 2009). For example, a study by Sobanski and colleagues (2010) compared 1,186 children diagnosed with ADHD-C with 1,827 neurotypical children on measures of emotional liability, and found that the mean emotional liability score of ADHD-C children was over 1.5 standard deviations from that of neurotypical children. In addition, the researchers discovered that emotional liability correlated mostly with hyperactive/impulsive symptoms (Sobanski et al., 2010).
Inattention and Visual Attention Visual attention tasks often refer to one of two attentional processes: (a) The ability to focus on a relevant target stimuli while ignoring irrelevant distracting items (Mason, Humphreys, & Kent, 2003) and (b) the ability to focus on task-relevant information, while ignoring distracting contextual elements in a single stimulus (Della Libera & Chelazzi, 2006; Weissman, Mangun, & Woldorff, 2002). Several classic tasks that fit one of these definitions, as well as many variations of these tasks, have been in common use in the assessment of ADHD deficits, mainly the Stroop Task (Stroop, 1935), the Flanker task (Eriksen & Eriksen, 1974), the Visual Search Task (Treisman & Gelade, 1980), and the Navon Task (Navon, 1977). However, experiments applying these tasks to an ADHD population have often been inconclusive (Stroop: Homack & Riccio, 2004; Lansbergen, Kenemans, & van Engeland, 2007; Flanker: Johnson et al., 2008; Johnstone et al., 2009; Conjunction search: J. R. Booth et al., 2004; Mason et al., 2003; Navon: Groen, Mulder, Wijers, Minderaa, & Althaus, 2009; Helton, Head, & Russell, 2011). According to J. E. Booth, Carlson, and Tucker (2007), as well as Kooistra, Crawford, Gibbard, Kaplanab, and Fand (2011), when no distinction is made between the ADHD-PI subtype and the other subtypes, studies assessing visual attention in ADHD are predicted to often be inconclusive. As partial support to this claim, several studies that have assessed only ADHD-PI participants on measures of visual attention have found significant results: In a task employing hierarchical numbers, Song and Hakoda (2012) found participants diagnosed as ADHD-PI to have an atypical local interference. This means that while the control group showed no difference between the global and local conditions, the ADHD-PI group did show a marked increase in reaction times (RTs) and error rates in the global condition (which required ignoring the local elements of the stimuli; Song & Hakoda, 2012; see also Carlson, Benjamin, Lahey, & Neeper, 1986; Goth-Owens, Torteya, Martel, & Nigg, 2010; Weiler, Bernstein, Bellinger, & Waber, 2002).
The Current Study The main aim of the current study was to dissociate between inattention and impulsive/hyperactive symptoms. Review of the literature reveals that impulsivity is likely to be specifically related to measures of emotion regulation, while inattention is likely to be specifically related to measures of visual selective attention. A secondary aim of this study is to evaluate the factor structure of the Conner’s adult ADHD rating scales (CAARS-S; Conners et al., 1999) in an Israeli sample, as very few studies have tested the relevance of the CAARS-S to cultures other than that of the United States.
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Method Participants The study included 120 students from Ben-Gurion University of the Negev. Participants were male and female students (71 males) between 20 and 30 years old (M = 24.6), all native Hebrew speakers. Participants were recruited via ads that were posted on billboards around the university campus, as well as on various class forums. The participants were offered 20 New Israeli Shekels (NIS; around US$5.5) for participating in a 50-min experiment. All participants were asked prior to testing whether they had any history of mental illness and if they are being treated for it. Students who reported any illness other than ADHD were excluded. Out of the 120 participants, 18 had reported being diagnosed with ADHD, and 3 were taking regular medication for it. These participants were asked to refrain from taking the medication for 24 hr prior to the experiment. The experiment was approved by the local ethics committee and all participants signed an informed consent form prior to the beginning of the tests.
Procedure The CAARS-S. As mentioned above, the CAARS-S is a selfreport questionnaire designed to assess ADHD symptoms in adults. It is composed of 66 items each ranked on a 4-point Likert-type scale, and can be divided into four factors assessing cognitive/inattention, hyperactivity, emotional/impulsivity, and self concept. A Hebrew version of the questionnaire was administered as a pen-and-paper task. In this case, the participants were given a two-sided sheet of paper and a pen, and were asked to fill in as quickly and as absent of thought as possible, to what degree the different statements apply to them. To answer the main objective of this study, while the factor structure evaluation includes all factors and items, only the two relevant factors have been included as the predicted variables of a regression: the inattention and impulsivity factors. The Cyber Cruiser task. The Cyber Cruiser task was used to test the hypothesis that children with ADHD have a difficulty in switching between primary and secondary tasks, when the first is more emotionally engaging than the latter (Kerns, 2000; Kerns & Price, 2001). The task was chosen because it produces objective measures of emotional responses that have proven relevant to ADHD deficits in children. The experiment was conducted on a 15-inch IBM T60 Laptop. Participants in this experiment used their dominant hand to control a small cartoon car on a 2D road, using a joystick, to avoid cars that were driving in the opposite direction, as well as the occasional fire truck or ambulance that came from behind. Participants could also raise or
lower the speed in which the cars were driving around them (explained as “going faster”). Participants were awarded points for every second they did not hit any car (and received more points the “faster” they went), and lost 10 points for every car they hit. In addition to avoiding oncoming traffic, their secondary task was to check their fuel gauge from time to time (by pressing a key on the joystick), and to refuel their car if the gauge was in the red zone of the fuel meter (by pressing a different key on the joystick). While the main task was designed to be challenging and fun as described in the original paper, the secondary task was designed to be a chore while the fuel gauge was displayed, the car could not be controlled and points were often lost. The penalty for not fulfilling the secondary task, however, was losing all of the points that were accumulated so far, and starting the count from zero. Detaching oneself from the primary task to fulfill the secondary one is then emotionally taxing in at least two different ways: First, it requires the participant to stop an enjoyable task to perform a chore. Second, it requires the participant to ignore an immediate small aversive stimulus (i.e., losing points), to avoid a bigger more distant aversive stimulus (i.e., losing all points). Following the analyses performed by Kerns and Price (2001), only two measures are to be included as predictors in the regression—the number of times that the participant checked the fuel gauge and the number of times that the participant ran out of gas. The Microgenesis task. The current study used the Microgenesis version employed by Kimchi (1998; Experiment 1), which was designed to test the effects of global and local processing in a primed similar/different judgment task. We chose this version because it has been proven useful in identifying specific impairments in a clinical group (Behrmann et al., 2006). The experiment was conducted on a 15-inch IBM T60 laptop. Participants used their dominant hand to respond. On each trial, a fixation point appeared on the screen for 250 ms, followed by a prime that was presented for 40, 90, 190, 390 or 690 ms. Immediately after the prime, two stimuli (targets) appeared in the middle of the screen for 3,000 ms, and participants were asked to judge as quickly and as accurately as possible if the targets were the same or different. Prime and targets consisted of either 4 or 16 (few or many) squares or circles (local elements) arranged in the shape of either a square or a diamond (global configuration). This creates four relevant conditions: (a) Few-local priming, in which the prime and targets were composed of four elements, and the prime had the same elements as the targets, but a different configuration; (b) Few-global priming, in which the prime and targets were composed of four elements, and the prime had the same configuration as the targets, but different elements; (c) Many-local priming, in
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Journal of Attention Disorders XX(X) 4-element prime and 16-element targets). This was done so as not to allow any strategy to be formed. Then, as was done in the original study, we analyzed only “same” trials (trials in which the targets were identical to one another), in which the prime and targets conformed to one of the four conditions (meaning that the prime and targets had the same amount of elements, and the prime was similar to the targets in only one respect, either its local elements were the same as the targets’ or its global configuration was the same as the targets’; Behrmann et al., 2006; Kimchi, 1998).
Figure 1. Examples of the stimuli used in the Microgenesis task, as presented in Behrmann and colleagues (2006).
which the prime and targets were composed of 16 elements, and the prime had the same elements as the targets, but a different configuration; and (d) Many-global priming, in which the prime and targets were composed of 16 elements, and the prime had the same configuration as the targets, but different elements. Examples of the four conditions are shown in Figure 1. Of these four conditions, two are more perceptually demanding, that is, it has been shown to be more difficult to focus one’s attention on the global configuration of a four element shape, as well as to focus one’s attention of the local element of a 16-element shape (Kimchi, Hadad, Behrmann, & Palmer, 2005; Kimchi & Palmer, 1982, 1985). This means that the few-local priming condition and the many-local priming condition should require a longer presentation of the prime (an effect we term the “prime duration effect”) to show the priming effect. This phenomenon was previously shown by Behrmann and colleagues (2006). To measure the prime duration effect, it is possible to calculate the slope of the RTs as a function of the prime duration (RT slopes), that is, to measure how RTs become shorter as a compatible prime is presented for a longer duration. In the current study, we will calculate the RT slopes separately for each of the four conditions (i.e., few-global slope, few-local slope, many-global slope, and many-local slope). These four measures will be included as predictors in the regression analysis. It should be noted that beside the fact that the targets were always matched in the amount of elements they were composed of, every possible prime-targets combination was used (meaning that some trials included, for example, a
Experimental procedure. Each participant stepped into a well-lit room and was greeted by one of two experimenters. The participant then filled identifying information (such as age and email address), and signed an informed consent form. The participant was then seated in front of a 15-inch IBM T60 laptop. The Microgenesis task and The Cyber Cruiser were counter balanced so that one of the tasks was run, then the CAARS-S was administered, and then the other task was run. The participant was then thanked, and given payment.
Results CAARS’ Factor Structure Exploratory factor analysis. A scree test was used to determine the number of factors to be used in the rotation of the data. The scree plot showed a slight bending at the four factor points, and a large bending at the five factor points making these two points candidates for the rotation. As was done in the original study, we used a varimax rotation (a rotation which chooses the solution with maximum independence between factors), while forcing the data to divide into exactly five factors. In addition, restrictions used in the original study were used, that is, we removed items with a loading of less than 0.3 on all items, as well as items with a loading of more than 0.3 on two factors or more. However, when using this method, the fifth factor was completely removed. Next, we used a varimax rotation while forcing the data to divide into exactly four factors, and using the same restrictions that were mentioned above. The four factors explained 49.79% of the total variance, and the rotated factors accounted for 15.92%, 12.99%, 11.50%, and 9.39% of the variance, respectively. Somewhat unexpectedly, the resulting matrix, as presented in the appendix, bears great resemblance to the one found in the original paper. A simple count shows that 88% of the items that composed the four factors of the original study loaded on the same factors in the Israeli sample, and only one item (“easily frustrated”) migrated from one factor to another. The remaining differences were very slight from a theoretical view point, with only a few items removed
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Figure 2. Confirmatory factor analysis model: factors in circles, items in squares and error variances in small circles. The model was created using IBM® SPSS® AMOS 18.
(e.g., “squirm and fidget”), and with items that were added where they were predicted to belong (e.g., “having trouble organizing tasks and activities” was included in the cognitive/inattention factor). These results show that if the original study was conducted in Israel, it is possible that a very similar factor structure would have been deduced. Confirmatory factor analysis. The confirmatory factor analysis was performed using the AMOS 18 software. Only the 42 items that were included in the final factor matrix of the original paper were included in the analysis (see Christiansen et al., 2011). The program was then presented with the theoretical model (shown in Figure 2), and the fit between the model and the data was calculated. The degrees of freedom in the model were 813, with 42 observed variables and 46 unobserved variables. The items’ loading patterns were satisfactory, with all items having a loading score larger than 0.3, except one (“like to do active things”). In addition, the factors show
high internal consistency—inattention/cognitive Cronbach’s α: .878; hyperactivity Cronbach’s α: .877; impulsivity/emotion Cronbach’s α: .858; self-concept Cronbach’s α: .856. Unfortunately, goodness of fit measures did not reach the accepted norms. The Root Mean Square Error of Approximation (RMSEA) statistic equaled .085 (while the suggested cutoff is .06 or less), the Tucker-Lewis index (TLI) statistic equaled .661 (while the suggested cutoff is .95 or above), and the comparative fit index (CFI) statistic equaled .695 (while the suggested cutoff is .95 or above). These results may stem from problems in the sample size, as is discussed in length in the discussion part.
Experimental Results The CAARS-S inattention and impulsivity factors were extracted by averaging over the relevant items of each factor (see Conners et al., 1999). The skewness statistics of the inattention and impulsivity factors were positive (0.681 and 0.692,
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Table 1. Correlations Between the Predicting Variables of the Regression.
Fuel checks Times Out of gas Few local slope Few global slope Many local slope Many global slope
Fuel checks
Times out of gas
Few local slope
Few global slope
Many local slope
Many global slope
R (significance)
R (significance)
R (significance)
R (significance)
R (significance)
R (significance)
1 −.232 (.01) −.121 (.19) .005 (.96) .097 (.29) .055 (.55)
−.232 (.01) 1 .052 (.58) −.051 (.58) −.059 (.53) −.001 (.99)
−.121 (.193) .052 (.58) 1 .12 (.19) −.085 (.36) .17 (.064)
.005 (.96) −.051 (.0584) .12 (.19) 1 −.141 (.125) −.069 (.45)
.097 (.3) −.059 (.53) −.085 (.36) −.141 (.13) 1 −.079 (.4)
.055 (.55) −.001 (.99) .17 (.06) −.069 (.45) −.079 (.4) 1
respectively), showing that the distribution leans toward the lower end of the range, that is, showing that the scores of both factors tended to be smaller than 2.5 (as can be expected in a sample of neurotypical participants). Nevertheless, the lower quartiles are fairly high (0.83 and 0.6, respectively, out of 3), indicating that there is probably no floor effect. The Cyber Cruiser measures were extracted by counting the number of times the participant pressed the fuel check button, and by counting the number of times the car ran out of gas. No outlier problems arose when examining the data. The measures of The Microgenesis task were extracted by first averaging the RTs of corresponding trials, separately for each of the four conditions and for each prime duration (e.g., few-global trials, in which the prime was presented for 40 ms were grouped together, and averaged over). Then, we calculated the slope of the RTs as a function of the prime duration (e.g., all few-global means were arranged according to their prime presentation duration, and a slope score was calculated using MS Excel). The longest prime durations were discarded, because they showed an unexplained spike (the mean RT for the longest prime duration was as high as that of the shortest prime duration in all four conditions). The slope of the four remaining prime durations was then calculated separately for each of the four conditions. A correlation matrix of the six predicting variables is shown in Table 1. Often, when performing a multiple regression, a problem of multicollinearity might be considered if the correlations between measures surpass 0.7 (Slinker & Glantz, 1985). As can readily be seen, in the current regression model there is no such concern. To examine whether The Cyber Cruiser and Microgenesis measures are specifically related to the inattentive and impulsive factors, we performed two multiple regression analyses in which we predicted each of the relevant CAARS factors using all of the mentioned measures as predictors. The regression results for the inattentiveness and impulsivity regressions are presented in Tables 2 and 3, respectively. While the two regression models were not significant, R2 = .068, F(8, 109) = 1.325, p = NOS; R2 = .069 F(8, 109) = 1.338, p = NOS, out of the six predictors used, only the many-local condition’s slope was a significant predictor of the inattentiveness score, β = −.191, t(114) = −2.02, p = .04.
Table 2. Inattentiveness Predicted Using the Cyber Cruiser and Microgenesis Measures. Variable Number of gas checks Times out of gas Few local slope Few global slope Many local slope Many global slope
β
SE
p
.017 .103 .122 .008 −.191 −.009
.002 .053 .073 .070 .060 .052
.859 .281 .203 .931
research-article2013
JADXXX10.1177/1087054713510351Journal of Attention DisordersBen Shalom et al.
Article
A Double Dissociation Between Inattentive and Impulsive Traits, on Tasks of Visual Processing and Emotion Regulation
Journal of Attention Disorders 201X, Vol XX(X) 1–11 © 2013 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1087054713510351 jad.sagepub.com
Dorit Ben Shalom1, Ziv Ronel1, Yifat Faran2, Gal Meiri1, Lidia Gabis3, and Kimberly A. Kerns4
Abstract Objective: To dissociate between inattentive and impulsive traits common in attention deficit hyperactivity disorder (ADHD) using a non-dichotomous measurment of these traits. Method: 120 university students who completed the Conner’s adult ADHD rating scales (CAARS) were also tested on the Microgenesis task which requires visual attention and on the Cyber Cruiser task which requires emotion regulation. Results show that a measure of inattention was specifically related to a measure of effortful visual processing condition. In addition, a measure of impulsivity was specifically related to the tendency to fail in refueling one’s car on time, although this relation was opposite to the predicted direction. Furthermore, by using exploratory and confirmatory factor analyses, the CAARS’ factor structure was confirmed to be relevant to an Israeli population. Discussion: The current experiment supports the idea that visual attention may play a part in inattentive symptoms, and that emotion regulation may play a part in impulsivity symptoms. Keywords ADHD subtypes, adults, visual attention, emotion regulation, CAARS
Inattention and Impulsivity and Deficits in Visual Processing and Emotion Regulation ADHD is a neurodevelopmental disorder that typically begins in childhood and often persists into adulthood. It is characterized by inappropriate development levels of inattention and hyperactivity resulting in functional impairment in academic, family, and social life (American Psychiatric Association [APA], 1994). As a disorder that typically begins in childhood, ADHD has been found to affect between 3% and 7% of all children, a finding which was found to remain stable across many different countries (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007). Between 71% and 83% of children diagnosed with ADHD retain this diagnosis when older (Barkley, Fischer, Smallish, & Fletcher, 2002), and the prevalence of ADHD in adults is estimated to be between 2% (Simon, Czobor, Bálint, Mészáros, & Bitter, 2009) and 4% (Kessler et al., 2006). The Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; APA, 2000) divides ADHD criteria into two main groups of symptoms: Nine inattention symptoms that mainly include problems with noticeable details, problems with order and organization, and problems with following instructions. The second group is
composed of nine hyperactivity-impulsivity symptoms, which can be further divided into six hyperactivity symptoms that mainly include problems with sitting still and staying in one place, and three impulsivity symptoms that mainly include problems in waiting for one’s turn when talking or playing with others. It is important to note that the main line of studies that aim to dissociate inattention and hyperactivity/impulsivity have done so through the use of the diagnostic subtypes of the disorder. According to the DSM IV-TR, the two symptom groups manifest themselves in three clinically distinct subtypes: predominantly inattentive subtype (ADHD-PI) defined as having six or more inattention symptoms, but five or less hyperactivity/impulsivity symptoms; predominantly hyperactive/impulsive subtype (ADHD-HI) defined as having six or more hyperactivity/impulsivity symptoms, but five or less inattention symptoms; and combined subtype 1
Ben-Gurion University of the Negev, Beer Sheva, Israel Ashkelon College, Israel 3 Shiba Hospital, Tel Hashomer, Israel 4 University of Victoria, British Columbia, Canada 2
Corresponding Author: Dorit Ben Shalom, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer Sheva, Israel. Email: [email protected]
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(ADHD-C) defined as having six or more inattention and six or more hyperactivity/impulsivity symptoms. A major problem with this approach is that the impulsive/hyperactive subtype seems to be very rare, with a prevalence that drops to near zero when diagnosing any population that is older than preschool age (Barkley et al., 2002; Dulcan, 1997; Murphy, Barkley, & Bush, 2002; Riley et al., 2008; Van Voorhees, Hardy, & Kollins, 2011). This means that studies that have focused on ADHD in adolescents and adults often compare only the ADHD-PI and ADHD-C subtypes on various tasks, and have so far found no consistent differences between the two groups (Epstein et al. 2011; Paternite, Loney, & Roberts, 1996; Pritchard, Neumann, & Rucklidge, 2008; Riccio, Homack, Jarratt, & Wolfe, 2006; Solanto, Schulz, Fan, Tang, & Newcorn, 2009). In addition, studies that have tried to dissociate the ADHD-C and the ADHD-HI subtypes in children have been unsuccessful as well (Riley et al., 2008; Tucha et al., 2006). While the question of whether the different subtypes should be considered as separate remains open, there is little debate regarding the distinction between the symptom groups. Despite the limitations that have been listed, when reviewing the literature of ADHD studies, it is possible to draw a relation between problems of impulsivity and deficits in emotion regulation, and between problems of inattention, and deficits in visual selective attention. These possible relations stem from studies that have either found single dissociations (i.e., findings of only one of the subtypes to be specifically impaired compared with a control group) or specific relations between symptom measures and specific task scores.
Impulsivity and Emotion Regulation Emotional dysregulation can be defined as a failure to engage in self-regulatory actions, including self-soothing, refocusing attention, moderating the initial emotion, and organizing for coordinated action in the service of goaldirected behavior (Mitchell, Robertson, Anastopolous, Nelson-Gray, & Kollins, 2012). Building on this definition, the relation between ADHD and emotion liability has been often reported (Anastopoulos et al., 2011; Brotman et al., 2006; Martel, 2009; Retz, Stieglitz, Corbisiero, RetzJunginger, & Rösler, 2012; Skirrow, McLoughlin, Kuntsi, & Asherson, 2009). For example, a study by Sobanski and colleagues (2010) compared 1,186 children diagnosed with ADHD-C with 1,827 neurotypical children on measures of emotional liability, and found that the mean emotional liability score of ADHD-C children was over 1.5 standard deviations from that of neurotypical children. In addition, the researchers discovered that emotional liability correlated mostly with hyperactive/impulsive symptoms (Sobanski et al., 2010).
Inattention and Visual Attention Visual attention tasks often refer to one of two attentional processes: (a) The ability to focus on a relevant target stimuli while ignoring irrelevant distracting items (Mason, Humphreys, & Kent, 2003) and (b) the ability to focus on task-relevant information, while ignoring distracting contextual elements in a single stimulus (Della Libera & Chelazzi, 2006; Weissman, Mangun, & Woldorff, 2002). Several classic tasks that fit one of these definitions, as well as many variations of these tasks, have been in common use in the assessment of ADHD deficits, mainly the Stroop Task (Stroop, 1935), the Flanker task (Eriksen & Eriksen, 1974), the Visual Search Task (Treisman & Gelade, 1980), and the Navon Task (Navon, 1977). However, experiments applying these tasks to an ADHD population have often been inconclusive (Stroop: Homack & Riccio, 2004; Lansbergen, Kenemans, & van Engeland, 2007; Flanker: Johnson et al., 2008; Johnstone et al., 2009; Conjunction search: J. R. Booth et al., 2004; Mason et al., 2003; Navon: Groen, Mulder, Wijers, Minderaa, & Althaus, 2009; Helton, Head, & Russell, 2011). According to J. E. Booth, Carlson, and Tucker (2007), as well as Kooistra, Crawford, Gibbard, Kaplanab, and Fand (2011), when no distinction is made between the ADHD-PI subtype and the other subtypes, studies assessing visual attention in ADHD are predicted to often be inconclusive. As partial support to this claim, several studies that have assessed only ADHD-PI participants on measures of visual attention have found significant results: In a task employing hierarchical numbers, Song and Hakoda (2012) found participants diagnosed as ADHD-PI to have an atypical local interference. This means that while the control group showed no difference between the global and local conditions, the ADHD-PI group did show a marked increase in reaction times (RTs) and error rates in the global condition (which required ignoring the local elements of the stimuli; Song & Hakoda, 2012; see also Carlson, Benjamin, Lahey, & Neeper, 1986; Goth-Owens, Torteya, Martel, & Nigg, 2010; Weiler, Bernstein, Bellinger, & Waber, 2002).
The Current Study The main aim of the current study was to dissociate between inattention and impulsive/hyperactive symptoms. Review of the literature reveals that impulsivity is likely to be specifically related to measures of emotion regulation, while inattention is likely to be specifically related to measures of visual selective attention. A secondary aim of this study is to evaluate the factor structure of the Conner’s adult ADHD rating scales (CAARS-S; Conners et al., 1999) in an Israeli sample, as very few studies have tested the relevance of the CAARS-S to cultures other than that of the United States.
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Method Participants The study included 120 students from Ben-Gurion University of the Negev. Participants were male and female students (71 males) between 20 and 30 years old (M = 24.6), all native Hebrew speakers. Participants were recruited via ads that were posted on billboards around the university campus, as well as on various class forums. The participants were offered 20 New Israeli Shekels (NIS; around US$5.5) for participating in a 50-min experiment. All participants were asked prior to testing whether they had any history of mental illness and if they are being treated for it. Students who reported any illness other than ADHD were excluded. Out of the 120 participants, 18 had reported being diagnosed with ADHD, and 3 were taking regular medication for it. These participants were asked to refrain from taking the medication for 24 hr prior to the experiment. The experiment was approved by the local ethics committee and all participants signed an informed consent form prior to the beginning of the tests.
Procedure The CAARS-S. As mentioned above, the CAARS-S is a selfreport questionnaire designed to assess ADHD symptoms in adults. It is composed of 66 items each ranked on a 4-point Likert-type scale, and can be divided into four factors assessing cognitive/inattention, hyperactivity, emotional/impulsivity, and self concept. A Hebrew version of the questionnaire was administered as a pen-and-paper task. In this case, the participants were given a two-sided sheet of paper and a pen, and were asked to fill in as quickly and as absent of thought as possible, to what degree the different statements apply to them. To answer the main objective of this study, while the factor structure evaluation includes all factors and items, only the two relevant factors have been included as the predicted variables of a regression: the inattention and impulsivity factors. The Cyber Cruiser task. The Cyber Cruiser task was used to test the hypothesis that children with ADHD have a difficulty in switching between primary and secondary tasks, when the first is more emotionally engaging than the latter (Kerns, 2000; Kerns & Price, 2001). The task was chosen because it produces objective measures of emotional responses that have proven relevant to ADHD deficits in children. The experiment was conducted on a 15-inch IBM T60 Laptop. Participants in this experiment used their dominant hand to control a small cartoon car on a 2D road, using a joystick, to avoid cars that were driving in the opposite direction, as well as the occasional fire truck or ambulance that came from behind. Participants could also raise or
lower the speed in which the cars were driving around them (explained as “going faster”). Participants were awarded points for every second they did not hit any car (and received more points the “faster” they went), and lost 10 points for every car they hit. In addition to avoiding oncoming traffic, their secondary task was to check their fuel gauge from time to time (by pressing a key on the joystick), and to refuel their car if the gauge was in the red zone of the fuel meter (by pressing a different key on the joystick). While the main task was designed to be challenging and fun as described in the original paper, the secondary task was designed to be a chore while the fuel gauge was displayed, the car could not be controlled and points were often lost. The penalty for not fulfilling the secondary task, however, was losing all of the points that were accumulated so far, and starting the count from zero. Detaching oneself from the primary task to fulfill the secondary one is then emotionally taxing in at least two different ways: First, it requires the participant to stop an enjoyable task to perform a chore. Second, it requires the participant to ignore an immediate small aversive stimulus (i.e., losing points), to avoid a bigger more distant aversive stimulus (i.e., losing all points). Following the analyses performed by Kerns and Price (2001), only two measures are to be included as predictors in the regression—the number of times that the participant checked the fuel gauge and the number of times that the participant ran out of gas. The Microgenesis task. The current study used the Microgenesis version employed by Kimchi (1998; Experiment 1), which was designed to test the effects of global and local processing in a primed similar/different judgment task. We chose this version because it has been proven useful in identifying specific impairments in a clinical group (Behrmann et al., 2006). The experiment was conducted on a 15-inch IBM T60 laptop. Participants used their dominant hand to respond. On each trial, a fixation point appeared on the screen for 250 ms, followed by a prime that was presented for 40, 90, 190, 390 or 690 ms. Immediately after the prime, two stimuli (targets) appeared in the middle of the screen for 3,000 ms, and participants were asked to judge as quickly and as accurately as possible if the targets were the same or different. Prime and targets consisted of either 4 or 16 (few or many) squares or circles (local elements) arranged in the shape of either a square or a diamond (global configuration). This creates four relevant conditions: (a) Few-local priming, in which the prime and targets were composed of four elements, and the prime had the same elements as the targets, but a different configuration; (b) Few-global priming, in which the prime and targets were composed of four elements, and the prime had the same configuration as the targets, but different elements; (c) Many-local priming, in
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Journal of Attention Disorders XX(X) 4-element prime and 16-element targets). This was done so as not to allow any strategy to be formed. Then, as was done in the original study, we analyzed only “same” trials (trials in which the targets were identical to one another), in which the prime and targets conformed to one of the four conditions (meaning that the prime and targets had the same amount of elements, and the prime was similar to the targets in only one respect, either its local elements were the same as the targets’ or its global configuration was the same as the targets’; Behrmann et al., 2006; Kimchi, 1998).
Figure 1. Examples of the stimuli used in the Microgenesis task, as presented in Behrmann and colleagues (2006).
which the prime and targets were composed of 16 elements, and the prime had the same elements as the targets, but a different configuration; and (d) Many-global priming, in which the prime and targets were composed of 16 elements, and the prime had the same configuration as the targets, but different elements. Examples of the four conditions are shown in Figure 1. Of these four conditions, two are more perceptually demanding, that is, it has been shown to be more difficult to focus one’s attention on the global configuration of a four element shape, as well as to focus one’s attention of the local element of a 16-element shape (Kimchi, Hadad, Behrmann, & Palmer, 2005; Kimchi & Palmer, 1982, 1985). This means that the few-local priming condition and the many-local priming condition should require a longer presentation of the prime (an effect we term the “prime duration effect”) to show the priming effect. This phenomenon was previously shown by Behrmann and colleagues (2006). To measure the prime duration effect, it is possible to calculate the slope of the RTs as a function of the prime duration (RT slopes), that is, to measure how RTs become shorter as a compatible prime is presented for a longer duration. In the current study, we will calculate the RT slopes separately for each of the four conditions (i.e., few-global slope, few-local slope, many-global slope, and many-local slope). These four measures will be included as predictors in the regression analysis. It should be noted that beside the fact that the targets were always matched in the amount of elements they were composed of, every possible prime-targets combination was used (meaning that some trials included, for example, a
Experimental procedure. Each participant stepped into a well-lit room and was greeted by one of two experimenters. The participant then filled identifying information (such as age and email address), and signed an informed consent form. The participant was then seated in front of a 15-inch IBM T60 laptop. The Microgenesis task and The Cyber Cruiser were counter balanced so that one of the tasks was run, then the CAARS-S was administered, and then the other task was run. The participant was then thanked, and given payment.
Results CAARS’ Factor Structure Exploratory factor analysis. A scree test was used to determine the number of factors to be used in the rotation of the data. The scree plot showed a slight bending at the four factor points, and a large bending at the five factor points making these two points candidates for the rotation. As was done in the original study, we used a varimax rotation (a rotation which chooses the solution with maximum independence between factors), while forcing the data to divide into exactly five factors. In addition, restrictions used in the original study were used, that is, we removed items with a loading of less than 0.3 on all items, as well as items with a loading of more than 0.3 on two factors or more. However, when using this method, the fifth factor was completely removed. Next, we used a varimax rotation while forcing the data to divide into exactly four factors, and using the same restrictions that were mentioned above. The four factors explained 49.79% of the total variance, and the rotated factors accounted for 15.92%, 12.99%, 11.50%, and 9.39% of the variance, respectively. Somewhat unexpectedly, the resulting matrix, as presented in the appendix, bears great resemblance to the one found in the original paper. A simple count shows that 88% of the items that composed the four factors of the original study loaded on the same factors in the Israeli sample, and only one item (“easily frustrated”) migrated from one factor to another. The remaining differences were very slight from a theoretical view point, with only a few items removed
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Figure 2. Confirmatory factor analysis model: factors in circles, items in squares and error variances in small circles. The model was created using IBM® SPSS® AMOS 18.
(e.g., “squirm and fidget”), and with items that were added where they were predicted to belong (e.g., “having trouble organizing tasks and activities” was included in the cognitive/inattention factor). These results show that if the original study was conducted in Israel, it is possible that a very similar factor structure would have been deduced. Confirmatory factor analysis. The confirmatory factor analysis was performed using the AMOS 18 software. Only the 42 items that were included in the final factor matrix of the original paper were included in the analysis (see Christiansen et al., 2011). The program was then presented with the theoretical model (shown in Figure 2), and the fit between the model and the data was calculated. The degrees of freedom in the model were 813, with 42 observed variables and 46 unobserved variables. The items’ loading patterns were satisfactory, with all items having a loading score larger than 0.3, except one (“like to do active things”). In addition, the factors show
high internal consistency—inattention/cognitive Cronbach’s α: .878; hyperactivity Cronbach’s α: .877; impulsivity/emotion Cronbach’s α: .858; self-concept Cronbach’s α: .856. Unfortunately, goodness of fit measures did not reach the accepted norms. The Root Mean Square Error of Approximation (RMSEA) statistic equaled .085 (while the suggested cutoff is .06 or less), the Tucker-Lewis index (TLI) statistic equaled .661 (while the suggested cutoff is .95 or above), and the comparative fit index (CFI) statistic equaled .695 (while the suggested cutoff is .95 or above). These results may stem from problems in the sample size, as is discussed in length in the discussion part.
Experimental Results The CAARS-S inattention and impulsivity factors were extracted by averaging over the relevant items of each factor (see Conners et al., 1999). The skewness statistics of the inattention and impulsivity factors were positive (0.681 and 0.692,
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Journal of Attention Disorders XX(X)
Table 1. Correlations Between the Predicting Variables of the Regression.
Fuel checks Times Out of gas Few local slope Few global slope Many local slope Many global slope
Fuel checks
Times out of gas
Few local slope
Few global slope
Many local slope
Many global slope
R (significance)
R (significance)
R (significance)
R (significance)
R (significance)
R (significance)
1 −.232 (.01) −.121 (.19) .005 (.96) .097 (.29) .055 (.55)
−.232 (.01) 1 .052 (.58) −.051 (.58) −.059 (.53) −.001 (.99)
−.121 (.193) .052 (.58) 1 .12 (.19) −.085 (.36) .17 (.064)
.005 (.96) −.051 (.0584) .12 (.19) 1 −.141 (.125) −.069 (.45)
.097 (.3) −.059 (.53) −.085 (.36) −.141 (.13) 1 −.079 (.4)
.055 (.55) −.001 (.99) .17 (.06) −.069 (.45) −.079 (.4) 1
respectively), showing that the distribution leans toward the lower end of the range, that is, showing that the scores of both factors tended to be smaller than 2.5 (as can be expected in a sample of neurotypical participants). Nevertheless, the lower quartiles are fairly high (0.83 and 0.6, respectively, out of 3), indicating that there is probably no floor effect. The Cyber Cruiser measures were extracted by counting the number of times the participant pressed the fuel check button, and by counting the number of times the car ran out of gas. No outlier problems arose when examining the data. The measures of The Microgenesis task were extracted by first averaging the RTs of corresponding trials, separately for each of the four conditions and for each prime duration (e.g., few-global trials, in which the prime was presented for 40 ms were grouped together, and averaged over). Then, we calculated the slope of the RTs as a function of the prime duration (e.g., all few-global means were arranged according to their prime presentation duration, and a slope score was calculated using MS Excel). The longest prime durations were discarded, because they showed an unexplained spike (the mean RT for the longest prime duration was as high as that of the shortest prime duration in all four conditions). The slope of the four remaining prime durations was then calculated separately for each of the four conditions. A correlation matrix of the six predicting variables is shown in Table 1. Often, when performing a multiple regression, a problem of multicollinearity might be considered if the correlations between measures surpass 0.7 (Slinker & Glantz, 1985). As can readily be seen, in the current regression model there is no such concern. To examine whether The Cyber Cruiser and Microgenesis measures are specifically related to the inattentive and impulsive factors, we performed two multiple regression analyses in which we predicted each of the relevant CAARS factors using all of the mentioned measures as predictors. The regression results for the inattentiveness and impulsivity regressions are presented in Tables 2 and 3, respectively. While the two regression models were not significant, R2 = .068, F(8, 109) = 1.325, p = NOS; R2 = .069 F(8, 109) = 1.338, p = NOS, out of the six predictors used, only the many-local condition’s slope was a significant predictor of the inattentiveness score, β = −.191, t(114) = −2.02, p = .04.
Table 2. Inattentiveness Predicted Using the Cyber Cruiser and Microgenesis Measures. Variable Number of gas checks Times out of gas Few local slope Few global slope Many local slope Many global slope
β
SE
p
.017 .103 .122 .008 −.191 −.009
.002 .053 .073 .070 .060 .052
.859 .281 .203 .931
