Dimensions of Impulsivity in Healthy People,Patients with Borderline Personality Disorder,and Patients with Attention-Deficit/Hyperactivity Disorder

Abstract

Objective: Impulsivity, observed in patients with various psychiatric disorders, is a heterogeneous construct with different behavioral manifestations. Through confirmatory factor analysis (CFA), this study tests hypotheses about relationships between dimensions of impulsivity measured using personality questionnaires and behavioral tests. Method: The study included 200 healthy people, 40 patients with borderline personality disorder, and 26 patients with attention-deficit/hyperactivity disorder (ADHD) who underwent a comprehensive impulsivity test battery including the Barratt Impulsiveness Scale (BIS), UPPS-P Impulsive Behavior Scale, a Go-NoGo task, a stop-signal task, and a delay discounting task. Results: A CFA model comprising three self-reported and three behavioral latent variables reached a good fit. Both patient groups scored higher in the self-reported dimensions and impulsive choice; only the ADHD patients displayed impaired waiting and stopping impulsivity. Conclusions: Using the developed CFA model, it is possible to describe relations between impulsivity dimensions and show different impulsivity patterns in patient populations.

Keywords

impulsivity confirmatory factor analysis latent factor model borderline personality disorder ADHD

Introduction

Impulsivity can be broadly defined as the degree to which one tends to act on the spur of the moment without considering consequences. Impulsive behaviors have been theorized to include substance abuse, gambling, dangerous sexual behavior, risky driving, binge eating, aggression, self-harm, and suicidality (Barteček, Hořínková, Linhartová, & Kašpárek, 2019; Cyders, Combs, Fried, Zapolski, & Smith, 2009). Heightened impulsivity has been identified as a diagnostic feature of borderline personality disorder (BPD) and attention-deficit/hyperactivity disorder (ADHD) by diagnostic manuals (American Psychiatric Association [APA], 2013; World Health Organization, 2011), but it has also been observed in patients with many other psychiatric conditions, including addiction (Smith, Mattick, Jamadar, & Iredale, 2014), bipolar disorder (Najt et al., 2007), and schizophrenia (Ouzir, 2013).

Although existing literature clearly indicates that there is no global impulsivity factor (MacKillop et al., 2014; MacKillop et al., 2016; Stahl et al., 2014), there remains uncertainty about what the dimensions of impulsivity are and how they relate to each other. Existing classifications of impulsivity dimensions have followed one of two broad approaches. Under the more traditional approach, impulsivity is considered a personality trait, extremely high levels of which can have pathological consequences. Dimensions of impulsivity are then measured by self-report questionnaires and identified through factor analysis. According to the other view, impulsivity results from the disruption of specific cognitive abilities, such as the ability to postpone or interrupt actions. Behavioral tests of various impulsivity-related cognitive abilities have been developed to measure separate impulsivity dimensions. However, there has been little empirical investigation into whether these dimensions are truly independent (as in MacKillop et al., 2016; Stahl et al., 2014). Furthermore, investigations of the relationships between personality-based and behavioral dimensions have been rare (e.g., Cyders & Coskunpinar, 2011; MacKillop et al., 2016). Thus, our aim, following a review of proposed impulsivity dimensions, is to conduct a confirmatory factor analysis (CFA), examining relationships between personality-based and behavioral dimensions of impulsivity. After specifying a well-fitting CFA model, we can examine (a) whether people with impulsivity-related psychiatric conditions (BPD and ADHD in the current study) exhibit higher levels of certain dimensions, and (b) whether the two patient groups differ from each other in their impulsivity profiles.

Personality Models of Impulsivity

Of the theories according to which impulsivity is a personality trait (for historical reviews, see Evenden, 1999; Parker & Bagby, 1997), the most dominant models are represented by the Barratt Scale (BIS, Barratt, 1959) and the UPPS-P Scale (Cyders & Smith, 2007; Whiteside & Lynam, 2001). The eleventh version of BIS (Patton, Stanford, & Barratt, 1995) has been in use for more than 20 years. Through factor analysis, the scale’s authors found that the items reflected three main dimensions: attentional, motor, and nonplanning impulsivity. However, most subsequent studies have failed to replicate this three-factor structure and to uncover one global factor (e.g., Reise, Moore, Sabb, Brown, & London, 2013; Steinberg, Sharp, Stanford, & Tharp, 2013; Vasconcelos, Malloy-Diniz, & Correa, 2012), although it is common for researchers and clinicians to calculate the total BIS score. Regardless of its limitations, the BIS remains widely used and has been found to differentiate between healthy people and patients with impulsivity-related disorders (Stanford et al., 2009), including BPD (e.g., Barker et al., 2015; Maraz et al., 2016) and ADHD (e.g., Chamberlain, Ioannidis, Leppink, & Niaz, 2017; Roberts, Milich, & Fillmore, 2016).

The UPPS-P personality model of impulsivity (Cyders & Smith, 2007; Whiteside & Lynam, 2001) was developed in an effort to integrate a number of proposed personality models. Whiteside and Lynam (2001) developed a scale that included 20 subscales from nine existing questionnaires and 14 original items. Factor analysis revealed four impulsivity factors: urgency (U), (lack of) perseverance (P), (lack of) premeditation (P), and sensation seeking (S). Subsequently, Cyders and Smith (2007) developed a revised scale, the UPPS-P, which contained an additional urgency subscale gauging impulsive behavior under the influence of positive emotions. A CFA of the UPPS-P conducted by these authors revealed a hierarchical structure in which negative and positive urgency reflected a higher-order “urgency” factor, while lack of perseverance and premeditation reflected higher-order “deficits in conscientiousness.” Sensation seeking was found to be a separate factor. In introducing the notion of urgency—a tendency to act impulsively under influence of emotions—the UPPS-P took into account emotional impulsivity, which had been previously overlooked. Numerous studies showed each of the lower order factors to be associated with various forms of psychopathology (Berg, Latzman, Bliwise, & Lilienfeld, 2015). Compared to healthy people, heightened scores on all UPPS-P subscales except sensation seeking were observed among both BPD patients (e.g., Bøen et al., 2015; Paret, Hoesterey, Kleindienst, & Schmahl, 2016) and ADHD patients (e.g., Lopez, Dauvilliers, Jaussent, Billieux, & Bayard, 2015; Pedersen et al., 2016).

Behavioral Models of Impulsivity

Behavioral models of impulsivity can be divided into two broad subtypes (i.e., dimensions). Each behavioral impulsivity subtype can be assessed through several behavioral tests, summarized in Figure 1. The figure also summarizes other labels used for these dimensions in the literature.

Figure 1.

Overview of the behavioral impulsivity dimensions based on existing literature.

The first subtype, which we will call “impulsive action” (Winstanley, Eagle, & Robbins, 2006), involves impaired inhibition with respect to withholding premature actions or stopping ongoing actions. In effect, impulsive action can be subdivided into “waiting impulsivity” (difficulty inhibiting or postponing unwanted or premature actions) and “stopping impulsivity” (difficulty interrupting already ongoing actions; Robinson et al., 2009). The most frequently used test for measuring waiting impulsivity is the Go-NoGo task (GNG; e.g., Smith et al., 2014). BPD patients usually show no impairment in GNG (Hagenhoff et al., 2013; van Eijk et al., 2015), but their performance has been shown to decrease under the influence of emotions (Krause-Utz et al., 2016; Turner, Sebastian, & Tüscher, 2017). The examination of ADHD patients’ performance in the GNG has produced mixed results (Pani et al., 2013; Rubia, Smith, & Taylor, 2007; Schachar et al., 2007), possibly because of the influence of task parameters on GNG performance (Metin, Roeyers, Wiersema, Van Der Meere, & Sonuga-Barke, 2012; Nieuwenhuis, Yeung, van den Wildenberg, & Ridderinkhof, 2003). Stopping impulsivity is typically measured using the stop-signal task (SST; Verbruggen & Logan, 2009). BPD patients usually manifest no deficits in SST (Barker et al., 2015; Jacob et al., 2010), whereas ADHD patients usually do show impairment (Lampe et al., 2007; Pani et al., 2013).

The second impulsivity subtype, which we will call “impulsive choice” (Winstanley et al., 2006), involves impaired decision making. People with high levels of impulsive choice prefer immediate gains or rewards at the expense of future goals. For these individuals, the value of a reward drops rapidly if they must wait for it. The most frequently used test for measuring impulsive choice is the delay discounting task (DDT; Ainslie, 1975). Both BPD and ADHD patients continuously show higher levels of discounting, implying more pronounced impulsive choice (Barker et al., 2015; Patros et al., 2016; Turner et al., 2017).

Relationships Between Impulsivity Subtypes

The impulsivity dimensions of the BIS and the UPPS-P have generally been found to be related (e.g., Lozano, 2015; MacKillop et al., 2016). Within the UPPS-P itself, studies looking at relationships between higher-order factors (Cyders & Smith, 2007; Linhartová et al., 2017) have found moderate-to-high correlations between deficits in conscientiousness and urgency. Sensation seeking has, meanwhile, been found to have moderate-to-no relationship with the two other factors.

Regarding the behavioral dimensions, there is evidence from neuroimaging studies that waiting and stopping impulsivity are two different processes (Eagle, Bari, & Robbins, 2008; Swick, Ashley, & Turken, 2011). In line with this finding, patients with the same psychiatric disorder have been found to perform differently in GNG than in SST (Pani et al., 2013; Smith et al., 2014). Despite these findings, most studies either do not report correlations between GNG and SST or model the tests as measures of one impulsive action dimension (MacKillop et al., 2016; Stahl et al., 2014), so little is known about the strength of the relationship between waiting and stopping impulsivity. Impulsive action, modeled as a latent variable reflecting waiting and stopping impulsivity, has, meanwhile, been repeatedly found to be independent from impulsive choice (MacKillop et al., 2016; Stahl et al., 2014).

The relationship between different personality and behavioral dimensions has received little research attention. A recent study using CFA found a low but statistically significant association between a latent factor defined by the UPPS-P and the BIS subscales and impulsive choice, as well as impulsive action (MacKillop et al., 2016). However, in collapsing all personality dimensions into one latent factor and doing the same for waiting and stopping impulsivity, this approach prevents us from drawing hypotheses about relationships between potentially distinct personality and behavioral impulsivity dimensions. Earlier, Cyders and Coskunpinar (2011) performed a meta-analysis of 28 studies presenting correlations between different impulsivity personality and behavioral measures. The authors found a significant but very small general association between personality and behavioral measures (r = 0.10). Specific results are difficult to interpret because the authors collapsed different measures into predefined categories which were consequently compared. Generally, the degree of overlap between personality and behavioral impulsivity measures found in existing studies is very low.

Current Study: Aims and Hypotheses

Our overarching hypothesis is that the BIS, deficits in conscientiousness, urgency, sensation seeking, waiting impulsivity (measured by GNG), stopping impulsivity (measured by SST), and impulsive choice (measured by DDT) constitute separate dimensions of impulsivity either in that they have only weak-to-moderate interrelationships as latent variables within the CFA model, or in that, as latent variables, they differ in clinical profile—that is, in how they differentiate among healthy people, patients with BPD, and patients with ADHD. We do not incorporate a higher-order impulsive action latent variable composed of waiting and stopping impulsivity, so that both dimensions can be interpreted separately. Similarly, we do not hypothesize any higher-order latent variables defined by two or more personality latent variables.

With respect to the interrelationships of dimensions, one hypothesis is that deficits in conscientiousness, urgency, and the BIS will be intercorrelated or have similar clinical profiles, while sensation seeking will be independent of these factors in terms of correlations and clinical profiles. We also hypothesize weak-to-no intercorrelations between the personality dimensions (BIS, deficits in conscientiousness, urgency, and sensation seeking) and the behavioral dimensions (waiting impulsivity, stopping impulsivity and impulsive choice). There is insufficient data to make a hypothesis about the relationship between waiting and stopping impulsivity. However, there is a basis for hypothesizing that both BPD and ADHD patients will differ from healthy controls in impulsive choice and on all self-report dimensions except sensation seeking. We further hypothesize that ADHD patients differ from healthy controls in stopping impulsivity; considering the inconsistencies in the literature, we make no hypothesis about the difference between ADHD patients and healthy controls in waiting impulsivity.

Method

This study was reviewed and approved by the Institutional Ethical Committee. Before the research was carried out, the study was explained thoroughly to the subjects who then signed informed consent forms. The research was carried out in accordance with APA ethical standards.

Participants

The participant pool consisted of 200 healthy people (54% women, age: M = 23.04, SD = 12.1), 40 patients with BPD (88% women, age: M = 23.40, SD = 4.72), and 26 patients with ADHD (31% women, age: M = 26.15, SD = 12.77). The ADHD diagnosis was confirmed by two board-certified psychiatrists according to Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; APA, 2013) and the Diagnostic Interview for ADHD in adults (DIVA; Kooij, 2012). The BPD diagnosis was confirmed by two board-certified psychiatrists according to DSM-V criteria and by a psychologist using the Diagnostic Interview for Borderlines, revised (DIB-R; Zanarini, Gunderson, Frankenburg, & Chauncey, 1989). Comorbid psychotic or affective disorder and addiction were the exclusion criteria. The Mini-International Neuropsychiatric Interview (MINI; Sheehan et al., 1998) was conducted with healthy volunteers to confirm the absence of psychiatric symptoms. 40 people (88% women, age: M = 24.25, SD = 6.88) from the healthy sample were matched to BPD patients on age, gender, and education; a further 26 (31% of women, age: 24.46, SD = 7.94) were similarly matched to ADHD patients.

Procedure

Under the supervision of a research assistant, participants completed a single 60-min test battery comprising two questionnaires and three computerized behavioral tests. The tests were presented in a fixed order.

Measures

Validated translations of the Barratt Scale (BIS, 11th version, 30 items, Patton et al., 1995) and the UPPS-P scale (Cyders & Smith, 2007) were used (Linhartová et al., 2017). The UPPS-P consisted of five subscales: lack of premeditation (11 items), lack of perseverance (10 items), sensation seeking (12 items), negative urgency (12 items), and positive urgency (14 items). Behavioral measures were developed in E-Prime 2.0. Waiting impulsivity was measured by a GNG task, stopping impulsivity by an SST, and impulsive choice by a DDT.

The GNG used white letters A and B on a black background as stimuli. Participants were asked to press the space key on the computer keyboard whenever a Go stimulus (the letter “A”) appeared and to suppress that action whenever a NoGo stimulus (the letter “B”) appeared. Go stimuli were highly prevalent (83%) to make action suppression less automatic and thus encourage more NoGo commissions (Nieuwenhuis et al., 2003). Stimuli duration was 0.4 s; each stimulus was preceded by a fixation cross with variable duration between 1.1 s and 2.6 s. The whole task included four blocks with 48 trials in each block. Two accuracy-based and two reaction-time-based indices were derived from the GNG, namely NoGo commissions percentage (percentage of NoGo trials erroneously followed by a key press), Go omissions percentage (percentage of Go trials erroneously followed by no key press), NoGo reaction time (average reaction time on erroneous NoGo trials) and Go reaction time (average reaction time on correct Go trials).

The SST used white left and right arrows on a black background as the Go stimuli. Participants were asked to press the left or right arrow key when the respective Go stimulus appeared, except when the Go stimulus was followed by a stop signal: a change in the arrow color from white to red. The time between the Go stimulus and the appearance of the stop signal—the stop-signal delay (SSD)—was initially set to 200 ms and decreased by 45 ms whenever action suppression was successful, while increasing by 45 ms after unsuccessful Stop trials (Verbruggen & Logan, 2009). Stop-signal frequency was 25%; each trial ended either after the participant’s response or, in the absence of a response, 1 s after the appearance of the stimulus. Each trial was preceded by a fixation cross with a variable duration between 1.1 s and 2.6 s. The whole task included four blocks with 48 trials in each block. Three accuracy-based indices were derived from the SST, namely Stop commissions percentage (percentage of Stop trials erroneously followed by a key press), Go commissions percentage (percentage of left [right] arrow Go trials erroneously followed by right [left] arrow key press), Go omissions percentage (percentage of Go trials erroneously followed by no key press). The two reaction-time-based indices derived from the SST include Go reaction time (average reaction time on correct Go trials) and stop-signal reaction time (SSRT) which was estimated by subtracting the average SSD from the average Go reaction time. The SSRT provides an indication of the average time required for successful stopping. Longer SSRTs indicate greater difficulty interrupting actions.

In the DDT, participants were asked to answer a set of questions requiring a choice between a smaller but immediate reward (IR) and a higher but delayed reward (DR; e.g., Would you rather receive 510 CZK now, or 990 CZK in a month?). For each combination of a DR amount and a delay period (D), different immediate reward amounts were displayed until an “indifference point” (IP) could be determined for the combination. The IP is the immediate reward amount that has the same subjective value as the higher delayed reward. Questions regarding different IRs and DRs were presented in random order, with the IR being selected according to the procedure described by Richards, Zhang, Mitchell, and de Wit (1999). The Ds and DRs were determined based on pilot studies. Chosen Ds were 1 day, 1 week, 1 month, 3 months, and 6 months. Two DRs were chosen: a smaller amount of 990 CZK (approx. 40 EUR), and a higher amount approximately equivalent to the median monthly salary in the Czech Republic (24.900 CZK, approx. 980 EUR). The IR amounts varied in 20 CZK steps in questions about the smaller DR and in 500 CZK steps in questions about the higher DR.

Two indices (k parameter and Area Under the Curve) were derived for each DR, resulting into four DDT indices. Based on the IPs for each DR, each participant’s “discounting parameter,” k, was estimated using the following hyperbolic function, across multiple Ds on the x-axis and a fixed DR amount: DR = IR / (1 + k × D). Higher values of k indicate steeper (i.e., quicker) discounting with increased delay, and, correspondingly, higher levels of impulsive choice (Mazur, 1987). The Area Under the Curve (AUC) in the DDT graph, was derived by connecting the IPs for multiple Ds (Myerson, Green, & Warusawitharana, 2001). The higher the AUC, the slower the discounting, and the higher the impulsive choice. The advantage of the AUC is in its independence from the theoretical discounting function used to derive k.

Statistical Analysis

The CFA model (presented in Figure 2) was estimated using the lavaan package (Rosseel, 2012) in R (v. 3.5.1). Robust maximum likelihood (RML) based on a full-information matrix likelihood (FIML) was used as the estimation method. The latent variables were (a) the BIS, (b) deficits in conscientiousness (lack of premeditation and lack of perseverance subscales from the UPPS-P), (c) urgency (negative urgency and positive urgency subscales from the UPPS-P), (d) sensation seeking (from the UPPS-P), (e) waiting impulsivity as measured by GNG, (f) stopping impulsivity as measured by SST, and (g) impulsive choice as measured by DDT. All correlations between latent variables were allowed.

Figure 2.

Estimated latent factor model of impulsivity dimensions.

The observed variables defining the personality dimensions (1-4) were determined based on preliminary CFAs, conducted separately for the BIS and the UPPS-P. As expected, for the BIS, we failed to find an adequate model fit for the proposed three-factor structure¹ or unidimensional solution.² However, because of its ability to discriminate between healthy people and some clinical populations, the scale was retained in the main model and was represented as a single latent factor, defined by two parcels (randomly selected half of the items in each parcel) with regression parameters constrained to be equal (Little, Cunningham, Shahar, & Widaman, 2002). In the preliminary CFA of UPPS-P items, in line with earlier findings we found a well-fitting model comprising three latent variables: deficits in conscientiousness (defined by lack of perseverance and lack of premeditation subscales), urgency (defined by the negative urgency and positive urgency subscales) and sensation seeking (defined by three item parcels).³ Urgency correlated strongly (r = 0.71) with deficits in conscientiousness, while sensation seeking was independent of the other higher-order latent factors.

The observed variables defining the behavioral dimensions (5-7) are described in Method section. There are two types of indices from the GNG and SST: accuracy-based and reaction-time-based. Similarly to Stahl et al. (2014), we argue that task performance is characterized by a combination of these indices, not only by a single predefined variable. To reduce model complexity (and avoid a Heywood case), an equality constraint was placed on the regression parameters of the relationship between the waiting impulsivity/GNG latent variable and NoGo reaction time and Go reaction time. Furthermore, residual correlations between SSRT and Go reaction time in SST and between NoGo reaction time and Go reaction time in GNG were allowed, so that individual differences in reaction times were not overestimated. After model-fitting to the full healthy sample, latent scores for BPD patients, ADHD patients, and matched healthy controls were estimated and compared between the matched groups.

Results

The correlation matrix of all observed variables is provided in Supplemental material. The model described in the previous section showed a not-quite-sufficient robust fit, χ² = 413.634; df = 190; χ²/df = 2.177; comparative fit index (CFI) = 0.867, root mean square error approximation (RMSEA) = 0.075; standardized root mean square residual (SRMR) = 0.079. Two changes were made to the initial model. First, sensation seeking was removed from the model, as it was (a) completely independent of all the other latent variables, and (b) in exploratory analyses, no different across patients and matched controls. Second, NoGo commissions—an observed variable loading on the waiting impulsivity/GNG latent variable—was removed from the model, as it had too low associated regression coefficient (0.22). The updated model is presented in Figure 2 and obtained a good robust fit (χ² = 226.204; df = 122; χ²/df = 1.854; CFI = 0.921, RMSEA = 0.065; SRMR = 0.067). Following model specifications among healthy participants, latent variable scores were estimated for BPD patients, ADHD patients, and matched controls. The results of t tests comparing matched groups are presented in Table 1.

Table 1.

Mean Differences in Latent Variable Scores Between Patients With BPD (N = 40) and Their Matched Healthy Controls (N = 40), and Between Patients With ADHD (N = 26) and Their Matched Healthy Controls (N = 26).

Latent variable	BPD			ADHD
Latent variable	t(78)	p	Cohen’s d	t(50)	p	Cohen’s d
Barratt Scale	−8.00	< .001	−1.79	−6.42	< .001	−1.78
Deficits in conscientiousness	−6.68	< .001	−1.49	−4.17	< .001	−1.16
Urgency	−8.55	< .001	−1.91	−6.49	< .001	−1.80
Waiting impulsivity/GNG	−0.71	.479	−0.16	−2.78	.008	−0.77
Stopping impulsivity/SST	−1.16	.250	−0.26	−2.37	.022	−0.66
Impulsive choice/DDT	4.99	< .001	1.12	3.72	< .001	1.03

Note. Group assignment: 0 = controls, 1 = patients; negative differences indicate higher values in patients than in healthy controls in all variables. Higher values in patients than in healthy controls indicate higher impulsivity in patients in all variables except Impulsive choice/DDT. In Impulsive choice/DDT, lower values in patients than in healthy controls indicate higher impulsivity in patients because the latent variable’s valence is driven by AUC observed variables (see Method for AUC index computation). BPD = borderline personality disorder; GNG = Go-NoGo task; SST = stop-signal task; DDT = delay discounting task; AUC = Area under the Curve.

In line with our hypothesis of intercorrelations for deficits in conscientiousness, urgency, and the BIS, we observed a strong and significant correlation (r = 0.82) between deficits in conscientiousness and urgency. Moreover, both deficits in conscientiousness and urgency were moderately and significantly correlated with the BIS (r = 0.51 and 0.40), and BPD patients and ADHD patients scored higher than matched controls on all three latent variables. With respect to our hypothesis of small-to-no correlations between the self-report-based dimensions and the behavioral dimensions, we observed two significant low correlations of urgency with waiting impulsivity/GNG (r = 0.25) and stopping impulsivity/SST (r = 0.26). Furthermore, two very low marginally significant correlations (r < 0.02) were found between BIS and impulsive choice/DDT, and between deficits in conscientiousness and stopping impulsivity/SST. No hypothesis was made regarding the relationship between waiting impulsivity/GNG and stopping impulsivity/SST, and our analysis revealed the relationship to be nonsignificant. As expected, impulsive choice/DDT was found to be independent from the other two behavioral dimensions as well (see Table 2 for all correlations between latent variables). With respect to the clinical profiles of behavioral tests, the findings supported all hypotheses: both BPD and ADHD patients showed heightened impulsive choice, and ADHD patients additionally showed heightened stopping and waiting impulsivity (see Table 1).

Table 2.

Correlation Table of Latent Variables of the Final Model.

R	1	2	3	4	5	6
1. Barratt Scale	—	.51***	.40***	.14	.09	−.14*
2. Deficits in conscientiousness		—	.82***	.13	.19*	−.04
3. Urgency			—	.25*	.26**	.01
4. Waiting impulsivity/GNG				—	−.02	−.00
5. Stopping impulsivity/SST					—	−.04
6. Impulsive choice/DDT						—

Note. GNG = Go-NoGo task; SST = stop-signal task; DDT = delay discounting task.

Discussion

Based on the literature review and our clinically motivated interest in impulsivity dimensions, we developed a well-fitting CFA model in which existing conceptualizations were modeled as separate dimensions. The model was used to test hypotheses about relationships between personality-related and behavioral dimensions.

Three personality-related dimensions—deficits in conscientiousness, urgency, and the BIS—were found to be related, in that they were correlated in the CFA model and more pronounced among BPD and ADHD patients relative to matched controls. A fourth personality-related dimension—sensation seeking—was excluded from the final model because it was found to be uncorrelated with all other dimensions and not heightened among patients with BPD or ADHD. It can be concluded that sensation seeking is unrelated to impulsivity in healthy people and patients with BPD and ADHD, which is consistent with previous research (Lopez et al., 2015; MacKillop et al., 2016; Paret et al., 2016).

Meanwhile, the behavioral dimensions were found to be completely unrelated to each other. Lack of correlation between impulsive choice/DDT and impulsive action as measured by GNG and SST has been observed in previous studies (MacKillop et al., 2016; Stahl et al., 2014), but our results further suggest that, while the GNG and SST are both distinguishable from impulsive choice, they should not be considered measures of a single impulsive action dimension. Furthermore, waiting impulsivity/GNG and stopping impulsivity/SST were found to be expressed differently in patients. BPD patients showed no differences from healthy controls in either waiting or stopping impulsivity, but ADHD patients showed increases in both of these dimensions. The results lend support to the existing suggestion that BPD patients are not prone to impulsive action in emotionally neutral situations (Turner et al., 2017). It is, therefore, possible that an emotional variant of GNG (and SST) might better discriminate between some patient groups and controls. Future studies could include emotional variants of GNG and SST in the model (e.g., Hare et al., 2008; Pawliczek et al., 2013). Our results further align with previous findings of impairment in both waiting and stopping impulsivity in patients with ADHD (as in Schachar et al., 2007).

In contrast to previous studies, where the percentage of NoGo commissions was considered the primary index of waiting impulsivity as measured by GNG (as in MacKillop et al., 2016), we found differences between ADHD patients and controls on a dimension measured by three other indices derivable from GNG. In our CFA model, the percentage of NoGo commissions was actually removed from the model due to the low associated regression coefficient. It follows that the deficit of ADHD patients in waiting impulsivity might be driven by attention deficit and response speed abnormalities, but not primarily by the number of action withdrawal failures. Moreover, an increased number of NoGo commissions might constitute a behavioral result of attention and response speed impairment and might be pronounced in challenging situations, since more NoGo commissions are observed in more cognitively demanding tasks (Nieuwenhuis et al., 2003). Thus, inconsistent results regarding a deficit in waiting impulsivity in ADHD patients (Pani et al., 2013; Rubia et al., 2007; Schachar et al., 2007) might follow from taking into account only NoGo commissions, but not considering attention- and response-speed-related task parameters, especially in less cognitively demanding tasks. More confident conclusions to this effect could be drawn if our results were replicated with a more cognitively demanding GNG. Overall, our findings illustrate the importance of evaluating behavioral impulsivity constructs based on multiple indices of global task performance rather than a single variable that might not sufficiently reflect the underlying process leading to impulsivity.

Impulsive choice, as measured by the DDT, was found to be elevated in both patient groups while being uncorrelated with all other dimensions. This result is in agreement with previous studies (Barker et al., 2015; Patros et al., 2016; Turner et al., 2017) and indicates that, regardless of their scores on GNG, SST, and self-report measures, both BPD and ADHD patients tended to prefer immediate rewards over long-term benefits, thus displaying decision making with low sensitivity to consequences. With respect to best practices in the measurement of impulsive choice, our CFA model identified the AUC parameters as more reliable measures than k parameters, since the AUCs were found to load more strongly on the latent variable expressing impulsive choice.

Regarding the associations of self-report-based and behavioral dimensions, several low to very low relationships were observed. Low associations were found between urgency and both dimensions of impulsive action. Comparable correlations have been previously recorded in the literature (Cyders & Coskunpinar, 2011; VanderBroek-Stice, Stojek, Beach, vanDellen, & MacKillop, 2017). Because these associations were observed in the model including healthy participants in our study, it suggests that a link between dimensions of impulsive action and emotional impulsivity exists in the healthy population. In other words, it is possible that the decrease in waiting and stopping associated with a tendency to emotional impulsivity, as known in BPD patients, could, to a lesser degree, apply to healthy people also. Other associations between self-report-based and behavioral measures were found to be very low, although two of them reached borderline significance. Overall, our results correspond with previous studies in that relationships between self-report-based and behavioral dimensions of impulsivity are generally low to very low, although they might reach significance in bigger samples. The most prominent relationship between self-report-based and behavioral impulsivity dimensions based on our study exists between urgency and dimensions of impulsive action.

A limitation of the current study is that the BIS was included among the latent factors in the final CFA model, despite the lack of clarity over its factor structure in preliminary CFAs. Problems with the scale’s factor structure have been likewise observed in past studies (e.g., Linhartová et al., 2017; Reise et al., 2013; Vasconcelos et al., 2012). In our study, the scale was retained for its previously demonstrated ability to discriminate between patients and healthy people (Barker et al., 2015; Chamberlain et al., 2017). One direction for improving the fit of the unidimensional structure of the BIS and, consequently, any CFA models incorporating the BIS would be to shorten the scale, leaving only the items with high loadings on a global impulsivity factor. Abbreviated psychometrically sound versions of the BIS with different factor structures have been already proposed (Morean et al., 2014; Spinella, 2007; Steinberg et al., 2013). A further limitation is that, amid sample size constraints, we were unable to fit the proposed CFA model separately within BPD and ADHD patients to track potential group differences in dimensional interrelationships.

In sum, the latent factor model developed in this study shows that impulsive behaviors can stem from different independent impulsivity subtypes and that underlying impulsivity dimensions can differ in psychiatric patient groups. The results suggest that therapeutical approaches to patients with increased impulsivity can be tailored specifically according to the type of impulsivity in the given patient group. For example, for patients who demonstrate deficits in waiting and stopping impulsivity, training in behavioral inhibition based on the respective behavioral tests would be appropriate (e.g., Melara, Singh, & Hien, 2018; Zhao & Jia, 2019). However, if the dominant dimension is impulsivity under the influence of strong emotions, treatment based on distress tolerance and emotion regulation enhancement can be recommended (such as dialectical behavior therapy, Linehan, 1993). The proposed model can be used for descriptions of impulsivity profiles in psychiatric groups, and thus help to tailor therapy for impulsive patients.

Supplemental Material

Supplementary_material_correlation_matrix – Supplemental material for Dimensions of Impulsivity in Healthy People, Patients with Borderline Personality Disorder, and Patients with Attention-Deficit/Hyperactivity Disorder

Supplemental material, Supplementary_material_correlation_matrix for Dimensions of Impulsivity in Healthy People, Patients with Borderline Personality Disorder, and Patients with Attention-Deficit/Hyperactivity Disorder by Pavla Linhartová, Jan Širůček, Anastasia Ejova, Richard Barteček, Pavel Theiner and Tomáš Kašpárek in Journal of Attention Disorders

Footnotes

Authors’ Note

Anastasia Ejova is currently affiliated with School of Psychology, The University of Auckland, New Zealand.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Health of the Czech Republic, grant nr. 15-30062A. All rights reserved.

Notes

Supplemental Material

Supplemental material for this article is available online.

ORCID iD

Pavla Linhartová

Author Biographies

Pavla Linhartová is a psychologist and doctoral candidate at the Department of Psychiatry, Faculty of Medicine, Masaryk University and University Hospital Brno, Czech Republic. Her primary research and clinical interests are the borderline personality disorder and its treatment options, including rTMS or fMRI neurofeedback.

Jan Širůček, PhD is a senior researcher at the Institute for Research on Children, Youth and Family, Faculty of Social Studies, Masaryk University Brno, Czech Republic and Department of Psychiatry, Faculty of Medicine, Masaryk University and University Hospital Brno, Czech Republic. His primary research interests are psychometrics and psychological assessment.

Anastasia Ejova, PhD is a postdoctoral research fellow at the University of Auckland’s School of Psychology. She studies gambling, physiological expressions of anxiety, and the cultural and religious contexts affecting mental health.

Richard Barteček, PhD is a psychiatrist and senior researcher at the Department of Psychiatry, Faculty of Medicine, Masaryk University and University Hospital Brno, Czech Republic. His main research interests include neuroimaging, biomarkers of the severity of affective and other psychiatric disorders and virtual reality utilisation in the research and treatment of psychiatric disorders.

Pavel Theiner, PhD is a psychiatrist and senior researcher at the Department of Psychiatry, Faculty of Medicine, Masaryk University and University Hospital Brno, Czech Republic. He is specialized in child and adolescent psychiatry; his main research interests are ADHD (genetics and neurobiology) and eating disorders.

Tomáš Kašpárek, PhD is an associate professor of Psychiatry and the head of the Department of Psychiatry, Faculty of Medicine, Masaryk University and University Hospital Brno, Czech Republic. He is involved in the neuroimaging research of neuropsychiatric disorders.

References

Ainslie

(1975). Specious reward: A behavioral theory of impulsiveness and impulse control. Psychological Bulletin, 82, 463-496. doi:10.1037/h0076860

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.

Barker

Romaniuk

Cardinal

R. N.

Pope

Nicol

Hall

(2015). Impulsivity in borderline personality disorder. Psychological Medicine, 45, 1955-1964. doi:10.1017/S0033291714003079

Barratt

E. S.

(1959). Anxiety and impulsiveness related to psychomotor efficiency. Perceptual and Motor Skills, 9, 191-198. doi:10.2466/pms.1959.9.3.191

Barteček

Hořínková

Linhartová

Kašpárek

(2019). Emotional impulsivity is connected to suicide attempts and health care utilization in patients with borderline personality disorder. General Hospital Psychiatry. Advance online publication. doi:10.1016/j.genhosppsych.2018.11.008

Berg

J. M.

Latzman

R. D.

Bliwise

N. G.

Lilienfeld

S. O.

(2015). Parsing the heterogeneity of impulsivity: A meta-analytic review of the behavioral implications of the UPPS for psychopathology. Psychological Assessment, 27, 1129-1146. doi:10.1037/pas0000111

Bøen

Hummelen

Elvsåshagen

Boye

Andersson

Karterud

Malt

U. F.

(2015). Different impulsivity profiles in borderline personality disorder and bipolar II disorder. Journal of Affective Disorders, 170, 104-111. doi:10.1016/j.jad.2014.08.033

Chamberlain

S. R.

Ioannidis

Leppink

E. W.

Niaz

(2017). ADHD symptoms in non-treatment seeking young adults: Relationship with other forms of impulsivity. CNS Spectrums, 22(1), 22-30. doi:10.1017/S1092852915000875

Cyders

M. A.

Combs

Fried

R. E.

Zapolski

T. C. B.

Smith

G. T.

(2009). Emotion-based impulsivity and its importance for impulsive behavioral outcomes. In Lassiter

G. H.

(Ed.), Impulsivity: Causes, control and disorders (pp. 105-126). New York, NY: Nova Science Publishers.

10.

Cyders

M. A.

Coskunpinar

(2011). Measurement of constructs using self-report and behavioral lab tasks: Is there overlap in nomothetic span and construct representation for impulsivity? Clinical Psychology Review, 31, 965-982. doi:10.1016/j.cpr.2011.06.001

11.

Cyders

M. A.

Smith

G. T.

(2007). Mood-based rash action and its components: Positive and negative urgency. Personality and Individual Differences, 43, 839-850. doi:10.1016/j.paid.2007.02.008

12.

Eagle

D. M.

Bari

Robbins

T. W.

(2008). The neuropsychopharmacology of action inhibition: Cross-species translation of the stop-signal and go/no-go tasks. Psychopharmacology, 199, 439-456. doi:10.1007/s00213-008-1127-6

13.

Evenden

J. L.

(1999). Varieties of impulsivity. Psychopharmacology, 146, 348-361. doi:10.1007/PL00005481

14.

Hagenhoff

Franzen

Koppe

Baer

Scheibel

Sammer

Lis

(2013). Executive functions in borderline personality disorder. Psychiatry Research, 210, 224-231. doi:10.1016/j.psychres.2013.05.016

15.

Hare

T. A.

Tottenham

Galvan

Voss

H. U.

Glover

G. H.

Casey

B. J.

(2008). Biological substrates of emotional reactivity and regulation in adolescence during an emotional go-nogo task. Biological Psychiatry, 63, 927-934. doi:10.1016/j.biopsych.2008.03.015

16.

Jacob

G. A.

Gutz

Bader

Lieb

Tüscher

Stahl

(2010). Impulsivity in borderline personality disorder: Impairment in self-report measures, but not behavioral inhibition. Psychopathology, 43, 180-188. doi:10.1159/000304174

17.

Kooij

J. J. S.

(2012). Adult ADHD. Diagnostic assessment and treatment (3rd ed.). New York, NY: Springer-Verlag Publishing.

18.

Krause-Utz

Cackowski

Daffner

Sobanski

Plichta

M. M.

Bohus

. . . Schmahl

(2016). Delay discounting and response disinhibition under acute experimental stress in women with borderline personality disorder and adult attention deficit hyperactivity disorder. Psychological Medicine, 46, 3137-3149. doi:10.1017/S0033291716001677

19.

Lampe

Konrad

Kroener

Fast

Kunert

H. J.

Herpertz

S. C.

(2007). Neuropsychological and behavioural disinhibition in adult ADHD compared to borderline personality disorder. Psychological Medicine, 37, 1717-1729. doi:10.1017/S0033291707000517

20.

Linehan

M. M.

(1993). Cognitive-behavioral treatment of borderline personality disorder. New York, NY: The Guilford Press.

21.

Linhartová

Širůček

Barteček

Theiner

Jeřábková

Rudišinová

Kašpárek

(2017). Czech versions of impulsivity self-report scales and their psychometric properties. Czech and Slovak Psychiatry, 113, 149-157.

22.

Little

T. D.

Cunningham

W. A.

Shahar

Widaman

K. F.

(2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling: A Multidisciplinary Journal, 9, 151-173. doi:10.1207/S15328007SEM0902_1

23.

Lopez

Dauvilliers

Jaussent

Billieux

Bayard

(2015). A multidimensional approach of impulsivity in adult attention deficit hyperactivity disorder. Psychiatry Research, 227, 290-295. doi:10.1016/j.psychres.2015.03.023

24.

Lozano

J. H.

(2015). Are impulsivity and intelligence truly related constructs? Evidence based on the fixed-links model. Personality and Individual Differences, 85, 192-198. doi:10.1016/j.paid.2015.04.049

25.

MacKillop

Miller

J. D.

Fortune

Maples

Lance

C. E.

Campbell

W. K.

Goodie

A. S.

(2014). Multidimensional examination of impulsivity in relation to disordered gambling. Experimental and Clinical Psychopharmacology, 22, 176-185. doi:10.1037/a0035874

26.

MacKillop

Weafer

J. C.

Gray

Oshri

Palmer

de Wit

(2016). The latent structure of impulsivity: Impulsive choice, impulsive action, and impulsive personality traits. Psychopharmacology, 233, 3361-3370. doi:10.1007/s00213-016-4372-0

27.

Maraz

Andó

Rigó

Harmatta

Takách

Zalka

Demetrovics

(2016). The two-faceted nature of impulsivity in patients with borderline personality disorder and substance use disorder. Drug and Alcohol Dependence, 163, 48-54. doi:10.1016/j.drugalcdep.2016.03.015

28.

Mazur

J. E.

(1987). An adjusting procedure for studying delayed reinforcement. In Commons

M. L.

Mazur

J. E.

Nevin

J. A.

Rachlin

(Eds.), Quantitative Analyses of Behavior: Vol. 5. The effect of delay and of intervening events on reinforcement value (pp. 55-73). Hillsdale, NJ: Lawrence Erlbaum Associates.

29.

Melara

R. D.

Singh

Hien

D. A.

(2018). Neural and behavioral correlates of attentional inhibition training and perceptual discrimination training in a visual flanker task. Frontiers in Human Neuroscience, 12, 1-15. doi:10.3389/fnhum.2018.00191

30.

Metin

Roeyers

Wiersema

J. R.

Van Der Meere

Sonuga-Barke

(2012). A meta-analytic study of event rate effects on Go/No-Go performance in attention-deficit/hyperactivity disorder. Biological Psychiatry, 72, 990-996. doi:10.1016/j.biopsych.2012.08.023

31.

Morean

M. E.

DeMartini

K. S.

Leeman

R. F.

Pearlson

G. D.

Anticevic

Krishnan-Sarin

. . . O’Malley

S. S.

(2014). Psychometrically improved, abbreviated versions of three classic measures of impulsivity and self-control. Psychological Assessment, 26, 1003-1020. doi:10.1037/pas0000003

32.

Myerson

Green

Warusawitharana

(2001). Area under the curve as a measure of discounting. Journal of the Experimental Analysis of Behavior, 76, 235-243. doi:10.1901/jeab.2001.76-235

33.

Najt

Perez

Sanches

Peluso

M. A.

Glahn

Soares

J. C.

(2007). Impulsivity and bipolar disorder. European Neuropsychopharmacology, 17, 313-320. doi:10.1016/j.euroneuro.2006.10.002

34.

Nieuwenhuis

Yeung

van den Wildenberg

Ridderinkhof

K. R.

(2003). Electrophysiological correlates of anterior cingulate function in a go/no-go task: Effects of response conflict and trial type frequency. Cognitive, Affective & Behavioral Neuroscience, 3, 17-26. doi:10.3758/CABN.3.1.17

35.

Ouzir

(2013). Impulsivity in schizophrenia: A comprehensive update. Aggression and Violent Behavior, 18, 247-254. doi:10.1016/j.avb.2012.11.014

36.

Pani

Menghini

Napolitano

Calcagni

Armando

Sergeant

J. A.

Vicari

(2013). Proactive and reactive control of movement are differently affected in Attention Deficit Hyperactivity Disorder children. Research in Developmental Disabilities, 34, 3104-3111. doi:10.1016/j.ridd.2013.06.032

37.

Paret

Hoesterey

Kleindienst

Schmahl

(2016). Associations of emotional arousal, dissociation and symptom severity with operant conditioning in borderline personality disorder. Psychiatry Research, 244, 194-201. doi:10.1016/j.psychres.2016.07.054

38.

Parker

J. D. A.

Bagby

R. M.

(1997). Impulsivity in adults: A critical review of measurement approaches. In Webster

C. D.

Jackson

M. A.

(Eds.), Impulsivity: Theory, assessment, and treatment (pp. 142-157). New York, NY: The Guilford Press.

39.

Patros

C. H. G.

Alderson

R. M.

Kasper

L. J.

Tarle

S. J.

Lea

S. E.

Hudec

K. L.

(2016). Choice-impulsivity in children and adolescents with attention-deficit/hyperactivity disorder (ADHD): A meta-analytic review. Clinical Psychology Review, 43, 162-174. doi:10.1016/j.cpr.2015.11.001

40.

Patton

J. H.

Stanford

M. S.

Barratt

E. S.

(1995). Factor structure of the Barratt Impulsiveness Scale. Journal of Clinical Psychology, 51, 768-774. doi:10.1002/1097-4679(199511)51:6<768::AID-JCLP2270510607>3.0.CO;2-1

41.

Pawliczek

C. M.

Derntl

Kellermann

Kohn

Gur

R. C.

Habel

(2013). Inhibitory control and trait aggression: Neural and behavioral insights using the emotional stop signal task. NeuroImage, 79, 264-274. doi:10.1016/j.neuroimage.2013.04.104

42.

Pedersen

S. L.

Walther

C. A. P.

Harty

S. C.

Gnagy

E. M.

Pelham

W. E.

Molina

B. S. G.

(2016). The indirect effects of childhood attention deficit hyperactivity disorder on alcohol problems in adulthood through unique facets of impulsivity. Addiction, 111, 1582-1589. doi:10.1111/add.13398

43.

Reise

S. P.

Moore

T. M.

Sabb

F. W.

Brown

A. K.

London

E. D.

(2013). The Barratt Impulsiveness Scale–11: Reassessment of its structure in a community sample. Psychological Assessment, 25, 631-642. doi:10.1037/a0032161

44.

Richards

J. B.

Zhang

Mitchell

S. H.

de Wit

(1999). Delay or probability discounting in a model of impulsive behavior: Effect of alcohol. Journal of the Experimental Analysis of Behavior, 71, 121-143. doi:10.1901/jeab.1999.71-121

45.

Roberts

Milich

Fillmore

M. T.

(2016). The effects of preresponse cues on inhibitory control and response time in adults with ADHD. Journal of Attention Disorders, 20, 317-324. doi:10.1177/1087054713495737

46.

Robinson

E. S. J.

Eagle

D. M.

Economidou

Theobald

D. E. H.

Mar

A. C.

Murphy

E. R.

. . . Dalley

J. W.

(2009). Behavioural characterisation of high impulsivity on the 5-choice serial reaction time task: Specific deficits in “waiting” versus “stopping.” Behavioural Brain Research, 196, 310-316. doi:10.1016/j.bbr.2008.09.021

47.

Rosseel

(2012). lavaan: An R package for structural equation modelling. Journal of Statistical Software, 48(2), 1-36. doi:10.18637/jss.v048.i02

48.

Rubia

Smith

Taylor

(2007). Performance of children with attention deficit hyperactivity disorder (ADHD) on a test battery of impulsiveness. Child Neuropsychology, 13, 276-304. doi:10.1080/09297040600770761

49.

Schachar

Logan

G. D.

Robaey

Chen

Ickowicz

Barr

(2007). Restraint and cancellation: Multiple inhibition deficits in attention deficit hyperactivity disorder. Journal of Abnormal Child Psychology, 35, 229-238. doi:10.1007/s10802-006-9075-2

50.

Sheehan

D. V.

Lecrubier

Sheehan

K. H.

Amorim

Janavs

Weiller

. . . Dunbar

G. C.

(1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(Suppl. 2), 22-33.

51.

Smith

J. L.

Mattick

R. P.

Jamadar

S. D.

Iredale

J. M.

(2014). Deficits in behavioural inhibition in substance abuse and addiction: A meta-analysis. Drug and Alcohol Dependence, 145, 1-33. doi:10.1016/j.drugalcdep.2014.08.009

52.

Spinella

(2007). Normative data and a short form of the Barratt Impulsiveness Scale. International Journal of Neuroscience, 117, 359-368. doi:10.1080/00207450600588881

53.

Stahl

Voss

Schmitz

Nuszbaum

Tüscher

Lieb

Klauer

K. C.

(2014). Behavioral components of impulsivity. Journal of Experimental Psychology: General, 143, 850-886. doi:10.1037/a0033981

54.

Stanford

M. S.

Mathias

C. W.

Dougherty

D. M.

Lake

S. L.

Anderson

N. E.

Patton

J. H.

(2009). Fifty years of the Barratt Impulsiveness Scale: An update and review. Personality and Individual Differences, 47, 385-395. doi:10.1016/j.paid.2009.04.008

55.

Steinberg

Sharp

Stanford

M. S.

Tharp

A. T.

(2013). New tricks for an old measure: The development of the Barratt Impulsiveness Scale–Brief (BIS-Brief). Psychological Assessment, 25, 216-226. doi:10.1037/a0030550

56.

Swick

Ashley

Turken

(2011). Are the neural correlates of stopping and not going identical? Quantitative meta-analysis of two response inhibition tasks. NeuroImage, 56, 1655-1665. doi:10.1016/j.neuroimage.2011.02.070

57.

Turner

Sebastian

Tüscher

(2017). Impulsivity and cluster B personality disorders. Current Psychiatry Reports, 19, Article 15. doi:10.1007/s11920-017-0768-8

58.

VanderBroek-Stice

Stojek

M. K.

Beach

S. R. H.

vanDellen

M. R.

MacKillop

(2017). Multidimensional assessment of impulsivity in relation to obesity and food addiction. Appetite, 112, 59-68. doi:10.1016/j.appet.2017.01.009

59.

van Eijk

Sebastian

Krause-Utz

Cackowski

Demirakca

Biedermann

S. V.

Tüscher

. (2015). Women with borderline personality disorder do not show altered BOLD responses during response inhibition. Psychiatry Research: Neuroimaging, 234, 378-389. doi:10.1016/j.pscychresns.2015.09.017

60.

Vasconcelos

A. G.

Malloy-Diniz

Correa

(2012). Systematic review of psychometric proprieties of Barratt Impulsiveness Scale version 11 (BIS-11). Clinical Neuropsychiatry, 9(2), 61-74. doi:10.1037/t05661-000

61.

Verbruggen

Logan

G. D.

(2009). Models of response inhibition in the stop-signal and stop-change paradigms. Neuroscience and Biobehavioral Reviews, 33, 647-661. doi:10.1016/j.neubiorev.2008.08.014

62.

Whiteside

S. P.

Lynam

D. R.

(2001). The five factor model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30, 669-689. doi:10.1016/S0191-8869(00)00064-7

63.

Winstanley

C. A.

Eagle

D. M.

Robbins

T. W.

(2006). Behavioral models of impulsivity in relation to ADHD: Translation between clinical and preclinical studies. Clinical Psychology Review, 26, 379-395. doi:10.1016/j.cpr.2006.01.001

64.

World Health Organization. (2011). International classification of diseases (10th revision). Geneva, Switzerland: Author.

65.

Zanarini

M. C.

Gunderson

J. G.

Frankenburg

F. R.

Chauncey

D. L.

(1989). The revised diagnostic interview for borderlines: Discriminating BPD from other axis II disorders. Journal of Personality Disorders, 3(1), 10-18. doi:10.1521/pedi.1989.3.1.10

66.

Zhao

Jia

(2019). Training and transfer effects of interference control training in children and young adults. Psychological Research, 83, 1519-1530. doi:10.1007/s00426-018-1007-6

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