i
The Moderating Effect of Impulsivity on Psychopathy
and Risk-taking in a Community Sample
A Thesis
Submitted to the Faculty
of
Drexel University
by
Unnati H. Patel
in partial fulfillment of the
requirements for the degree
of
Master of Science in Psychology
May 2015
ii Acknowledgements
First and foremost, I would like to express my sincere gratitude to my mentor, Dr. David
DeMatteo, for his patience, motivation, enthusiasm, immense knowledge of the field, and continuous support of my research. I appreciate the contributions of his time and ideas to make my experience at Drexel University both stimulating and productive. I could not have imagined having a better advisor and mentor. Besides Dave, I would like to thank the rest of my thesis committee, Dr. Jennifer Gallo and Dr. Stephanie Brooks-Holliday, for their encouragement, insightful comments, and for broadening the value of my work.
Additionally, I thank the fellow members of the DeMatteo lab: Heidi Strohmaier, Megan
Murphy, Casey LaDuke, and Jaymes Fairfax-Columbo for the ever-helpful discussions, quick email responses, and for the moral support they provided over two years. Lastly, I would like to thank my family: my parents, Jagruti and Hemant Patel, and my sister,
Reema Patel, for their love and encouragement throughout this process. Without their continual support, this project would not have been possible.
iii Table of Contents
List of Tables ...... v
Abstract ...... vi
1. Psychopathy ...... 1
1.1 Psychopathy and Neuropsychology ...... 4
1.2 Community Psychopathy ...... 7
1.3 Corporate Psychopathy ...... 11
2. Impulsivity ...... 13
2.1 Impulsivity and Neuropsychology ...... 15
2.2 Impulsivity and Psychopathy ...... 16
3. Risk-taking ...... 18
4. Current Study ...... 19
4.1 Hypotheses ...... 20
5. Methods...... 21
5.1 Participants ...... 21
5.2 Procedures ...... 21
5.3 Materials ...... 22
5.3.1 Behavioral Measures ...... 22
5.3.2 Self-Report Measures...... 25
6. Statistical Analyses ...... 27
7. Results ...... 29
7.1 Preliminary Analyses ...... 29
7.2 Hypothesis Testing...... 31 iv 7.3 Exploratory Analyses ...... 33
8. Discussion ...... 34
8.1 Limitations ...... 36
8.2 Directions and Conclusions ...... 38
List of References ...... 40
Appendix A: Demographic Questionnaire...... 48
v List of Tables
1. Correlations for personality and behavioral measures ...... 52
2. Descriptive statistics for the LSRP and BIS-11 personality measures ...... 53
3. Descriptive statistics for the behavioral measures ...... 54
4. Demographic characteristics of sample ...... 55 vi Abstract
The Moderating Effect of Impulsivity on Psychopathy and Risk-taking in a Community Sample Unnati H. Patel David DeMatteo, J.D., Ph.D.
Psychopathy is a personality disorder comprised of affective (superficial charm, callousness) and behavioral (impulsivity, risk-taking) deficits, along with deficits in interpersonal relations (manipulativeness, grandiosity). Because a majority of the research on psychopathic individuals has been restricted to incarcerated offenders, little is known about community psychopathy and the characteristics associated with it. The construct of impulsivity is considered a core behavioral feature of offender populations, but the relationship between community psychopathy and impulsivity has received limited empirical focus. The purpose of this study is to fill the gap in the literature by examining the relationship among between impulsivity, risk-taking, and psychopathy among community members. Eighty-nine participants were recruited using Amazon
Mechanical Turk to complete the study. Using several behavioral and self-report measures, participants provided information related to their impulsivity, risk-taking, and psychopathy. Separate multiple regressions were used to analyze whether impulsivity moderates the relationship between psychopathy and risk-taking. Regression results revealed no moderation effect of impulsivity between psychopathy and risk-taking, but correlational analyses suggest the presence of a relationship between psychopathy and trait impulsivity. 1 1: Psychopathy
Psychopathy is a widely studied personality disorder that includes traits like callousness, manipulativeness, impulsivity, and recklessness (Lee & Salekin, 2010), and it is considered by some researchers to be the most important clinical construct in the criminal justice system (Hemphill & Hare, 2004). Despite attempts to expand knowledge of the personality disorder, research has remained focused on incarcerated populations.
The study of community psychopathy is just now beginning to gain traction as a domain in which psychopathy research can grow. Cleckley’s (1941) conceptualization of psychopathy was the basis of the modern conceptualization of the construct, and he lists
16 characteristics that make up the prototypical psychopath, including superficial charm, lack of remorse, and poor judgment. Many of the characteristics are non-criminal in nature and instead focus on the affective characteristics of the prototypical psychopathic individual.
Despite the likeness to antisocial personality disorder (APD), psychopathy is a distinct construct rooted in core personality traits. APD is a clinical diagnosis in the
Diagnostic and Statistical Manual of Mental Disorders (5 th ed.) (DSM-5; American
Psychiatric Association, 2013) linked to violations of social norms, disregard for safety, irresponsibility, and numerous other behavioral features. Estimates of APD in the population range from 0.2% and 3.3% using the diagnostic criteria offered by the DSM-5
(American Psychiatric Association, 2013), while the prevalence rate of psychopathy remains around 1% (Hare, 1999). Criticisms of APD have included over-reliance of criminality as criterion for the disorder (Crego & Widiger, 2014). 2 Because psychopathy does not have a clinical basis like APD, it is assessed using measures like Hare’s Psychopathy Checklist-Revised (PCL-R; Hare, 2003). The PCL-R is the most widely used forensic assessment tool for the detection of psychopathy
(DeMatteo et al., 2014). The most common interpretation of the PCL-R is based on the two-factor model, which taps into the interpersonal and behavioral aspects of the condition, respectively (Hare, 1991). Cleckley’s influence can be noted in PCL-R.
Examples of the Factor 1 aspect include terms such as superficial charm and grandiose sense of self-worth, with Factor 2 features containing terms such as poor behavioral controls and parasitic lifestyle (Hare, 1991). Each of the 20 items is given a score from 0 to 2 with the highest possible score as 40, and a score of 30 is typically considered the cut-off for psychopathy.
In recent years, it has become more acceptable to acknowledge psychopathy as a dimensional rather than categorical construct and the PCL-R has received criticism in that it arbitrarily assigns a label as “psychopath” or “non-psychopath” when it may be more appropriate to identify an individual on a spectrum from normalcy to full-blown psychopathy (Bjork, Chen, & Hommer, 2012). Despite criticisms, the PCL-R remained one of the most heavily used tools in forensic psychology by researchers and clinicians alike, and its introduction in U.S. courts has steadily been increasing in the years since its publication (DeMatteo et al., 2014).
Hare (1991) conceptualized Factor 1 and Factor 2 of the PCL-R to tap into two distinct subgroups, primary and secondary psychopathy. Primary psychopathy is closely related to the interpersonal traits of psychopathy, while items more closely linked to behavioral features reflect secondary psychopathy. Primary psychopathy is considered by 3 Karpman (1948) to be the only true form of psychopathy as the features are based in a lack of empathy, which is a highly distinguishing feature of psychopaths (Hare, 1991).
On the contrary, secondary psychopaths exhibit higher anxiety and fear levels, and tend to display poor behavioral control (Karpman, 1948). However, psychopathy does not necessarily have to be either based on only primary or secondary psychopathy.
Individuals can endorse features on both facets, but if they display more characteristics on one aspect, they are considered to be higher in either “primary” or “secondary” psychopathy. Based on the older classification system of psychopathy, the cut-off score of 30 on the PCL-R would help determine if someone was a psychopath or not. But with the more recent “dimensionality” construct of psychopathy, it has become more challenging to separate community psychopathic individuals into either primary or secondary psychopathy. Because the understanding of community psychopathy is also relatively new, there is no specific rule that individuals must endorse specific features of a particular factor in order to be considered psychopathic.
Continuing with the primary and secondary psychopathy subtypes, Hicks,
Markon, Patrick, Krueger, and Newman (2004) identified subtypes of psychopathy in a criminal population, lending support to the notion of the two-factor model. Prior research conducted on psychopaths has been mostly limited to studying the condition in forensic samples, although there is growing research in community populations. Incarcerated individuals higher in primary psychopathy are more likely to be accurate when judging victim vulnerability according to gait (Book, Costello, & Camilleri, 2013). Forty-seven inmates viewed 12 videos of unsuspecting targets walking (8 women, 4 men) and rated them on 10-point scale measuring vulnerability. Participants who scored higher on Factor 4 1 on PCL-R were more accurate in judging victim vulnerability by looking at gait (Book et al., 2013).
1.1: Psychopathy and Neuropsychology
Various imaging studies have attempted to identify the basis of psychopathy in neurobiology. Psychopathy is not restricted to a single dysfunctional brain structure or network. For instance, the orbitofrontal, ventromedial prefrontal, and cingulate cortices have all been implicated in the development of the disorder (Gao, Glenn, Schug, Yang, &
Raine, 2009). More specifically, a structural brain imaging study by Yang and colleagues
(2005) found that convicted psychopathic individuals had a 22.3% reduction in the volume of prefrontal gray matter, suggesting that on average incarcerated psychopaths had a substantially different prefrontal cortex than those who evaded arrest. Research on the amygdala has found that participants higher on psychopathy have a more blunted response during emotional moral decision-making tasks (Glenn, Raine, & Schug, 2009).
Particularly, the joint dysfunction of the amygdala and the ventromedial prefrontal cortex contributes to the unique behavioral characteristics of the psychopath (Blair, 2008).
The relationship between psychopathy and executive functioning has also been empirically established. Hiatt, Schmitt, and Newman (2004) found that psychopathic offenders perform better than controls in two out of three Stroop tasks, which measure an individual’s ability to ignore (or succumb to the influence of) information that is unimportant or irrelevant to the task at hand (MacLeod, 1992). Since the psychopathic offenders were able to successfully ignore the peripheral information, this suggests that they are better able to “tune out” information that is not integrated with the relevant material and does not lead them to their goals (Hiatt, Schmitt, & Newman, 2004). 5
However, another study utilizing various neuropsychological paradigms has found that inmates with psychopathy performed worse than criminal controls on selective attention and executive function tasks (Pham, Vanderstukken, Philippot, & Vanderlinden, 2003). It must be noted that psychopathic individuals in Pham et al.’s (2003) study had lower psychopathy scores as measured by the PCL-R than participants in Hiatt, Schmitt, and
Newman’s (2004) study.
An additional focus in psychopathy has been in psychophysiology. Different avenues of research in the field have been examined and generally indicate a significant difference between psychopaths and their non-psychopathic counterparts. One approach includes examining heart rate (HR) and electrodermal activity (EDA), which are considered the most popular measures of psychophysiology. HR is controlled by both sympathetic and parasympathetic autonomic nervous system activity (Lorber, 2004), whereas EDA is regulated only by sympathetic nervous system activity (Blascovich &
Kelsey, 1990). A meta-analysis by Lorber (2004) found that psychopaths on average had lower baseline and reactive heart rates when exposed to aversive stimuli. This diminished autonomic reactivity strengthens the distinction between low and high psychopathic individuals and furthers the notion that psychopathy is comprised of distinct emotional and interpersonal deficits (characterized by a more blunted or flat response that non- psychopathic individuals, which may be interpreted as either calm or non-caring) that are not better explained by any other psychiatric conditions. Additional evidence to support the above hypothesis arrives from Patrick, Bradley, and Lang’s (1993) study, in which it was reported that emotional attachment – and not criminality – is the feature most 6 strongly associated with the atypical startle response of psychopaths (i.e., an absence of the startle potentiation to negative stimuli).
Research on psychopathy with electroencephalography (EEG) has been less conclusive, with some studies finding a smaller P300 amplitude (Kiehl et al., 1999) while others find a larger amplitude in psychopathic individuals (Raine & Venables, 1988).
P300 is an electrical potential that occurs when a person is experiencing novel stimuli, and it has been suggested that reduced or delayed P300 amplitudes indicate fewer attentional resources being allocated to the task at hand (Polich, 2003). The disparity in the cognitive research on psychopathy may be related to the hypothesis that reduced P300 amplitudes reflect antisocial behaviors rather than interpersonal features of psychopathy
(Hicks et al., 2007), which may provide some reasoning as to why no association has been found between P300 and psychopathy in numerous studies (Gao & Raine, 2009).
Additionally, according to Gray's biopsychological theory of personality (Carver &
White, 1994), both primary and secondary psychopathy are positively linked to the
Behavioral Activation System (BAS). Individuals who are activated to a high degree of the BAS tend to be more sensitive to goal-oriented behaviors that end in reward rather being sensitive to avoiding behaviors that might result in punishment (Ross, Moltó, et al.,
2007). This fits well with the notion that a psychopathic individual experiences low trait anxiety and more often seeks out behaviors that are stimulating and rewarding.
Exploratory studies have been conducted to examine the relationship between hormones – particularly, cortisol and testosterone – and psychopathy. It has been hypothesized that low levels of cortisol and high levels of testosterone lead to a diminished sensitivity to punishment and increased sensitivity to reward, which may 7 contribute to the development of psychopathy (van Honk & Schutter, 2006).
Behaviorally, a higher testosterone to cortisol ratio in the amygdala impairs the signals between it and the orbitofrontal and medial regions of the prefrontal cortex, which can result in a reduction of fear and an increase in the aggression of an individual (van Honk
& Schutter, 2006). Though the research in the field of hormones requires expansion, it has offered an interesting and alternative explanation for the development of psychopathy.
1.2: Community Psychopathy
Previous findings lend credence to the notion that primary psychopathy is made up of interpersonal features that are not intrinsically associated with criminal behavior
(Belmore & Quinsey, 1994). Primary psychopaths may be more selfish, manipulative, and superficially charming, but because these traits are not illegal, other people are much less likely to view these characteristics as part of an existing personality disorder. They are instead seen as more extreme variants of normal personality traits. Moreover, psychopathic traits in community populations are expected to be lower in severity as the individuals who express more extreme variants of the disorder are more likely to end up incarcerated. Indeed, using an undergraduate university sample as a proxy for community members, Levenson, Kiehl, and Fitzpatrick (1995) asked respondents to fill out the
Levenson Self-Report Psychopathy Scale and found that the majority of endorsed items rated as either “agree somewhat” or “disagree somewhat” rather than “agree strongly” or
“disagree strongly,” which indicates that most respondents displayed only mild psychopathy. 8
The differences in the sophistication between primary and secondary psychopathy have led some researchers to consider the primary psychopath to be more “successful” than his secondary counterpart. This “success” arrives from the notion that these individuals have not yet come into contact with the criminal justice system. Additionally, if these “successful” psychopathic individuals do participant in illegal activities, they are better able to hide them. Another idea of “success” is based upon the individual’s appropriate use of emotion even when incapable of fully understanding it. For example, some successful psychopathic individuals are able to avoid causing harm to others because they may be more adept at correcting their inability to interpret emotion, a mechanism that psychopathic individuals high in secondary psychopathy do not possess
(Sifferd & Hirstein, 2013). Gao and Raine (2010) posited that successful psychopathic individuals have average or above-average decision-making and emotion-regulating capabilities, which prevents them from acting out aggressively. As this subset of psychopaths possesses greater skill in being able to avoid detection, they are also often referred to as community psychopaths.
Evidence to support this claim comes from a study conducted by DeMatteo,
Heilbrun, and Marczyk (2006) in which community participants were found to have significantly higher Factor 1 than Factor 2 scores. As such, one plausible theory of community psychopathy is that some non-incarcerated psychopathic individuals are likely to be “lower” on psychopathy (particularly secondary psychopathy) than their incarcerated counterparts, as incarcerated individuals may exhibit higher levels of psychopathy in part due to the violent nature of imprisonment. This “lowered” psychopathy is reflected by a lower score, but does not explain whether the score arises 9 from displaying fewer features of psychopathy or if the features that are endorsed are of lower severity (i.e., answers are neutral instead of strongly agree/disagree). In addition to being able to avoid settings that may exacerbate the presence of psychopathy, psychopathic individuals in the community may be able to avoid engaging in criminal behavior due to their intelligence. Intelligence has been found to moderate the relationship between psychopathy and criminality, such that individuals who are high in psychopathy and high in intelligence endorsed less criminal activity (Wall, Sellbom, &
Goodwin, 2013).
Machiavellianism, characterized by a lack of interpersonal affect in relationships, lack of concern with conventional morality, lack of acute psychopathology, and low ideological commitment, has been shown to be positively associated with primary psychopathy in a community sample (McHoskey, Worzel, & Szyarto, 1998). An additional unique feature of primary psychopaths is their narcissism (McHoskey et al.,
1998). Research also suggests that psychopathic individuals who are high in primary psychopathy display greater emotional deficits, but that their enhanced impulse control allows them to remain undetected (Ross, Benning, & Adams, 2007).
Conversely, Ross, Lutz, and Bailley (2004) postulated that secondary psychopaths are more likely to be plagued by poor organizational skills and distress and that they do not possess the skills to keep maladaptive traits and behaviors in check. Dean et al.’s
(2013) study, in which results suggested that secondary psychopathy is linked to making risky choices even when presented with all the risk and reward outcomes, supports this hypothesis. Executive dysfunction is also associated with secondary psychopathy (Ross,
Benning, & Adams, 2007). Secondary psychopathy appears to be more environmentally 10 based, which suggests that this subset of psychopathic individuals has a greater chance of recovery in therapy (Skeem, Poythress, Edens, Lilienfeld, & Cale, 2003).
Psychopathic individuals in the community do share a number of characteristics, one of which is possessing riskier driving attitudes than non-psychopaths (Lee & Salekin,
2010). Further, Belmore and Quinsey (1994) found that participants higher in psychopathy (using a cut-off score of 18 on the PCL-R) played a higher number of standard playing cards in a computerized experiment than non-psychopaths but did not lose significant amounts of money doing so. Participants were shown either a face card
(ace, king, queen, or jack) or a number card (from 2 to 10) and either gained or lost money, respectively, as the probability of winning decreased as they continued to play cards. They had the option to continue playing or stop and “collect” their winnings at any point (Belmore & Quinsey, 1994). This suggests that those higher on psychopathy overall may possess better adaptation and adjustment skills than low-psychopathy individuals, which lays in contradiction to an earlier characterization of “success” in psychopathy.
The discrepancies among the available literature indicate a need to further study the constructs. This includes parsing out the differences in particular tasks and attributing them to either primary or secondary psychopathy. Lee and Salekin (2010) reported that both subgroups of psychopathy were more likely to engage in risky and criminal activity.
However, despite these commonalities, the need for a more formal distinction in psychopathy is necessary as research indicates a clear distinction between the two subgroups in both their behaviors and attitudes.
As community-based psychopathic individuals lay between non-psychopaths and incarcerated psychopaths on the psychopathy continuum, the distinction between non- 11 psychopaths and psychopathic individuals in the community is not as apparent as the one between non-psychopaths and incarcerated psychopaths. This is particularly true if the actions of the psychopathic individual are not seen as overtly negative or dangerous. If low emotionality presents itself as a callous and uncaring trait, that characteristic is less striking than that of an individual who is more hostile and aggressive. Therefore, the lines of expected variations in personality traits and psychopathy blur when referring to psychopathic tendencies that are based in primary psychopathy. However, although the differences may be smaller they are still able to make a noticeable impact in the social and/or occupational functioning of an individual. For example, community-based psychopathic individuals have also been referred to as “corporate” psychopaths if they are able to use their manipulative and conning skills to get ahead in the workplace
(Boddy, 2013).
1.3: Corporate Psychopathy
An emerging field is that of corporate psychopathy, in which individuals with advantageous psychopathic characteristics are able to make calculated moves to advance in the workplace. Because those displaying corporate psychopathy are not engaging in criminally psychopathic behavior, the seemingly positive personality traits they possess may be characteristics employers seek. An in-depth analysis of the research conducted on corporate psychopaths revealed that the subset of psychopaths who work in white-collar positions and whose primary motivation is money-related is unique from the other types of psychopathy (Chiaburu, Muñoz, & Gardner, 2012).
These individuals are more likely to have characteristics that are closely tied to primary psychopathy, such as being egocentric, pathological liars, charming, conning, 12 narcissistic, patronizing towards others, and having a sense of entitlement (Perri, 2013).
Because they possess better cognitive functions, they are more easily able to deflect responsibility off their own actions. Rationalization plays a large role in how they are able to justify their behavior (Perri, 2013). Although workplace psychopathy may be beneficial in certain regards, it does not go unnoticed by employees who come into contact with this type of psychopathic individual (Boddy, 2011, 2013). In a preliminary study by Boddy (2013), white-collar employees took online surveys about their colleagues, revealing that employees who worked in the presence of a corporate psychopath display counterproductive work behaviors and intentionally decrease productivity due to an increase in the negative emotions felt by those in contact with the psychopathic individual. This brings forward the idea that even though psychopathic individuals may not physically harming those surrounding them, the behaviors they exhibit are upsetting to their colleagues. Thus, the effects of the psychopathic personality in the workplace may be more covert, but may indeed—at least preliminarily—reflect a
“successful” type of psychopathy nonetheless.
Psychopathy in the workplace is a double-edged sword in that it may be adaptive in certain circumstances but harmful in others (Smith & Lilienfeld, 2013). Emotional intelligence is traditionally considered a prosocial trait; however, the “dark side” of emotional intelligence is the ability to emotionally manipulate other people (Grieve &
Panebianco, 2013). An example of these manipulative tendencies exhibits itself in the fraudulent nature of psychopathic individuals, where to make gains in the workplace, they engage in behaviors that appear to endorse company development or corporation advancement but instead are false indicators of good workmanship (Chiaburu et al., 13
2012). They are also less likely to engage in social entrepreneurship, which is likely due in part to the lack of personal gain made by assisting in the community (Akhtar,
Ahmetoglu, & Chamorro-Premuzic, 2013).
2: Impulsivity
Impulsivity, generally defined as the inability to inhibit behavior when necessary or to delay gratification (Cherek et al., 1997), is a multi-faceted construct that has received a great deal of empirical attention. Clinical disorders that are traditionally associated with impulsivity include Attention-Deficit/Hyperactivity Disorder, substance use disorders, and Antisocial Personality Disorder (Dalley, Everitt, & Robbins, 2011).
Although a considerable amount of research has been conducted on impulsivity, much of it in the forensic field has been limited to impulsive aggression as it is easy to measure
(Moeller et al., 2001).
Because impulsivity presents in numerous ways, researchers have devised multiple ways to measure the construct. Generally, impulsivity is separated into three domains: impulsive action, impulsive choice, and reflection impulsivity. Impulsive action, also known as motor impulsivity, refers to the tendency to fail to withhold a motor response (Brevers et al., 2012) and is traditionally assessed using the Stop Signal Task
(SST; Dalley et al., 2011). The Delay Discounting Task (DDT) is often utilized to measure impulsive choice or “cognitive impulsivity” (Dalley et al., 2011), which involves uncharacteristic levels of delay aversion (Brevers et al., 2012). Reflection impulsivity refers to the inability to collect and process information before making a decision and is evaluated using the Information Sampling Task (Lawrence, Luty, Bogdan, Sahakian, &
Clark, 2009). However, the measurement of impulsivity is not restricted to only 14 behavioral tests. One of the most frequently utilized measures of impulsivity is the self- report Barratt Impulsiveness Scale (BIS-11) developed by Patton, Stanford, and Barratt
(1995).
Several studies investigating the role of impulsivity have discovered that different measures of impulsivity understandably measure different aspects of the construct.
Studies evaluating multiple measures of impulsivity shown that the relationships between behavioral and self-report measures are weak (Broos et al., 2012; Franken & Muris,
2005; Moeller et al., 2002; Vigil-Colet, 2007). This is important to note as it reflects difficulties for researchers to reach a consensus on the definition of impulsivity because it is not a unitary construct and rather made up of several separate underlying processes
(Reynolds, Ortengren, Richards, & De Wit, 2006). Enticott and Ogloff (2006) prefer to define impulsivity as an outcome that is a result of numerous behavioral processes rather than as a construct, which is distinct from the definitions offered by most other researchers. An alternate explanation offered by Winstanley, Eagle, and Robbins (2006) suggests impulsivity can be separated into a set of behavioral subtypes that are anatomically different.
Behavioral measures like the DDT and SST are considered to tap into state impulsivity, while self-report measures such as the BIS-11 measure more trait-related aspects of impulsivity (Broos et al., 2012). There are advantages and drawbacks associated with both types of tools, as self-report measures rely on the veracity of the participant but allow researcher to gather data on long-term behaviors, while behavioral measures lack the long-term component but offer unbiased results as participants cannot lie on those tests (Moeller et al., 2001). It is crucial to capture data on the various forms 15 of impulsivity since it can manifest in a number of ways in individuals who display impulsive behaviors. Since multiple measures (e.g., the SST, DDT, and BIS-11) are able to capture the construct of impulsivity, it is important that researchers use as many tools as are available so that the separate components of impulsivity can be assessed individually. The best technique to assess for impulsivity is to use a multi-measure approach that includes utilizing multiple definitions of the construct and by also using the most valid and reliable tests that capture those different definitions.
2.1: Impulsivity and Neuropsychology
Researchers have increased efforts to discover the etiology of impulsivity. In both human and rodent studies, it has been suggested that the nucleus accumbens plays a role in impulsivity, particularly in the domain of impulsive action (Basar et al., 2010). The orbitofrontal cortex (OFC) is an additional region implicated in impulsive choice, though conflicting results have arisen from these studies. Some studies have reported that OFC lesions increase preference for the delayed reward in rodents (Winstanley et al., 2004).
Others found that rat subjects instead were more likely to choose immediate and certain rewards (Mobini et al., 2002).
Impulsive action is considered to have a distinct neural basis from impulsive choice as its focus is on the inability to suppress an automatic response. The inferior frontal gyrus has been theorized to have some control over this particular form of impulsivity in humans (Aron et al., 2003), particularly in the right hemisphere (Garavan,
Ross, & Stein, 1999). Some researchers go as far as to define a “stop circuit” in the brain that includes regions such as the right-hemisphere presupplementary motor area, inferior frontal gyrus, and a projection to the subthalamic nucleus that are involved in the Stop 16
Signal Task (Aron et al., 2007). This theory has been met with some controversy as the left frontal cortex does also show activation during the SST (Dalley et al., 2011). These findings suggest that there is a clear distinction between impulsive choice and impulsive action in terms of neuroanatomy and brain activation, and highlight the need to include both forms of impulsivity in terms of defining and measuring the construct.
2.2: Impulsivity and Psychopathy
Research suggests that a relationship between psychopathy, risk-taking, and impulsivity exists (Blackburn & Maybury, 1985; Damasio, Tranel, & Damasio, 1990;
Mitchell, Colledge, Leonard, & Blair, 2002). Although some researchers consider impulsivity and psychopathy to be the same construct (Blackburn & Coid, 1998;
Blaszczynski, Steel, & McConaghy, 1997), other researchers urge that the constructs be separated as the relationship between psychopathy and impulsivity is not always present
(Poythress & Hall, 2011). Results from LaPierre, Braun, and Hodgins’ (1995) study revealed that criminals high on psychopathy performed worse on neurocognitive tasks than criminal non-psychopaths. Researchers have also discovered that the distinction between primary and secondary psychopathy in incarcerated samples can be made using impulsivity as a distinguishing factor (Ray et al., 2009).
An early study that examined the relationship between neuropsychological behavioral performance and psychopathy in a community sample found that the affective-interpersonal factor of psychopathy was positively associated with executive cognitive functioning, while the social deviance factor was negatively associated with it and response inhibition (Sellbom & Verona, 2007). Although the factor scores used by
Sellbom and Verona (2007) do not fully break down in representing primary and 17 secondary psychopathy as do the factors of the PCL-R, there are enough similarities to suggest that there is a distinct neuropsychological difference between the two subgroups of psychopathy. A related study determined that primary psychopaths exhibited reduced impulsivity related to acting without thinking and planning for the future compared to secondary psychopathy. This suggests that impulsivity is not automatically raised in all psychopathic populations and when it is, it manifests itself in different situations according to the stronger subtype of psychopathy (Snowden & Gray, 2011).
A study by Hunt, Hopko, Bare, Lejuez, and Robinson (2005) examined the relationship between impulsivity and psychopathy in a non-forensic sample using the
Balloon Analogue Risk Task (BART). Researchers found that there was a small correlation between the behavioral task and self-report impulsivity. Primary findings revealed that higher psychopathy scores significantly predicted increasing risk-taking on the BART (Hunt et al., 2005). Morgan, Gray, and Snowden (2011) conducted a similar study, looking at the relationship between psychopathy and impulsivity in a community sample. They reported that a correlation between the BIS-11 and psychopathy existed, but that behavioral measures were not correlated with psychopathy (Morgan et al., 2011).
This current research is looking to determine if a relationship between psychopathy and impulsivity exists using multiple forms of measures of impulsivity (e.g., self-report, behavioral). Fowles and Dindo (2006) found that primary psychopathy-based individuals manifest the type of impulsivity in which they are willing to engage in risky activity even after taking the consequences of their actions into account.
18
3: Risk-taking
Floden and colleagues (2008) indicate that parsing out behaviors that either belong to impulsivity or risk-taking is difficult because true operational definitions of both constructs have not yet been developed. However, it appears that the most striking difference between risk-taking and impulsivity is that impulsivity involves decisions made as a result of poor inhibition, while risk-taking involves appraising the available options and deciding to proceed with a risky choice (Floden et al., 2008).
Using the Risk Taking Task, Ernst et al. (2002) found that there is an existing decision-making neural network, and that different brain regions become activated when participants are guessing on items versus being actively involved in the decision-making process. This aids in distinguishing brain regions that are related to risk-taking, namely the orbital and dorsolateral prefrontal cortex, anterior cingulate, insula, inferior parietal cortex, and thalamus (Ernst et al., 2002). Although a number of these regions have also been implicated in impulsivity, the distinct structures put forth as being active during the commission of an impulsive act in this study further aid to separate the two constructs.
Although the relationship between risk-taking and psychopathy is not as well researched as that between impulsivity and psychopathy, the research that has been conducted thus far offers differing results. Risk-taking as measured by the BART was not significantly associated with psychopathy in an offender sample (Swogger, Walsh,
Lejuez, & Kosson, 2010). Interestingly, a study by Hunt and colleagues (2005) makes use of the BART but refers to the measure as relating to impulsivity rather than risk-taking.
This example supports Floden et al.’s (2008) suggestion that the definitions of both risk- taking and impulsivity lack a true empirical basis. 19
4: Current Study
The current study examined the relationship between psychopathy and risk-taking with impulsivity as a moderating variable. As the tests that measure constructs such as impulsivity and risk-taking can overlap, this study makes a clear distinction as to which measure relates to which construct. It appears that no research has examined whether each subset of impulsivity moderates the relationship between psychopathy and risk- taking in a community sample. Total psychopathy was used in these analyses rather than separating into primary and secondary psychopathy because it offers a more complete picture of how psychopathic traits are manifested in a community population. While studying the moderating effect of impulsivity on risk-taking and factors of psychopathy would offer a more distilled perspective of the condition, it would not reflect the study’s aim to understand community psychopathy as a whole. Another aspect that was examined is whether impulsive action or impulsive choice plays a greater role in risk-taking for this particular population. Traditionally, this domain of research is more cognitive based, so this study is novel in its attempts to merge cognitive and forensic psychological research.
Because psychopathy research in non-forensic populations has only developed in recent years, there is a great deal more to learn about the subset of individuals who are below threshold for psychopathy according to measures like the PCL-R but may still exhibit characteristics of their incarcerated counterparts.
Furthering research in the domain of community psychopathy will help researchers better understand the construct and will aid in revealing more about those who primarily display non-criminal aspects of the personality disorder. Because there is a great deal of available literature on psychopathy in offender populations, this study has 20 the potential to help researchers better understand the similarities and differences between psychopathic individuals in the community and those who have been processed through the criminal justice system by offering novel information about psychopathic individuals in the community. If impulsivity contributes to the behavioral tendencies to commit crime, there may be a sizeable difference in impulsivity scores of incarcerated compared to non-incarcerated individuals, even if both types of individuals would be considered psychopathic according to their personality characteristics. Therefore, participants in this study may exhibit levels of impulsivity that are lower than their forensic counterparts, but are still higher than non-psychopathic individuals.
4.1: Hypotheses
Specific hypotheses included:
(1) Impulsive choice will significantly moderate the relationship between
psychopathy and risk-taking.
(2) Impulsive action will significantly moderate the relationship between
psychopathy and risk-taking.
(3) Trait impulsivity will significantly moderate the relationship between
psychopathy and risk-taking.
(4) Risk-taking will be significantly predicted by participants’ age, gender,
ethnicity, race, education level, employment status, impulsivity, and psychopathy.
(5) There will be a positive correlation between impulsivity and secondary
psychopathy.
(6) Impulsive choice will account for more variance in risk-taking than impulsive
action. 21
5: Methods
5.1: Participants
One hundred and twenty five participants were recruited using Amazon
Mechanical Turk (MTurk). A power analysis was calculated using G*Power 3.1 (Faul,
Erdfelder, Buchner, & Lang, 2009). A medium effect size was chosen as there are no effect sizes reported in the literature and is the effect size typically used in novel research approaches. Using a medium effect size (.25) and an alpha level of .05, the results suggested 109 participants are needed to obtain adequate statistical power (.80) for the stepwise regression. Despite this model not being the primary analysis for this study, this test requires the greatest number of participants and therefore was used to calculate the number of participants needed for all the analyses to reach adequate power. However,
125 participants were recruited to account for those who do not successfully complete the measures on Mechanical Turk. Inclusion criteria included fluency in English and being between 18 and 45 years of age; the latter criterion was intended to limit any age-related decline in cognitive performance for the behavioral tasks. The only exclusion criterion included incarceration following a criminal conviction. As identifying information was not collected from participants and risk was minimal, we applied for a waiver of informed consent from Drexel’s Institutional Review Board.
5.2: Procedures
This study examined whether impulsivity moderates the relationship between psychopathy and risk-taking. Because MTurk is a website on which participants sign up, the sample was not truly random. Instead, participants who met qualifications for the study self-selected based on their interest. After participants consented to participate in 22 the study, they were directed to follow a hyperlink to self-administer the study on
Qualtrics. Qualtrics has the ability to limit participants to only taking the study once.
Additionally, participants were asked to create a unique password that they entered on both Qualtrics and MTurk upon completion of the study to receive compensation.
After following the hyperlink from MTurk to Qualtrics, participants completed a demographic questionnaire, and then completed the remaining measures in the order of most to least laborious and time-intensive. After concluding the demographic questionnaire, participants then completed the Stop Signal Task, Balloon Analogue Risk
Task, and Delay Discounting Task, and then completed the Levenson Self-Report
Psychopathy Scale and the Barratt Impulsiveness Scale. Once data collection was completed, statistical analyses were conducted to assess the hypotheses.
5.3: Materials
5.3.1: Behavioral Measures
Balloon Analogue Risk Task (BART; Lejuez et al., 2002). This is a computerized behavioral measure of risk-taking that uses realistic rewards to demonstrate actual risk- taking. The BART is proprietary, programmed using the Inquisit Millisecond software package, and required no special permission to be used on MTurk. Fifteen balloons have to be inflated, one by one. Participants are given the option to either inflate the balloon
(one inflation at a time) or collect the earnings from that balloon. Each successful inflation adds $0.05 to the total earnings in a temporary bank, but also increases the risk of the balloon popping. If a balloon pops, the potential earnings (the money in the temporary bank) from that balloon are lost. With each inflation, the risk of popping increases (1/128, 1/127, 1/126, etc.). The screen gives no information about when each 23 balloon would pop, and to proceed to the next balloon, “Collect $$$” has to be pressed.
“Collect $$$” is only offered as an option if participants inflate the balloon at least once, and once it is chosen, the money is moved from the temporary bank to the permanent bank. Once participants have collected money from a particular balloon, there is no option to return to previous balloons. Participants do not receive compensation in the amount they earn during the session. Adjusted average pumps, calculated based on the average number of balloon pumps for balloons that do not explode, was used as the primary dependent measure. There are no norms for risk-taking behaviors in the BART.
It has an acceptable test-retest reliability for the adjusted average pumps variable (r =
0.77; White, Lejuez, & de Wit, 2008).
Stop Signal Task (SST; Verbruggen, Logan, & Stevens, 2008). This is a non- proprietary computer-administered, behavioral measure of impulsive action programmed using the Inquisit Millisecond software package. It required no special permission to be used on MTurk. There are four blocks during this task: one practice block consisting of
32 trials, and three separate blocks of 32 trials. There is a 10-second break between each block. On each trial, participants see on the screen either an arrow pointing to the left or an arrow pointing to the right. There is a 2,000 millisecond break between each trial.
Their task is to respond as fast and accurate as possible to these go stimuli, which involves pressing the “D” key with the left index finger when the arrow is pointing to the left and pressing the 'K' key with the right index finger when the arrow is pointing to the right. The stimulus remains on the screen until participants respond or 1,250 milliseconds pass. On occasion, the stimulus (i.e., arrow pointing either left or right) is followed by a sound, indicating that participants should inhibit their response on that trial. Participants 24 begin the stop-signal trials with 250 milliseconds in between the stimulus and presentation of the stop signal. If participants successfully inhibit their responses, the delay increases by 50 milliseconds; if they are unsuccessful, the delay decreases by 50 milliseconds. If participants wait for a stop signal to occur, the computer delays presenting the stop signals. The mean stop signal reaction time in stop signal trials was examined. There are no norms for action-based impulsivity with the SST.
Delay Discounting Task (DDT; Cherek et al., 1997). This is a behaviorally based laboratory task used to assess impulsive choice. Participants are given two response options on a computer screen: an impulsive response option (shown as “A” onscreen) and a self-control response option (shown as “B” onscreen) in two separate blocks. One is a training block that contains 10 trials to familiarize participants with the procedure, and then the other block with thirty trials is administered. During the first half of the training session, participants are shown five separate trials where the letter A is presented onscreen and the participant is instructed to click on the “A” when it begins to flash after
5 seconds. After correctly clicking on “A”, the letter disappears and $0.05 is added to a permanent bank. During the second half of the training session, participants are presented with the letter B onscreen and instructed to click on “B” when it begins to flash after 15 seconds. After correctly clicking on “B”, the letter disappears and $0.15 is added to a permanent bank. This training session exposes participants to the different monetary amounts and delays associated with “A” and “B” letters.
During the second block, both the “A” and “B” letters appear on the screen at the beginning of each of the 30 trials. Participants are now given an option to choose either
“A” or “B”; once they choose a letter, the other disappears. After either a 5- or 15-second 25 delay, the remaining letter begins to flash, and a second click on it adds $0.05 or $0.15 to a permanent bank depending on the chosen letter. The letter choice in the previous trial determines how the subsequent timing of the “B” choice is administered. Each time participants choose “B,” the next delay for that letter increases by 2 seconds. The opposite also holds true; a choice of “A” decreases the delay by 2 seconds. For example, if a participant selects the “B” option after waiting 15 seconds, the subsequent selection of the “B” option would require waiting 17 seconds in order for money to be deposited into the permanent bank. The delay for option “B” is never less than 7 seconds, and therefore is never equal to or less than impulsive response option “A”. Participants did not receive compensation in the amount they earned during the session. The variable of interest was the percent of impulsive choices participants made only in the second block.
Participants were considered impulsive if greater than 50% of their choices were impulsive.
5.3.2: Self-Report Measures
Levenson Self-Report Psychopathy Scale (LSRP; Levenson et al., 1995). This is a
26-item self-report instrument used to measure psychopathy in non-incarcerated populations. It is non-proprietary, programmed using the Inquisit Millisecond software package, and required no special permission to be used on MTurk. The LSRP has two subscales, primary and secondary. The primary subscale is comprised of 16 items that are primarily personality-based, while the secondary subscale is comprised of 10 items focusing more on antisocial behaviors. The LSRP yields the primary psychopathy score and the secondary psychopathy score, which combine to yield the Total score. The Total score, which could have been a maximum of 104, was used for most analyses in this 26 study. Each item is rated on a 4-point Likert scale, with the following choices: “agree strongly,” “agree somewhat,” “disagree somewhat,” and “disagree strongly.” The LSRP has demonstrated good internal consistency for the total score (Cronbach’s alpha = 0.82) and both subscales (primary subscale Cronbach’s alpha = 0.83, secondary subscale
Cronbach’s alpha = 0.71; Falkenbach, Poythress, Falki, & Manchak, 2007).
Barratt Impulsiveness Scale (BIS-11; Patton et al., 1995). This is a widely used self-report questionnaire composed of 30 items, some of which are reverse-scored, that measures trait-like impulsive behaviors and preferences. It is non-proprietary, programmed using the Inquisit Millisecond software package, and required no special permission to be used on MTurk. Items are scored on a 4-point Likert scale, with 1 = rarely/never, 2 = occasionally, 3 = often, 4 = almost always/always. As impulsivity is multi-faceted, the BIS-11 also has three second-order factors: attentional (poor concentration), motor (acting without thinking), and non-planning (lacking in future plans) impulsivity. After reviewing a number of studies utilizing the BIS-11, Stanford and colleagues (2009) determined that total scores less than 52 suggest an overly- controlled or dishonest individual, 52 to 71 is considered a normal range, and scores over
72 indicate high impulsivity. The BIS-11 has good test–retest reliability (r = 0.83;
Stanford et al., 2009).
Demographic Questionnaire (see Appendix for complete questionnaire).
Participants were asked to complete a demographic questionnaire to identify their gender, race, ethnicity, age, highest education level, current employment, and past criminal activity, including activity where respondents were not caught/arrested. Participants who were not fluent in English, did not meet the age requirement of being between 18 to 45 27 years old, and reported being incarcerated following a conviction were excluded from final analyses.
6: Statistical Analyses
To investigate the relationship between impulsivity, psychopathy, and risk-taking, an alpha level of 0.05 was utilized for all analyses. As some statistical tests required comparing scores from two groups, participants were split into low and high psychopathy
(using a median split), and preliminary Chi-square analyses examined whether there were between-group differences based on gender, race, ethnicity, employment status, and education level. An independent t-test was used to compare participants’ age in the two psychopathy groups. Additionally, bivariate correlations were conducted between psychopathy and factors, risk-taking, impulsivity and factors (as the BIS-11 has three second-order factors that are also variables of interest), and various demographic characteristics to assess if a relationship exists between those variables.
(1) Impulsive choice will significantly moderate the relationship between
psychopathy and risk-taking.
This hypothesis was evaluated using a hierarchical multiple regression that
tested whether impulsive choice had a significant moderating effect on the
relationship between continuous total psychopathy scores and average
adjusted balloon pumps. Block 1 included psychopathy total score and
percentage impulsive choices. Block 2 included the two-way interaction
term for psychopathy and impulsive choice.
(2) Impulsive action will significantly moderate the relationship between
psychopathy and risk-taking. 28
This hypothesis was evaluated using a hierarchical multiple regression that
tested whether impulsive action had a significant moderating effect on the
relationship between continuous total psychopathy scores and average
adjusted balloon pumps. Block 1 included psychopathy total score and
mean stop-signal reaction time. Block 2 included the two-way interaction
term for psychopathy and impulsive action.
(3) Trait impulsivity will significantly moderate the relationship between psychopathy and risk-taking.
This hypothesis was evaluated using a hierarchical multiple regression that
tested whether trait impulsivity had a significant moderating effect on the
relationship between continuous total psychopathy scores and average
adjusted balloon pumps. Block 1 included psychopathy total score and
total BIS-11 score. Block 2 included the two-way interaction term for
psychopathy and trait impulsivity.
(4) Risk-taking will be significantly predicted by participants’ age, gender, race, ethnicity, education level, employment status, impulsivity, and psychopathy.
This hypothesis was evaluated using a stepwise multiple regression with
age, gender, race, ethnicity, education level, employment status, total
impulsivity score, percentage impulsive choices, mean stop-signal reaction
time, and total psychopathy score being entered as the independent
variables and average adjusted balloon pumps as the dependent variable.
All the independent variables were entered into the model at the same
time. 29
(5) There will be a positive correlation between impulsivity and secondary
psychopathy.
This hypothesis was evaluated using multiple Pearson bivariate
correlations between total impulsivity scores for each of the three
impulsivity measures and secondary psychopathy scores.
(6) Impulsive choice will account for more variance in risk-taking than impulsive
action.
This hypothesis was evaluated using a multiple regression model. One
multiple regression utilized average adjusted balloon pumps as the
dependent variable, with percentage impulsive choices representing
impulsive choice and mean stop-signal reaction time representing
impulsive action as the independent variables. The standardized
coefficient results gave an indication of which impulsivity subtype
accounted for more variance in risk-taking.
7: Results
7.1: Preliminary Analyses
Two hundred and forty five participants began the study on Amazon Mechanical
Turk and completed the demographic questionnaire. Only 127 completed the demographic questionnaire and the Inquisit battery of tests, which included the SST,
DDT, BART, BIS-11, and LSRP. Twenty three participants were removed because they had missing data for at least one of the measures, 2 participants were removed because they had endorsed being incarcerated following a conviction, 12 participants were removed as they entered ages higher than 45, and 1 participant was removed due to 30 duplicate data, which resulted in a final sample of 89 participants with complete data. The mean age of participants was 33.11 years ( SD = 6.49). There were 44 males and 45 females in the sample. Demographic characteristics of the sample are displayed in Table
4. Those 89 participants were included in the following analyses.
A median split to separate participants into low and high psychopathy was used to determine if there were any statistically significant differences between the two groups on various demographic characteristics. As the LSRP does not have a cut-off score for psychopathy, a median split was used so no participants would have to be excluded from the study. An alpha level of 0.05 was used for all analyses. The median psychopathy score was 46, so participants who scored 45 or lower on the LSRP were in the “low psychopathy” group (N = 44), whereas those who scored 46 or higher were in the “high psychopathy” group (N = 45). Scores on the LSRP ranged from 29 to 77, with the maximum score being 104.
To compare the age of participants in the high psychopathy group to those in the low psychopathy group, an independent samples t-test was conducted. Levene’s test was not significant, so equal variances were assumed. Results indicate that there was no significant difference in age in low ( M = 34.02, SD = 5.95) versus high ( M = 32.22, SD =
6.93) psychopathy groups ( t[87] = 1.31, p = 0.19, d = 0.28). Chi-square analyses were used to compare participants on a variety of demographic characteristics, which included gender, race, education level, and employment status. Between-groups analyses revealed that there were no significant differences in race (χ 2[5] = 3.59, p = 0.61, V = 0.20), ethnicity (χ 2[1] = 2.96, p = 0.09, V = 0.18), employment status (χ 2[3] = 0.20, p = 0.98, V
= 0.05), and education level (χ 2[3] = 6.88, p = 0.08, V = 0.28) across the high and low 31 psychopathy groups. There was a significant difference in gender (χ 2[1] = 4.06, p = 0.04,
V = 0.21) across the high and low psychopathy groups. For more information on the descriptive statistics of the personality and behavioral measures used in this study, see
Tables 2 and 3.
7.2: Hypothesis Testing
Three hierarchical multiple regressions were conducted to determine if different forms of impulsivity moderate the relationship between psychopathy and risk-taking. The assumption of normality was violated for the dependent variable, as risk-taking was positively skewed, so a square root transformation was conducted. For the moderation analyses, the transformed dependent variable was used.
The first hierarchical multiple regression was performed to determine whether the interaction between psychopathy and impulsive choice (as measured by the DDT) significantly predicts risk-taking after controlling for psychopathy and impulsive choice.
Preliminary analyses were conducted to ensure no violation of the assumptions of multicollinearity, normality, linearity, and homoscedasticity. Two outliers were removed from this analysis at this time (case IDs: 2 and 50) using Cook’s distance, leverage, and the scatterplot of the regression leverage and the studentized deleted residuals. In the first step of hierarchical multiple regression, two predictors were entered: psychopathy and impulsive choice. This model was not statistically significant ( F[2, 84] = 0.13, p = 0.88) and explained 0.3% of the variance in risk-taking. After entry of the interaction term at
Step 2, the total variance explained by the model was 1.1% ( F[1, 83] = 0.96, p = 0.33) and the overall model remained non-significant. The standardized beta weight for the interaction term was -0.11. 32
The second hierarchical multiple regression was performed to determine whether the interaction between psychopathy and impulsive action (as measured by the SST) significantly predicts risk-taking after controlling for psychopathy and impulsive action.
Preliminary analyses were conducted to ensure no violation of the assumptions of multicollinearity, normality, linearity, and homoscedasticity. In the first step of hierarchical multiple regression, two predictors were entered: psychopathy and impulsive action. This model was not statistically significant ( F[2, 86] = 0.93, p = 0.40) and explained 2.1% of the variance in risk-taking. After entry of the interaction term at Step
2, the total variance explained by the model was 2.7% ( F[1, 85] = 0.53, p = 0.47) and the overall model remained non-significant. The standardized beta weight for the interaction term was 0.08.
The third hierarchical multiple regression was performed to determine whether the interaction between psychopathy and self-report trait impulsivity (as measured by the
BIS-11) significantly predicts risk-taking after controlling for psychopathy and trait impulsivity. Preliminary analyses were conducted to ensure no violation of the assumptions of multicollinearity, normality, linearity, and homoscedasticity. Two outliers were removed from this analysis at this time (case IDs: 2 and 33) using Cook’s distance, leverage, and the scatterplot of the regression leverage and the studentized deleted residuals. In the first step of hierarchical multiple regression, two predictors were entered: psychopathy and trait impulsivity. This model was not statistically significant ( F[2, 84] =
0.28, p = 0.76) and explained 0.7% of the variance in risk-taking. After entry of the interaction term at Step 2, the total variance explained by the model increased to 6.3%
(F[1, 83] = 5.59, p = 0.02), but the overall model did not significantly predict risk-taking 33
(F[3, 83] = 2.06, p = 0.11). The standardized beta weight for the interaction term was -
0.26.
7.3: Exploratory Analyses
A Chi-square analysis was also conducted to determine if there were differences in psychopathy levels between individuals who had been arrested compared to those who had not. No significant difference was found (χ 2[1] = 2.12, p = 0.15, V = 0.15).
A stepwise multiple regression using stepwise entry was conducted to evaluate whether participants’ age, gender, race, ethnicity, education, employment status, impulsivity, and psychopathy significantly predicted risk-taking. Although the dependent variable was non-normal, the stepwise regression was able to handle the skewed variable and therefore no transformations were conducted. All variables were entered at the same time. At step 1, gender entered into the regression equation and was significantly related to risk-taking, r 2change = 0.07, F[1, 87] = 6.13, p = 0.02. No other independent variables entered into the equation at step 2 of the analysis. The standardized beta weight for gender was -0.26.
Trait impulsivity and secondary psychopathy were significantly correlated, r =
0.69, p < 0.001. Impulsive action was not significantly correlated to secondary psychopathy, r = -0.20, p = 0.06, and neither was impulsive choice, r = 0.13, p = 0.23.
Other significant correlations included those between trait impulsivity and impulsive choice ( r = 0.26, p < 0.05), and trait impulsivity and impulsive action ( r = -0.23, p <
0.05). For a full listing of the correlations between the various measures, see Table 1.
A multiple regression was conducted to determine whether impulsive choice or impulsive action accounts for more variance in risk-taking. Although the dependent 34 variable was non-normal, the multiple regression was conducted for unique purposes – to assess which subset of behavioral impulsivity lends more variance in predicting risk- taking – and not to see whether impulsive choice or impulsive action predicted risk- taking was able to handle it and therefore no transformations were conducted. The independent variables, impulsive choice and impulsive action, were added into the regression model at the same time. The standardized beta weight for impulsive choice was -0.01, while the standardized beta weight for impulsive action was -0.12.
8: Discussion
This study investigated whether various subsets of impulsivity – impulsive choice, impulsive action, and trait impulsivity – moderated the relationship between risk- taking and psychopathy among community members. Because impulsivity is multidimensional, various tools measure its presence and tap into different facets of the construct. Therefore, it was important that multiple moderation analyses were conducted for the various forms of impulsivity. The findings suggest that there is little to no relationship between risk-taking and both psychopathy and impulsivity. Risk-taking was not correlated with any of the other behavioral measures, nor did it correlate to any self- report measures. The model that aimed to predict risk-taking with psychopathy and trait impulsivity had a marginal increase with the addition of the interaction term, but the overall model was not significant. This suggests that there may be a stronger relationship between trait impulsivity and risk-taking than there is with risk-taking and other domains of impulsivity, but it is a relatively weak link.
Correlations only existed between self-report psychopathy and self-report impulsivity. However, there was no correlation between primary psychopathy 35
(represented by the LSRP primary psychopathy score) and non-planning impulsivity, which may suggest that the presence of this form of impulsivity may help discern between primary and secondary psychopathy. Non-planning impulsivity relates to carefully planning out tasks and thinking cautiously, which may aid those higher in primary psychopathy to avoid detection or possibly even avoid engaging in criminal activity. Additional research is necessary before this conclusion can be drawn; however, this finding suggests a clear difference in the manifestation of self-reported impulsivity between primary and secondary psychopathy as represented by the LSRP.
There were no significant correlations between self-report psychopathy (including secondary psychopathy) and the behavioral measures of impulsivity. This, along with the multiple correlations between psychopathy and self-report impulsivity, suggests that if there is impulsivity present in individuals who have psychopathic traits, it is better captured using a self-report impulsivity measure rather than a behavioral measure.
Because psychopathy is a life-persistent personality disorder, it is expected that its manifestations of impulsivity would better be determined using a survey-based measure that also measures long-term impulsive behaviors rather than those that establish levels of impulsivity based on a short, one-time task (such as the SST and DDT).
Impulsive choice as measured by the DDT was positively correlated with total trait impulsivity and non-planning trait impulsivity, which makes sense in light of the fact that the DDT asks individuals to select between an impulsive option that offers a smaller reward or a more delayed option that gives them greater gains and has similarities to self- report measures of impulsivity. Additionally, the relationship between behaviorally 36 measured impulsive choice and trait-based non-planning impulsivity is expected because the two measures tap into overlapping domains of the impulsivity construct.
Interestingly, the third measure for impulsivity, the SST, is purely behavioral and was negatively correlated with the total BIS-11 measure and the motor impulsivity factor.
Because the SST is related to impulsive action, a positive correlation between self- reported motor impulsivity and behavioral motor impulsivity would be expected because they belong to the same domain of impulsivity. However, it is possible that individuals who display motor impulsivity are unaware of its long-term behavioral implications and thus do not endorse such items on a self-report measure.
The only characteristic out of participants’ age, gender, race, ethnicity, education level, employment status, impulsivity, and psychopathy that significantly predicted risk- taking was gender, which suggests that gender differences exist in the manifestation of risk-taking behaviors. Nonetheless, more research would need to be conducted on gender differences in psychopathic and risk-taking behaviors to substantiate such a claim. The findings in this study were quite similar to those by Morgan and colleagues (2011). They similarly found that few correlations existed between self-report psychopathy and behavioral measures of impulsivity. Overall, since there was a lack of significant findings for a majority of the hypotheses in this study, it suggests that the relationship between impulsivity, psychopathy, and risk-taking is not easily understood and requires further examination.
8.1: Limitations
This study had a number of limitations, one of which was that individuals were not pre-screened for having a minimal level of psychopathy before gaining access to this 37 study, which may have led to an overrepresentation of individuals who display very little or no psychopathy since the base rate of the personality disorder is around 1% among community members (Hare, 1999). As a low number of participants in this study were moderately or highly psychopathic, adding a pre-screening measure of psychopathy would help ensure that the individuals who were included in the study endorsed a minimal level of psychopathy because it is less likely that recruitment through Amazon
Mechanical Turk would draw in many moderately or highly psychopathic individuals.
Moreover, this survey was administered through an online survey platform and all participants were anonymously recruited. If participant data had been collected in-person, additional measures could have been added to the test battery. Additionally, the test battery was administered in the same order of measures for each participant. This was done so that participants would first complete the most intensive measures first and conclude with the survey-based tests that required fewer resources; however, it is possible that the two behavioral impulsivity measures primed participants to think about their impulsive tendencies before they took the self-report impulsivity measure.
Additionally, participants also did not earn the monetary amounts they won in the tasks (particularly, the BART and the DDT). For completing the study, participants received $1.00 in compensation. Because they were not actually incentivized by the possibility of earning more money, carefully selecting the option that would maximize earnings was unlikely to be have been a key priority and therefore their scores may not have reflected the true propensity to take risks and engage in impulsive behaviors.
Additionally, although this study was open to the community, only people who have
Amazon Mechanical Turk accounts were able to participate. If there is a systemic 38 difference in psychopathy, impulsivity, or risk-taking levels between those who have
MTurk accounts and those who do not, that reduces the generalizability of the study.
8.2: Directions and Conclusions
This study was unique in its attempt to study the behavioral components of individuals who exhibit psychopathy, because the focus of primary psychopathy research has placed more focus on the affective traits of the individual rather than the behavioral aspects. Following studies should focus on obtaining a larger number of participants and measuring psychopathy, impulsivity, risk-taking using various measures to determine whether certain measures are better at detecting underlying similarities between the three constructs. In this study, the BART was used to measure risk-taking. Measures like the
Risky Gains Task and the Iowa Gambling Task also assess for behavioral risk-taking.
Adding a self-report measure of risk-taking such as the Risk Taking Questionnaire in future studies would also allow researchers to determine if a relationship exists between trait risk-taking, trait impulsivity, and psychopathy. Both self-report and behavioral measures of various constructs offer important and unique information about that specific construct that helps portray a more thorough picture of how impulsivity or risk-taking are exhibited. The behavioral measure for impulsive choice, the DDT, indicated whether participants made more (greater than 50%) impulsive choices when they were carrying out that task. From that, a snapshot of their impulsivity level is shown. However, by including the BIS, the self-report measure of impulsivity, the long-term impulsive tendencies of the individual can be determined. Participants with discrepant scores can be examined more carefully; if we had relied on only one overall measure of impulsivity, their impulsivity levels could have been portrayed incorrectly. Additionally, sensation 39 seeking was not examined in this study, though prior research suggests a relationship exists between risk-taking behaviors and sensation seeking in a sample of undergraduate students (Horvath & Zuckerman, 1993).
Community psychopathy has only in recent years begun to receive attention in forensic psychology research, and the majority of its focus has remained on the intrapersonal and affective traits of individuals who exhibit psychopathic features. This study was original in its attempts to operationally define and study the construct of risk- taking along with the construct of impulsivity, including separating it into its multiple subsets. Replication of this study with a larger sample that focuses on recruiting individuals who have a sufficient level of psychopathy would aid in further understanding the behavioral tendencies, such as impulsivity and risk-taking, of those who present with primary psychopathy features. 40
List of References
1. Akhtar, R., Ahmetoglu, G., & Chamorro-Premuzic, T. (2013). Greed is good? Assessing the relationship between entrepreneurship and subclinical psychopathy. Personality and Individual Differences , 54 , 420-425.
2. American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington, DC: American Psychiatric Publishing.
3. Aron, A. R., Behrens, T. E., Smith, S., Frank, M. J., & Poldrack, R. A. (2007). Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI. Journal of Neuroscience , 27 , 3743-3752.
4. Aron, A. R., Fletcher, P. C., Bullmore, E. T., Sahakian, B. J., & Robbins, T. W. (2003). Stop signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nature Neuroscience , 6, 115-116.
5. Basar, K., Sesia, T., Groenewegen, H., Steinbusch, H. W., Visser-Vandewalle, V., & Temel, Y., (2010). Nucleus accumbens and impulsivity. Progress in Neurobiology , 92 , 533-557.
6. Belmore, M. F., & Quinsey, V. L. (1994). Correlates of psychopathy in a noninstitutional sample. Journal Of Interpersonal Violence , 9, 339-349.
7. Bjork, J. M., Chen, G., and Hommer, D. W. (2012). Psychopathic tendencies and mesolimbic recruitment by cues for instrumental and passively obtained rewards. Biological Psychology , 89 , 408-415.
8. Blackburn, R., & Coid, J. W. (1998). Psychopathy and the dimensions of personality disorder in violent offenders. Personality and Individual Differences , 25 , 129- 145.
9. Blackburn, R., & Maybury, C. (1985). Identifying the psychopath: The relation of Cleckley’s criteria to the interpersonal domain. Personality and Individual Differences , 6, 375-368.
10. Blair, R. J. (2008). The amygdala and ventromedial prefrontal cortex: Functional contributions and dysfunction in psychopathy. Philosophical Transactions of the Royal Society of London B: Biological Sciences , 363 , 2557-2565.
11. Blascovich, J., & Kelsey, R. M. (1990). Using cardiovascular and electrodermal measures of arousal in social psychological research. Review of Personality and Social Psychology , 11 , 45-73. 41
12. Blaszczynski, A. P., Steel, Z., & McConaghy, N. (1997). Impulsivity in pathological gambling: The antisocial impulsivist. Addiction , 92 , 75-87.
13. Boddy, C. R. (2011). Corporate psychopaths, bullying and unfair supervision in the workplace. Journal Of Business Ethics , 100 , 367-379.
14. Boddy, C. R. (2013). Corporate psychopaths, conflict, employee affective well- being and counterproductive work behaviour. Journal Of Business Ethics , 121 , 107-121.
15. Brevers, D., Cleeremans, A., Verbruggen, F., Bechara, A., Kornreich, C., Verbanck, P., & Noël, X. (2012). Impulsive action but not impulsive choice determines problem gambling severity. PLoS One , 7, e50647.
16. Broos, N., Schmaal, L., Wiskerke, J., Kostelijk, L., Lam, T., Stoop, N., ... & Goudriaan, A. E. (2012). The relationship between impulsive choice and impulsive action: a cross-species translational study. PloS One , 7, e36781.
17. Book, A., Costello, K., & Camilleri, J. A. (2013). Psychopathy and victim selection: The use of gait as a cue to vulnerability. Journal of Interpersonal Violence , 28 , 2368-2383.
18. Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS Scales. Journal of Personality and Social Psychology , 67 , 319-333.
19. Cherek, D. R., Moeller, F. G., Dougherty, D. M., & Rhoades, H. (1997). Studies of violent and nonviolent male parolees: II. Laboratory and psychometric measurements of impulsivity. Biological Psychiatry , 5, 523-529.
20. Chiaburu, D. S., Muñoz, G. J., & Gardner, R. G. (2012). How to spot a careerist early on: Psychopathy and exchange ideology as predictors of careerism. Journal of Business Ethics , 118 , 473-486.
21. Cleckley, H. M. (1941). The mask of sanity . St. Louis, MO: Mosby.
22. Crego, C., & Widiger, T. A. (2014). Psychopathy and the DSM. Journal of personality . doi: 10.1111/jopy.12115
23. Dalley, J.W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron , 69 , 680-694.
24. Damasio, A. R., Tranel, D., & Damasio, H. (1990). Individuals with sociopathic behavior caused by frontal damage fail to respond automatically to social stimuli. Behavioural Brain Research, 41 , 81-94.
25. Dean, A. C., Altstein, L. L., Berman, M. E., Constans, J. I., Sugar, C. A., & McCloskey, M. S. (2013). Secondary psychopathy, but not primary psychopathy, is 42 associated with risky decision-making in noninstitutionalized young adults. Personality and Individual Differences , 54 , 272-277.
26. DeMatteo, D., Heilbrun, K., & Marczyk, G. (2006). An empirical investigation of psychopathy in a noninstitutionalized and noncriminal sample. Behavioral Sciences and the Law , 24 , 133-146.
27. DeMatteo, D., Edens, J. F., Galloway, M., Cox, J., Smith, S. T., Koller, J. P., & Bersoff, B. (2014). Investigating the role of the psychopathy checklist–revised in united states case law. Psychology, Public Policy, and Law , 20 , 96-107.
28. Enticott, P. G., & Ogloff, J. R. P. (2006). Elucidation of impulsivity. Australian Psychologist , 41 , 3-14.
29. Ernst, M., Bolla, K., Mouratidis, M., Contoreggi, C., Matochik, J. A., Kurian, V., ... & London, E. D. (2002). Decision-making in a risk-taking task: A PET study. Neuropsychopharmacology , 26 , 682-691.
30. Falkenbach, D., Poythress, N., Falki, M., & Manchak, S. (2007). Reliability and validity of two self-report measures of psychopathy. Assessment , 14 , 341-350.
31. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods , 41 , 1149-1160.
32. Floden, D., Alexander, M. P., Kubu, C. S., Katz, D., & Stuss, D. T. (2008). Impulsivity and risk-taking behavior in focal frontal lobe lesions. Neuropsychologia , 46 , 213-23.
33. Fowles, D. C., & Dindo, L. (2006). A dual deficit model of psychopathy. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 14-34). New York: Guilford Press.
34. Franken, I. H. A., & Muris, P. (2005). Individual differences in decision-making. Personality and Individual Differences , 39 , 991-998.
35. Gao, Y., Glenn, A. L., Schug, R. A., Yang, Y., & Raine, A. (2009). The neurobiology of psychopathy: A neurodevelopmental perspective. Canadian Journal of Psychiatry , 54 , 813-823.
36. Gao, Y., & Raine, A. (2009). P3 event-related potential impairments in antisocial and psychopathic individuals: A meta-analysis. Biological Psychology , 82 , 199-210.
37. Gao, Y. & Raine, A. (2010). Successful and unsuccessful psychopaths: A neurobiological model. Behavioral Sciences & The Law , 28 , 194-210.
38. Garavan, H., Ross, T. J., & Stein, E. A. (1999). Right hemispheric dominance of inhibitory control: An event-related functional MRI study. Proceedings of the National Academy of Sciences, U.S.A. , 96 , 8301-8306. 43
39. Glenn, A. L., Raine, A., & Schug, R. A. (2009). The neural correlates of moral decision-making in psychopathy. Molecular Psychiatry , 14 , 5-6.
40. Grieve, R., & Panebianco, L. (2013). Assessing the role of aggression, empathy, and self ‐serving cognitive distortions in trait emotional manipulation. Australian Journal Of Psychology , 65 , 79-88.
41. Hare, R. D. (1991). Manual for the Hare Psychopathy Checklist-Revised . Toronto, ON: Multi-Health Systems.
42. Hare, R. D. (2003). The Hare Psychopathy Checklist-Revised (2nd edition). Toronto, ON: Multi-Health Systems.
43. Hare, R. D. (1999). Without conscience: The disturbing world of the psychopaths among us . New York: Guilford Press.
44. Hemphill, J. F., & Hare, R. D. (2004). Some misconceptions about the Hare PCL– R and risk assessment: A reply to Gendreau, Goggin, and Smith. Criminal Justice & Behavior , 31 , 203-243.
45. Hiatt, K. D., Schmitt, W. A., & Newman, J.P. (2004). Stroop tasks reveal abnormal selective attention among psychopathic offenders. Neuropsychology , 18 , 50-59.
46. Hicks, B. M., Bernat, E., Malone, S. M., Iacono, W. G., Patrick, C. J., Krueger, R. F., & McGue, M. (2007). Genes mediate the association between P3 amplitude and externalizing disorders. Psychophysiology , 44 , 98-105.
47. Hicks, B. M., Markon, K. E., Patrick, C. J., Krueger, R. F., & Newman, J. P. (2004). Identifying psychopathy subtypes on the basis of personality structure. Psychological Assessment , 16 , 276-288.
48. Horvath, P., & Zuckerman, M. (1993). Sensation seeking, risk appraisal, and risky behavior. Personality and Individual Differences , 14 , 41-52.
49. Hunt, M. K., Hopko, D. R., Bare, R., Lejuez, C. W., & Robinson, E. V. (2005). Construct validity of the balloon analog risk task (BART) associations with psychopathy and impulsivity. Assessment , 12, 416-428.
50. Karpman, B. (1948). The myth of the psychopathic personality. American Journal of Psychiatry , 104 , 523-534.
51. Kiehl, K. A., Hare, R. D., Liddle, P. F., & McDonald, J. J. (1999). Reduced P300 responses in criminal psychopaths during a visual oddball task. Biological Psychiatry , 45 , 1498-1507.
52. LaPierre, D., Braun, C. M. J., & Hodgins, S. (1995). Ventral frontal deficits in psychopathy: neuropsychological test findings. Neuropsychologia , 33 , 139-151. 44
53. Lawrence, A. J., Luty, J., Bogdan, N. A., Sahakian, B. J., & Clark, L. (2009). Impulsivity and response inhibition in alcohol dependence and problem gambling. Psychopharmacology , 207 , 163-172.
54. Lee, Z., & Salekin, R. T. (2010). Psychopathy in a noninstitutional sample: Differences in primary and secondary subtypes. Personality Disorders: Theory, Research, and Treatment , 1, 153-169.
55. Lejuez, C. W., Read, J. P., Kahler, C. W., Richards, J. B., Ramsey, S. E., Stuart, G. L., ... & Brown, R. A. (2002). Evaluation of a behavioral measure of risk taking: The Balloon Analogue Risk Task (BART). Journal of Experimental Psychology: Applied , 8, 75-84.
56. Levenson, M. R., Kiehl, K. A., & Fitzpatrick, C. M. (1995). Assessing psychopathic attributes in a noninstitutionalized population. Journal of Personality and Social Psychology , 68 , 151-158.
57. Lorber, M. F. (2004). Psychophysiology of aggression, psychopathy, and conduct problems: A meta-analysis. Psychological Bulletin , 130 , 531-552.
58. MacLeod, C. M. (1992). The Stroop task: The 'gold standard' of attentional measures. Journal of Experimental Psychology: General , 121 , 12-14.
59. Mitchell, D. G. V., Colledge, E., Leonard, A., & Blair, R. J. R. (2002). Risky decisions and response reversal: Is there evidence of orbitofrontal cortex dysfunction in psychopathic individuals? Neuropsychologia , 40 , 2013-2022.
60. McHoskey, J. W., Worzel, W., & Szyarto, C. (1998). Machiavellianism and psychopathy. Journal of Personality and Social Psychology , 74 , 192-210.
61. Mobini, S., Body, S., Ho, M. Y., Bradshaw, C. M., Szabadi, E., Deakin, J. F. & Anderson, I. M. (2002). Effects of lesions of the orbitofrontal cortex on sensitivity to delayed and probabilistic reinforcement. Psychopharmacology , 160 , 290-298.
62. Moeller, F. G., Barratt, E. S., Dougherty, D. M., Schmitz, J. M., & Swann, A. C. (2001). Psychiatric aspects of impulsivity. The American Journal of Psychiatry , 158 , 1783-1793.
63. Moeller, F. G., Dougherty, D. M., Barratt, E. S., Oderinde, V., Mathias, C. W., Harper, R. A., & Swann, A. C. (2002). Increased impulsivity in cocaine dependent subjects independent of antisocial personality disorder and aggression. Drug and Alcohol Dependence , 68 , 105-111.
64. Morgan, J. E., Gray, N. S., & Snowden, R. J. (2011). The relationship between psychopathy and impulsivity: A multi-impulsivity measurement approach. Personality and Individual Differences , 51 , 429-434. 45
65. Patrick, C. J., Bradley, M. M., & Lang, P. J. (1993). Emotion in the criminal psychopath: Startle reflex modulation. Journal of Abnormal Psychology , 103 , 82-92.
66. Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the barratt impulsiveness scale. Journal of Clinical Psychology , 51 , 768-774.
67. Perri, F. S. (2013). Visionaries or false prophets. Journal of Contemporary Criminal Justice , 29 , 331-350.
68. Pham, T.H., Vanderstukken, O., Philippot, P., & Vanderlinden, M. (2003). Selective attention and executive functions deficits among criminal psychopaths. Aggressive Behavior , 29 , 393-405.
69. Polich, J. (2003). Overview of P3a and P3b. In J. Polich (Ed.), Detection of change: Event-related potential and fMRI findings (pp. 83-98). Boston, MA: Kluwer.
70. Poythress, N. G., & Hall, J.R. (2011). Psychopathy and impulsivity reconsidered. Aggression and Violent Behavior , 16 , 120-134.
71. Raine, A., & Venables, P. H. (1988). Enhanced P3 evoked potentials and longer P3 recovery times in psychopaths. Psychophysiology , 25 , 30-38.
72. Ray, J., Poythress, N., Weir, J., & Rickelm, A. (2009). Relationships between psychopathy and impulsivity in the domain of self-reported personality features. Personality and Individual Differences , 46 , 83-87.
73. Reynolds, B., Ortengren, A., Richards, J. B., & De Wit, H. (2006). Dimensions of impulsive behavior. Personality and behavioral measures. Personality and Individual Differences , 40 , 305-315.
74. Ross, S. R., Benning, S. D., & Adams, Z. (2007). Symptoms of executive dysfunction are endemic to secondary psychopathy: An examination in criminal offenders and noninstitutionalized young adults. Journal of Personality Disorders , 21 , 384-399.
75. Ross, S. R., Lutz, C. J., & Bailley, S. E. (2004). Psychopathy and the five factor model in a noninstitutionalized sample: A domain and facet level analysis. Journal of Psychopathology and Behavioral Assessment , 26 , 213-223.
76. Ross, S. R., Moltó, J., Poy, R., Segarra, P., Pastor, M., & Montañés, S. (2007). Gray's model and psychopathy: BIS but not BAS differentiates primary from secondary psychopathy in noninstitutionalized young adults. Personality and Individual Differences , 43 , 1644-1655.
77. Sellbom, M., & Verona, E. (2007). Neuropsychological correlates of psychopathic traits in a non-incarcerated sample. Journal of Research in Personality , 41 , 276-294. 46
78. Sifferd, K. L., & Hirstein, W. (2013). On the criminal culpability of successful and unsuccessful psychopaths. Neuroethics , 6, 129-140.
79. Skeem, J. L., Poythress, N., Edens, J. F., Lilienfeld, S. O., & Cale, E. M. (2003). Psychopathic personality or personalities? Exploring potential variants of psychopathy and their implications for risk assessment. Aggression and Violent Behavior , 8, 513–546.
80. Smith, S., & Lilienfeld, S. O. (2013). Psychopathy in the workplace: The knowns and unknowns. Aggression and Violent Behavior , 18 , 204-218.
81. Snowden, R. J., & Gray, N. S. (2011). Impulsivity and psychopathy: Associations between the barrett impulsiveness scale and the psychopathy checklist revised. Psychiatry Research , 187 , 414-417.
82. 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.
83. Swogger, M. T., Walsh, Z., Lejuez, C. W., & Kosson, D. S. (2010). Psychopathy and risk taking among jailed inmates. Criminal Justice and Behavior , 37 , 439-452.
84. van Honk, J., & Schutter, D. J. L. G. (2006). Unmasking feigned sanity: A neurobiological model of emotion processing in primary psychopathy. Cognitive Neuropsychiatry , 11 , 285-306.
85. Verbruggen, F., Logan, G. D., & Stevens, M. A. (2008). STOP-IT: Windows executable software for the stop-signal paradigm. Behavior Research Methods , 40 , 479- 483.
86. Vigil-Colet, A. (2007). Impulsivity and decision-making in the balloon analogue risk-taking task. Personality and Individual Differences , 43 , 37-45.
87. Wall, T. D., Sellbom, M., & Goodwin, B. E. (2013). Examination of intelligence as a compensatory factor in non-criminal psychopathy in a non-incarcerated sample . Journal of Psychopathology and Behavioral Assessment , 35 , 450-459.
88. White, T. L., Lejuez, C. W., & de Wit, H. (2008). Test-retest characteristics of the balloon analogue risk task (BART). Experimental and Clinical Psychopharmacology , 16 , 565-570.
89. 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.
90. Winstanley, C. A., Theobald, D. E. H., Cardinal, R. N., & Robbins, T. W. (2004). Contrasting roles of basolateral amygdala and orbitofrontal cortex in impulsive choice. Journal of Neuroscience , 24 , 4718-4722. 47
91. Yang, Y., Raine, A., Lencs, T., Lacasse, L., & Colletti, P. (2005). Volume reduction in prefrontal gray matter in unsuccessful criminal psychopaths. Biological Psychiatry , 57 , 1109-1116. 48
Appendix A: Demographic Questionnaire
What is your age? ______
What is your gender?
ü Male
ü Female
What is your ethnicity?
ü Hispanic/Latino
ü Non-Hispanic/Latino
What is your race?
ü American Indian/Alaskan Native
ü Asian
ü Black or African American
ü Native Hawaiian or other Pacific Islander
ü White or Caucasian American
ü Other
49
What is the highest level of education you obtained?
ü < 12th grade
ü GED or high school diploma
ü 2-year college degree
ü 4-year college degree
ü Graduate degree
Have you ever been arrested?
ü Yes
ü No
If yes, please answer accordingly:
Number of adult arrests ______
Number of juvenile arrests ______
Age at first arrest ______
Have you ever been convicted of a crime?
ü Yes
ü No
50
If yes, please answer accordingly:
Number of felony convictions ______
Number of misdemeanor convictions ______
Have you ever been incarcerated following a conviction?
ü Yes
ü No
If yes, please answer accordingly:
How many times? ______
What was the longest sentence you received? ______
What was the shortest sentence you received? ______
Have you ever engaged in behavior that could have resulted in incarceration but you managed to avoid being caught?
ü Yes
ü No
51
If so, which behaviors apply:
ß Crimes against persons (e.g., murder, rape, aggravated assault, robbery)
ß Crimes against morality (e.g., selling or possessing drugs, prostitution, weapons
possession, illegal gambling)
ß Property crimes (e.g., theft, arson, burglary, property destruction)
ß White collar crimes (e.g., embezzling, tax evasion, insider trading)
ß Other (please specify) ______
ß Not Applicable
What is your current employment status?
ü Full-time
ü Part-time
ü Student
ü Not employed
What is your occupation? ______
Please list any prescription medications you are currently taking: ______52
Table 1. Correlations for personality and behavioral measures.
1 1 2 3 4 5 6 7 8 9 0 1. LSRP ------Total 2. LSRP .93*** ------Factor 1 3. LSRP .79*** .50*** ------Factor 2 4. BIS .50*** .29** .69*** ------Total 5. BIS .44*** .34** .47*** .75*** ------Motor 6. BIS Attentio .42*** .23* .59*** .75*** .34** - - - - - nal 7. BIS Non- .35** .14 .58*** .87*** .46*** .51*** - - - - planning 8. BART -.03 -.03 -.01 .06 .13 -.05 .04 - - - 9. DDT .06 .002 .13 .26* .08 .14 .35** .01 - - 10. SST -.06 .04 -.20 -.23* -.22* -.15 -.18 -.12 -.19 -
* p < 0.05, ** p < 0.01, *** p < 0.001
53
Table 2. Descriptive statistics for the LSRP and BIS-11 personality measures.
Personality measure [total possible score] Median Mean SD Min Max
LSRP Total [104] 46 46.97 11.29 29 77
LSRP Factor 1 [64] 27 29.21 8.05 19 50
LSRP Factor 2 [40] 18 17.75 4.85 10 30
BIS-11 total [120] 56 55.39 10.55 34 88
BIS-11 Motor [44] 20 20.08 4.15 11 34
BIS-11 Attentional [32] 14 14.3 3.82 8 25
BIS-11 Non-planning [44] 21 21.01 5.28 11 35
Note: Levenson Self-Report Psychopathy Scale; Barratt Impulsiveness Scale. 54
Table 3. Descriptive statistics for the behavioral measures.
Measure Median Mean SD Min Max
BART (adjusted average balloon pumps)* 14.29 16.48 11.25 1.33 49
DDT (percent impulsive choices) 0.47 0.45 0.29 0 1
SST (mean stop signal reaction time) a 293.21 283.37 67.04 160.75 474.69
Note: Balloon Analogue Risk Task; Delay Discounting Task; Stop Signal Task. a SST was measured in milliseconds. * The descriptives include the untransformed BART variable. 55
Table 4. Demographic characteristics of sample.
Min Max M SD Variable N % (yrs.) (yrs.) (yrs.) (yrs.) Age 89 100.0% 21 45 33.11 6.49 Males 44 49.4% Females 45 50.6% Ethnicity 89 100.0% Non-Hispanic/Latino 80 89.9% Hispanic/Latino 9 10.1% Race 89 100.0% White/Caucasian 67 75.3% Asian 7 7.9% Black/African-American 9 10.1% American Indian/Alaskan 2 2.2% Native Native Hawaiian/Pacific 1 1.1% Islander
Other 3 3.4%
Education (Highest Degree 89 100.0% Obtained)
Less than 12 th Grade 0 0.0% GED or High School Diploma 21 23.6% 2-year College Degree 14 15.7% 4-year College Degree 40 44.9% Graduate Degree 14 15.7% Employment Status (Current) 89 100.0% Full-time 61 68.5% Part-time 19 21.3% Student 2 2.2% Not employed 7 7.9% Arrest History 89 100.0% Yes 13 14.6% No 76 85.4%