Implicit Attitudes Toward Violence in a Sample of Adolescent Offenders With Conduct Disorder

Abstract

Few studies have addressed attitudes toward violence in offender populations using implicit measures. The aim of this study is to test whether implicit attitudes toward two types of violence (physical and relational) differ between two groups of adolescent offenders: one group with conduct disorder (CD; n = 36) and the other group without this condition (No-CD; n = 26). We found that adolescent offenders with CD evidenced less negative implicit attitudes toward physical violence than the No-CD group. No differences between groups were observed in the case of relational violence. Our results suggest that CD modulates implicit attitudes toward violence in adolescent offenders and that the influence of CD is stronger in the case of physical rather than relational acts of violence.

Keywords

implicit attitudes violence conduct disorder offenders

Attitudes toward violence have an important role in the translation of hostile feelings into aggressive behaviors (Velicer, Huckel, & Hansen, 1989). In this vein, substantial evidence obtained from self-report questionnaires suggests that positive attitudes toward violence are associated with frequency of violent behavior (Vernberg, Jacobs, & Hershberger, 1999). For instance, attitudes toward violence have been shown to predict both sexual and nonsexual violent behavior toward women (Polaschek, Ward, & Hudson, 1997; Sugarman & Frankel, 1996). Other studies showed that attitudes supporting the use of violence are longitudinally influential on children’s and adolescents’ aggressive behavior (Huesmann & Guerra, 1997; Slaby & Guerra, 1988).

Until now, violent attitudes in offender populations have mostly been assessed with explicit measures. Researchers typically measure violent cognition by having offenders complete self-report questionnaires (such as the Velicer Attitudes Toward Violence Scale; Velicer et al., 1989) about criminal attitudes and beliefs. Although there is empirical evidence that self-report data can have predictive validity (Mills, Loza, & Kroner, 2003), some authors suggest that respondents’ ability for introspection (Nunes, Firestone, & Baldwin, 2007) and social desirability compromise the use of self-report instruments (Zwets et al., 2015), especially in the case of some mental disorders (Roefs et al., 2011).

As a result, there has been a growing interest in extending measurement procedures of psychological attributes beyond what self-assessment questionnaires can reveal. Indeed, implicit measures of attitudes (understood as outcomes of measurement procedures caused automatically by psychological attributes; De, Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009) have emerged dramatically in the last decade and have been applied to several domains of social cognition (Payne & Gawronski, 2010). The main premise is that implicit measures are indirect, in the sense that they provide an estimate of a person’s attitude without the researcher having to ask for a self-report (Bohner, Siebler, González, Haye, & Schmidt, 2008).

The Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998), the most used implicit measure, evidenced superior predictive validity relative to explicit measures for socially sensitive topics (such as lack of empathy in adult offenders; Kampfe, Penzhorn, Schikora, Dunzl, & Schneidenbach, 2009) and spontaneous behaviors (such as spontaneous shy behavior and performance changes due to anxiety; Asendorpf, Banse, & Mücke, 2002; Egloff & Schmukle, 2002; for a review, see Greenwald, Poehlman, Uhlmann, & Banaji, 2009).

The IAT evaluates associations between a bipolar target category (“Black” vs. “White”) and a bipolar attribute category (“negative” vs. “positive”) through a series of categorization tests requiring prompt responses. The fundamental principle is that when two concepts which are strongly associated (e.g., “White” and “positive”) share the same key, the reaction time (RT) is less than when this is not the case (e.g., “White” and “negative”). The procedure is considered a valid measure of implicit processing in the sense that the psychological attributes of the individual are inferred from the speed with which the participants respond to stimuli in the categorization task (De Houwer et al., 2009).

Nevertheless, few studies on implicit attitudes toward violence have been conducted. For instance, Snowden, Gray, Smith, Morris, and MacCulloch (2004) found that murderers highest in psychopathy had less negative implicit attitudes toward violence in comparison with nonmurderers. This finding is congruent with research on psychopathic traits (i.e., callous-unemotional traits, grandiose-manipulative traits and impulsive-irresponsible traits; Cohn et al., 2015), which are associated with a reduced empathic response (e.g., the processing of distress cues; Oliver, Neufeld, Dziobek, & Mitchell, 2016) and core impairments in aspects of decision making (e.g., reward system dysfunction; Hosking et al., 2017). As a result, individuals with psychopathic traits are more likely to commit actions that will harm other individuals (Blair, 2013).

Robertson and Murachver (2007) found that an incarcerated sample had more positive implicit attitudes toward violence than the nonincarcerated sample. Interestingly, both groups did not differ in their explicit attitudes (for a similar result, see Polaschek, Bell, Calvert, & Takarangi, 2010). In this vein, there is evidence that men enrolled in an intimate partner violence treatment showed more positive implicit attitudes regarding violence compared with a control group (Eckhardt, Samper, Suhr, Holtzworth-Munroe, 2012) and that forensic psychiatric inpatients evidenced a positive relation between implicit attitudes toward violence and some measures of psychopathy (Zwets et al., 2015). Surprisingly, results by Suter, Pihet, de Ridder, Zimmermann, and Stephan (2014) with adolescent offenders with psychopathic traits showed that while they implicitly saw themselves as respectful and kind, they explicitly described themselves as transgressive and aggressive.

The present research assesses whether implicit attitudes toward two types of violence (physical and relational) vary depending on conduct disorder (CD) in a sample of adolescent offenders. According to the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association [APA], 2013), CD in adolescents is characterized by a persistent pattern of behaviors that violate the right of others or that violate societal norms. Prevalence of CD in adolescents is believed to be between 2% and 10%, males being the most frequently affected (1:4). Male adolescents with CD show more physical aggressive behaviors (e.g., fights, vandalism, theft), while female adolescents with CD seem to show more frequently relational aggressions (e.g., emotional manipulation; APA, 2013).

The fact that these behaviors are typically poorly controlled and that adolescent offenders with CD often lie to obtain goods or favors makes an implicit approach relevant to this issue. In this vein, the DSM-5 suggests that explicit measures might have some limitations related to assessing the presence of CD specifiers (phenomenological subgrouping within a diagnosis) in youth populations. For instance, a minority of CD inpatients exhibit the “with limited prosocial emotions” specifier, which has been considered one of the personality features indicative of psychopathy (Kimonis et al., 2008). As pointed in the DSM-5, individuals with CD with this specifier may not readily admit to the traits in a self-report. Moreover, the fact that the presence of psychopathic traits in youths increase the risk of persistent antisocial behavior and aggression (Blair, 2013; Frick, Ray, Thornton, & Kahn, 2014) and that such traits can interfere with CD treatments (Hawes & Dadds, 2005) strengthen the point that implicit measurement procedures of psychological attributes are relevant in this context.

Other findings also suggest that, at least in adult offenders, there is a tendency toward self-presentation biases (Kampfe et al., 2009). To the best of our knowledge, this is the first attempt to assess implicit attitudes toward violence in a population of adolescent offenders with CD.

Method

Participants

Overall, 62 male adolescent offenders were selected from a total sample of 64 adolescent offenders from the “Instituto Psicoeducativo de Colombia” (Ipsicol, Medellín; CD group = 36, No-CD group = 26; age: M = 16.4, SD = 0.86). Two individuals opted not to participate in the study, citing reading problems. The CD group was selected according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; APA, 2000) and confirmed the diagnosis with the module of Conduct Disorder of the International Neuropsychiatric Interview (M.I.N.I [Mini International Neuropsychiatric Interview]; Sheehan et al., 1997). The clinical history and criminal records of all participants were studied to exclude any subject with evidence of any medical condition that may suggest the existence of any other developmental, emotional, or behavioral disorder.

All experimental sessions took place in the “Instituto Psicoeducativo de Colombia” (Ipsicol, Medellín). Informed consent was obtained from each adolescent and their legal guardian (the principal of the institution). We informed all participants that participation was voluntary and would have no impact on their legal involvement. The study was approved by the Bioethics Committee of the “Universidad Católica Luis Amigó” (Colombia).

Material

We displayed the stimuli on a 20-inch screen (60 Hz screen refresh rate) with a PC running OpenSesame v. 3.0.7 (Mathôt, Schreij, & Theeuwes, 2012) on Windows 8 (Microsoft Corporation). We used the Single-Target IAT (ST-IAT; Karpinski & Steinman, 2006) to assess implicit attitudes toward two types of violence: physical and relational (target categories). Target categories consisted of five physical violence words (e.g., pelear [to fight]) and five relational violence words (e.g., excluir [to exclude]). Attribute categories were positive (e.g., amor [love]; Valence M = 8.31, SD = 0.25; Arousal M = 6.73, SD = 0.83) and negative (e.g., veneno [poison]; Valence M = 1.52, SD = 0.17; Arousal M = 6.56, SD = 0.36) words adapted from the Affective Norms for English Words (Bradley & Lang, 1999; adapted to Spanish population by Redondo, Fraga, Padrón, & Comesaña, 2007; Appendix A). We controlled for valence (hedonic tone) and arousal (low to high degree of excitement of affective experience) given the consistent finding that both dimensions underlie the structure of emotional response (Russell & Barrett, 1999).

Procedure

Following the same procedure as Bluemke and Friese (2008), each stimulus was presented at least twice, adding up to 35 trials per combined block. Violence stimuli, coupled and uncoupled positive/negative stimuli occurred at a ratio of 10:10:15 trials (i.e., stimulus proportions across ST-IAT blocks; see Appendix B). By coupled (paired) stimuli, we mean that stimuli (items) that belong to different categories (e.g., “physical violence” and “positive”) share the same key (Appendix B). Before the task, clinical interviewers used the criteria from the DSM-IV-TR (APA, 2000) and confirmed the diagnosis with the module of Conduct Disorder of the International Neuropsychiatric Interview (M.I.N.I; Sheehan et al., 1997). Depending on the diagnosis, the experimenter assigned participants to the CD or No-CD condition by choosing one of the two options: “TDC” (conduct disorder) or “C” (“not conduct disorder”).

Participants completed two consecutive versions of the ST-IAT (“physical” or “relational” violence ST-IAT). In the ST-IAT, participants were asked to categorize each presented stimulus as quickly and accurately as possible. In a typical procedure, the experimenters assessed the association of the target category (e.g., physical violence) toward positive and negative valence attribute categories. The reasoning behind the ST-IAT is based on response interference or compatibility. If one has a negative implicit attitude toward physical violence, it should be easier to classify negative stimuli and physical violence stimuli with a single key than to classify positive stimuli and physical violence stimuli with the same key. The easiness of the task is evaluated through response latencies (RTs): shorter latencies indicate easier stimuli/category assignment (i.e., lesser interference/more compatibility), which is indicative of stronger implicit associations (Bohner et al., 2008; Richetin, Costantini, Perugini, & Schönbrodt, 2015). Therefore, by comparing response latencies between blocks where the target category (e.g., physical violence) is paired with negative stimuli (i.e., negative category) and blocks where the target category is paired with positive stimuli (i.e., positive category), a ST-IAT score can be computed.

The categorization task started with 20 trials for the training block, prior to the first combined block. This training block considered only two categories (“positive” and “negative”) and the obtained scores were not considered in further analysis. The task was explained to participants ahead of each block, and the category labels, which were visible at the top of the screen, served as a reminder. The order for which the first version of the ST-IAT was presented (“physical violence” or “relational violence” ST-IAT) was balanced between participants. The order of the item/category assignment was randomized within participants.

Results

As in previous ST-IAT research (Bluemke & Friese, 2008), we omitted participants who committed 30% or more errors (incorrect responses in the item/category assignment) in at least one of the two ST-IATs. As a result, our final analysis was based on a sample size of 38 male adolescent offenders (CD group = 19, No-CD group = 19). We recoded the trial latencies (RTs) that were below 300 ms (0.15% of the total trials of the task) or above 3,000 ms (7.33%) to the respective values and replaced the 14.33% trials that were errors by the block mean of correct latencies plus 600 ms (for a detailed description of the procedure, see Greenwald, Nosek, & Banaji, 2003; Richetin et al., 2015). ST-IAT effects were calculated on the basis of the attribute trials only by computing the widely used D-measure algorithm (Greenwald et al., 2003).

We tested the normality of data prior to the analysis using the Shapiro–Wilk test. Group differences were evaluated using independent-samples t tests and the Wilcoxon W test (when applicable). Compared with the No-CD group, offenders in the CD condition evidenced faster responses when physical violence items were paired with negative items, which indicates less negative association toward physical violence, t(36) = 2.0514, p = .048, d = 0.32 (Figure 1). Analysis of group differences did not reveal any other significant effects (Table 1).

Figure 1.

D-measures comparison for the physical violence ST-IAT.

Table 1.

D-Scores and Standard Deviations for Both Violence ST-IAT.

D-Measure	Condition	M	SD	Median	t	df	p	d (effect size)
Physical violence	No-CD	0.95	0.65	1.09	2.051	36	.048	0.32
Physical violence	CD	0.51	0.67	0.46
Relational violence	No-CD	0.99	0.62	1.10	0.17	32	.86	0.03
Relational violence	CD	0.96	0.42	0.92

Note. ST-IAT = Single-Target Implicit Association Test; CD = conduct disorder; No-CD = not conduct disorder.

To explore ST-IAT effects in more detail, we calculated four separate D-measures. Following the conventional level of strength adopted in previous research (Blanton, Jaccard, & Burrows, 2015), participants in both conditions evidenced strong negative attitudes toward relational violence (D = 0.92 for CD offenders and D = 1.10 for No-CD offenders). No-CD offenders also evidenced strong negative attitudes toward physical violence (D = 1.09). Congruent with the effect mentioned above, offenders with CD evidenced only moderate negative attitudes toward physical violence (D = 0.46).

Discussion

The results of this study add new elements to the literature on how CD is related to attitudes toward violence in adolescent offenders. This study extends previous research by finding that CD influences implicit evaluation of physical violence (but not relational violence). Thus the study adds further support to the proposal that individuals engaging in habitual violent behavior are more likely to hold favorable attitudes about violence. According to the general aggression model (Anderson & Bushman, 2002), aggression-prone individuals hold more entrenched aggression-related cognitions (e.g., attitudes). Results by Gilbert, Daffern, Talevski, and Ogloff (2013) with offender populations corroborated this claim by showing that aggression-related cognitions (e.g., normative beliefs, behavioral script and early maladaptive schema) concurrently increased aggression in violent offenders.

In this vein, these findings are consistent with previous research on implicit attitudes toward violence. As mentioned before, there is evidence that those individuals likely to be engaged in violent behavior, such as psychopaths (Snowden et al., 2004), incarcerated people (Robertson & Murachver, 2007), or men enrolled in an intimate partner violence treatment (Eckhardt et al., 2012), evidence more positive implicit attitudes toward violence than control groups. Our results support these findings by showing that CD offenders (who are characterized by a persistent pattern of antisocial behavior; APA, 2013) evidenced fewer negative implicit associations to physical violence than No-CD offenders.

The fact that CD influences implicit attitudes toward physical (but not relational) violence might be interpreted in the light of research on sex differences in CD. Indeed, it is widely documented that male adolescents with CD are more likely to engage in physical rather than relational aggressive behaviors (which is more associated with female adolescents with CD; see Arango, Olivera-La Rosa, Restrepo, & Puerta, 2018; Berkout, Young, & Gross, 2011).

We believe that the current findings, although still preliminary, may have practical implications. As Roefs et al. (2011) pointed out, implicit measures in psychopathology research have the potential to predict dysfunctional beliefs and behaviors that explicit measures do not predict. Other authors claim that the quality of diagnostic tools in clinical context, in which individuals may not be willing (or not be able) to respond truthfully, is closely tied to our ability to measure relevant attitudes (Bluemke, Teige, & Mocigemba, 2015; Polaschek et al., 2010). By taking an implicit approach to attitudes toward violence in adolescents with CD, we expect to contribute to new perspectives on CD and its diagnosis.

Several limitations should be noted. First, our final analysis was based on a small sample, which might compromise the generality of our results. However, other studies on implicit attitudes toward violence in offenders have also reported on a small sample of participants (Robertson & Murachver, 2007; Snowden et al., 2004). Second, our sample was based exclusively on male adolescent offenders. Indeed, this is a common practice in CD research, justified in part due to the fact that, relative to women, men are most frequently affected by CD (1:4; DSM-5). Third, we did not include a control group with no criminal records, which might help to delimit the role of CD in implicit attitudes toward violence. Further research should address these limitations. Finally, given that the extent to which attitudes toward violence generalize across cultures requires further investigation (Polaschek et al., 2010), we also believe that future studies should consider a cross-cultural approach (e.g., data from different countries) to achieve a better understanding of attitudes toward violence in adolescent offenders with CD.

Footnotes

Appendix A

Table A2.

Normative Values Attribute Items.

Number	English Word	Spanish Word	Valence-Mn-All subjects	Valence-Sd-All subjects	Arousal-Mn-All subjects	Arousal-Sd-All subjects	Attribute Category
263	Love	Amor	8.50	1.30	7.46	2.40	Positive
301	Pain	Dolor	1.61	1.46	6.40	2.59	Negative
390	Sickness	Enfermedad	1.51	0.93	6.10	2.61	Negative
100	Death	Muerte	1.23	0.64	6.46	2.76	Negative
304	Paradise	Paraíso	8.24	1.32	5.70	2.89	Positive
943	Present	Regalo	7.91	1.37	6.63	2.33	Positive
791	Holiday	Vacación	8.52	0.9	6.18	2.93	Positive
474	Venom	Veneno	1.67	1.17	6.93	2.12	Negative
872	Merry	Alegre ^a	8.41	0.97	7.66	1.65	Positive
121	Disaster	Desastre ^a	1.60	1.14	6.92	2.03	Negative

Source. Adapted from Redondo, Fraga, Padrón, and Comesaña (2007).

Original words were adapted to our population (Colombian’s young offenders).

Appendix B

Table B1.

Category Assignment and Stimulus Proportions Across ST-IAT Blocks for an Exemplary Participant.

Block	Task Description	Left Key Concepts (z)	Right Key Concepts (m)	Number of Stimuli
Block	Task Description	Left Key Concepts (z)	Right Key Concepts (m)	Positive	Negative	Violence
1	Evaluative training trials	Positive	Negative	10	10
2	Initial block	Positive + PV	Negative	10	15	10
3	Reversed block	Positive	Negative + PV	15	10	10
4	Initial block	Negative	Positive + RV	10	15	10
5	Reversed block	Negative + RV	Positive	15	10	10

Note. ST-IAT = Single-Target Implicit Association Test; PV = physical violence; RV = relational violence.

Acknowledgements

The authors thank Astrid Restrepo, Carolina Estrada, and Cristian Arias for help in the experimental procedure. The authors also thank Amanda Whitbeck for checking the English in the manuscript.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the research project FFI2013-43270-P (Spanish Government: Ministry of Economy and Competitiveness) and by Universidad Católica LuisAmigó.

Author Biographies

Antonio Olivera-La Rosa pursued his PhD in human cognition and evolution from the University of the Balearic Islands (Spain). He received a special distinction for his PhD dissertation. He is also a member of the Human Evolution and Cognition Group (EVOCOG). He is now serving as an associate professor at the Universidad Católica Luis Amigó (Colombia). His research interests are implicit social cognition, decision making, moral psychology, and emotions.

Javier Villacampa pursued his MS in cognition and evolution and BS in mathematics. He is a doctoral student in human cognition and evolution at the University of the Balearic Islands (Spain). He is also a member of the EVOCOG. He works as a senior data scientist at Mutua Madrileña (Madrid, Spain).

Omar Amador did his bachelor’s in psychology, with specialization in cognitive therapy. He is serving as an associate professor in Universidad Católica Luis Amigó (Colombia). His research interests include offenders and personality disorders.

Guido Corradi is a PhD candidate in human cognition and evolution, University of the Balearic Islands (Spain). He is also a member of the EVOCOG. His research interests include research on aesthetics and moral cognition.

Enric Munar, PhD, is a professor of basic psychology at the University of the Balearic Islands. He is also the director of the PhD program in cognition and human evolution at the same university. His research interests include research on perception, aesthetic appreciation, and human cognition.

Sergio Acosta did his master’s in psychology, with specialization in legal psychology. He is the director of the “Programa de atención humanizada para la niñez, la juventud y la familia” (Medellín, Colombia) and the director of “Instituto Psicoeducativo de Colombia” (Ipsicol, Medellín, Colombia).

Jaume Rosselló pursued his PhD in psychology from the University of the Balearic Islands (Spain). He is a professor of Basic Psychology at the University of the Balearic Islands. He is also a member of the EVOCOG. His interests include research on emotions, decision making, aesthetic appreciation, and human cognition.

References

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.

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

Anderson

C. A.

Bushman

B. J.

(2002). Human aggression. Annual Review of Psychology, 53, 27-51. doi:10.1146/annurev.psych.53.100901.135231

Arango

O. E.

Olivera-La Rosa

Restrepo

Puerta

(2018). Empathic skills and theory of mind in female adolescents with conduct disorder. Revista Brasileira De Psiquiatria, 40(1), 78-82

Asendorpf

J. B.

Banse

Mücke

(2002). Double dissociation between implicit and explicit personality self-concept: The case of shy behavior. Journal of Personality and Social Psychology, 83, 380-393. doi:10.1037//0022-3514.83.2.380

Berkout

O. V.

Young

J. N.

Gross

A. M.

(2011). Mean girls and bad boys: Recent research on gender differences in conduct disorder. Aggression and Violent Behavior, 16, 503-511.

Blair

R. J. R.

(2013). The neurobiology of psychopathic traits in youths. Nature Reviews Neuroscience, 14, 786-799.

Blanton

Jaccard

Burrows

C. N.

(2015). Implications of the implicit association test D-transformation for psychological assessment. Assessment, 22, 429-440. doi:10.1177/1073191114551382

Bluemke

Friese

(2008). Reliability and validity of the Single-Target IAT (ST-IAT): Assessing automatic affect towards multiple attitude objects. European Journal of Social Psychology, 38, 977-997. doi:10.1002/ejsp.487

10.

Bluemke

Teige-Mocigemba

(2015). Automatic processes in aggression: Conceptual and assessment issues. Aggressive Behavior, 41, 44-50.

11.

Bohner

Siebler

González

Haye

Schmidt

E. A.

(2008). Situational flexibility of in-group-related attitudes: A single category IAT study of people with dual national identity. Group Processes & Intergroup Relations, 11, 301-317. doi:10.1177/1368430208090644

12.

Bradley

M. M.

Lang

P. J.

(1999). Affective norms for English words (ANEW): Instruction manual and affective ratings. Gainesville: Center for Research in Psychophysiology, University of Florida.

13.

Cohn

M. D.

Veltman

D. J.

Pape

L. E.

van Lith

Vermeiren

R. R.

van den Brink

. . . Popma

(2015). Incentive processing in persistent disruptive behavior and psychopathic traits: A functional magnetic resonance imaging study in adolescents. Biological Psychiatry, 78, 615-624.

14.

De Houwer

Teige-Mocigemba

Spruyt

Moors

. (2009). Implicit measures: A normative analysis and review. Psychological Bulletin, 135, 347-368. doi:10.1037/a0014211

15.

Eckhardt

C. I.

Samper

Suhr

Holtzworth-Munroe

(2012). Implicit attitudes toward violence among male perpetrators of intimate partner violence: A preliminary investigation. Journal of Interpersonal Violence, 27, 471-491. doi:10.1177/0886260511421677

16.

Egloff

Schmukle

S. C.

(2002). Predictive validity of an implicit association test for assessing anxiety. Journal of Personality and Social Psychology, 83, 1441-1455. doi:10.1037/0022-3514.83.6.1441

17.

Frick

P. J.

Ray

J. V.

Thornton

L. C.

Kahn

R. E.

(2014). Can callous-unemotional traits enhance the understanding, diagnosis, and treatment of serious conduct problems in children and adolescents? A comprehensive review. Psychological Bulletin, 140, 1-57.

18.

Gilbert

Daffern

Talevski

Ogloff

J. R.

(2013). The role of aggression-related cognition in the aggressive behavior of offenders: A general aggression model perspective. Criminal Justice and Behavior, 40, 119-138. doi:10.1177/0093854812467943

19.

Greenwald

A. G.

McGhee

D. E.

Schwartz

J. L.

(1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464-1480. doi:10.1037/0022-3514.74.6.1464

20.

Greenwald

A. G.

Nosek

B. A.

Banaji

M. R.

(2003). Understanding and using the implicit association test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85, 197-216. doi:10.1037/0022-3514.85.2.197

21.

Greenwald

A. G.

Poehlman

Uhlmann

Banaji

M. R.

(2009). Understanding and using the Implicit Association Test: III—Meta-analysis of predictive validity. Psychological Bulletin, 97, 17-41. doi:10.1037/a0015575

22.

Hawes

D. J.

Dadds

M. R.

(2005). The treatment of conduct problems in children with callous-unemotional traits. Journal of Consulting and Clinical Psychology, 73, 737-741.

23.

Hosking

J. G.

Kastman

E. K.

Dorfman

H. M.

Samanez-Larkin

G. R.

Baskin-Sommers

Kiehl

K. A.

. . . Buckholtz

J. W.

(2017). Disrupted prefrontal regulation of striatal subjective value signals in psychopathy. Neuron, 95, 221-231.

24.

Huesmann

L. R.

Guerra

N. G.

(1997). Children’s normative beliefs about aggression and aggressive behavior. Journal of Personality and Social Psychology, 72, 408-419.

25.

Kampfe

Penzhorn

Schikora

Dunzl

Schneidenbach

(2009). Empathy and social desirability: A comparison of delinquent and non-delinquent participants using direct and indirect measures. Psychology Crime & Law, 15, 1-17. doi:10.1080/10683160802010640

26.

Karpinski

Steinman

R. B.

(2006). The single category implicit association test as a measure of implicit social cognition. Journal of Personality and Social Psychology, 91(1), 16-32. doi:10.1037/0022-3514.91.1.16

27.

Kimonis

E. R.

Frick

P. J.

Skeem

J. L.

Marsee

M. A.

Cruise

Munoz

L. C.

. . . Morris

A. S.

(2008). Assessing callous–unemotional traits in adolescent offenders: Validation of the Inventory of Callous–Unemotional Traits. International Journal of Law and Psychiatry, 31, 241-252. doi:10.1016/j.ijlp.2008.04.002

28.

Mathôt

Schreij

Theeuwes

(2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44, 314-324. doi:10.3758/s13428-011-0168-7

29.

Mills

J. F.

Loza

Kroner

D. G.

(2003). Predictive validity despite social desirability: Evidence for the robustness of self-report among offenders. Criminal Behaviour and Mental Health, 13, 140-150. doi:10.1002/cbm.536

30.

Nunes

K. L.

Firestone

Baldwin

M. W.

(2007). Indirect assessment of cognitions of child sexual abusers with the Implicit Association Test. Criminal Justice and Behavior, 34, 454-475. doi:10.1177/0093854806291703

31.

Oliver

L. D.

Neufeld

R. W.

Dziobek

Mitchell

D. G.

(2016). Distinguishing the relationship between different aspects of empathic responding as a function of psychopathic, autistic, and anxious traits. Personality and Individual Differences, 99, 81-88.

32.

Payne

B. K.

Gawronski

(2010). A history of implicit social cognition: Where is it coming from? Where is it now? Where is it going? Handbook of implicit social cognition: Measurement, theory, and applications (pp. 1-15). New York: Guilford Press.

33.

Polaschek

D. L. L.

Bell

R. K.

Calvert

S. W.

Takarangi

M. K. T.

(2010). Cognitive-behavioural rehabilitation of high-risk violent offenders: Investigating treatment change with explicit and implicit measures of cognition. Applied Cognitive Psychology, 24, 437-449. doi:10.1002/acp.1688

34.

Polaschek

D. L. L.

Ward

Hudson

S. M.

(1997). Rape and rapists: Theory and treatment. Clinical Psychology Review, 17, 117-144.

35.

Redondo

Fraga

Padrón

Comesaña

(2007). The Spanish adaptation of ANEW (affective norms for English words). Behavior Research Methods, 39, 600-605.

36.

Richetin

Costantini

Perugini

Schönbrodt

(2015). Should we stop looking for a better scoring algorithm for handling Implicit Association Test data? Test of the role of errors, extreme latencies treatment, scoring formula, and practice trials on reliability and validity. PLoS ONE, 10(6), e0129601. doi:10.1371/journal.pone.0129601

37.

Robertson

Murachver

(2007). Correlates of partner violence for incarcerated women and men. Journal of Interpersonal Violence, 22, 639-655. doi:10.1177/0886260506298835

38.

Roefs

Huijding

Smulders

F. T.

MacLeod

C. M.

de Jong

P. J.

Wiers

R. W.

Jansen

(2011). Implicit measures of association in psychopathology research. Psychological Bulletin, 137, 149-193. doi:10.1037/a0021729

39.

Russell

J. A.

Barrett

(1999). Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant. Journal of Personality and Social Psychology, 76, 805-819. doi:10.1037/0022-3514.76.5.805

40.

Sheehan

D. V.

Lecrubier

Harnett-Sheehan

Janavs

Weiller

Bonara

Ll.

Keskiner

. . . Dunbar

G. C.

(1997). Reliability and validity of the MINI International neuropsychiatric interview: According to the SCID-P. European Psychiatric, 12, 232-241. doi:10.4088/JCP.09m05305whi

41.

Slaby

R. G.

Guerra

N. G.

(1988). Cognitive mediators of aggression in adolescent offenders: I. Assessment. Developmental Psychology, 24, 580-588.

42.

Snowden

R. J.

Gray

N. S.

Smith

Morris

MacCulloch

M. J.

(2004). Implicit affective association to violence in psychopathic murderers. The Journal of Forensic Psychiatry & Psychology, 15, 620-641. doi:10.1080/14789940412331313377

43.

Sugarman

D. B.

Frankel

S. L.

(1996). Patriarchal ideology and wife-assault: A meta-analytic review. Journal of Family Violence, 11, 13-40.

44.

Suter

Pihet

de Ridder

Zimmermann

Stephan

(2014). Implicit attitudes and self-concepts towards transgression and aggression: Differences between male community and offender adolescents, and associations with psychopathic traits. Journal of Adolescence, 37, 669-680. doi:10.1016/j.adolescence.2014.03.004

45.

Velicer

W. F.

Huckel

L. H.

Hansen

C. E.

(1989). A measurement model for measuring attitudes toward violence. Personality and Social Psychology Bulletin, 15, 349-364. doi:10.1177/0146167289153006

46.

Vernberg

E. M.

Jacobs

A. K.

Hershberger

S. L.

(1999). Peer victimization and attitudes about violence during early adolescence. Journal of Clinical Child Psychology, 28, 386-395. doi:10.1207/S15374424jccp280311

47.

Zwets

A. J.

Hornsveld

R. H.

Muris

Huijding

Kanters

Snowden

R. J.

van Marle

(2015). Implicit attitudes toward violence and their relation to psychopathy, aggression, and socially adaptive behaviors in forensic psychiatric inpatients. The Journal of Forensic Psychiatry & Psychology, 26, 632-651. doi:10.1080/14789949.2015.1037331