Strong evidence that callous–unemotional traits are not related to risk-taking task performance

Abstract

A hypothesized association between callous–unemotional (CU) traits and risk-taking may account for the link between CU traits and real-world risky behaviors, such as illegal behavior. Prior findings show that reward and punishment responsivity differs in relation to CU traits, but is not associated with general risk-taking. However this has only been examined previously with one task, only with a frequentist framework, and with limited interpretation. Here, we expand to another task and to Bayesian analyses. A total of 657 participants (52% female) completed the Inventory of Callous–Unemotional Traits, the Balloon Analogue Risk Task (essentially a gambling task), and the Stoplight driving task, which repeatedly presents participants with riskier or less risky choices to make while driving. We found strong evidence for the null model, in which there is no relation between the two risk-taking tasks and CU traits (R ² = 0.001; BF ₁₀ = 1/60.22). These results suggest that general risk-taking does not underlie the real-world risky behavior of people with CU traits. Alternative explanations include a different method of valuing certain outcomes.

Introduction

Callous–unemotional (CU) traits are an aspect of psychopathy, which includes traits such as callously using others for one’s personal gain, a lack of caring for society’s values and lacking emotional depth ¹. Risk-taking includes choosing behaviors with uncertain outcomes (but possibly higher rewards) over behavior with more certainty in its rewards ². Here, we show that the two are unrelated when measured in a laboratory setting.

This is surprising for three reasons. First, a variety of risky real-world behaviors and illegal behaviors ¹—themselves risky—are associated with CU traits (e.g., substance use, sexual risk-taking) ^{3– 7}. Second, there is a difference in reward and punishment responsivity in relation to CU traits ^{4, 6, 8– 12}. For example, in a test of gambling, the Balloon Analogue Risk Task (BART), CU was related to weaker reward responsivity, in that adolescents with these traits failed to show an increase in risk-taking following successful (rewarded) trials ^{4, 13}. Third, CU traits are one aspect of a cluster of traits known as psychopathy, which is associated with risk-taking ^{14– 16}.

These data were originally collected as part of a study about the influence of peer presence on risk-taking behavior, with two laboratory tasks conducted ^{13, 17}. CU traits were measured as a potential moderator. Results on the relationship between CU traits and a gambling task have been previously reported using frequentist methods, but the null finding failed to be interpreted ¹³. Here, we re-analyze the data in a Bayesian framework, allowing for the relationship between CU traits and gambling to be interpreted. In addition, for the first time, we report our findings on the association between CU traits and a driving risk-taking task ¹⁷.

Methods

Participants and tasks

A total of 675 people (52% female; 16–18 years of age) from six schools in Northwest England participated in 2010. Heads of schools acted in loco parentis, and verbal consent was obtained to ensure privacy, which was approved by the ethics committee, within the schools where the research was conducted. Ethical approval was given by the University of Central Lancashire to the first author (PSY0809122). Complete information about the sample and recruitment can be found in a previous report ¹⁷.

A total of 657 participants produced usable data on all three measures reported here. The Inventory of Callous Unemotional Traits (ICU) ^{18– 20}, a self-reporting questionnaire, was used to assess CU traits. The BART ²¹, where participants can repeatedly gamble by pumping a balloon for greater reward but risk popping it and receiving no reward, was one measure of risk-taking. The Stoplight driving task ²², where participants repeatedly choose to either enter yellow/red lights and risk time-consuming crashes or stop and then proceed on the green light, was also given in counterbalanced order as an additional risk-taking task. All three are standard choices that have been validated ^{19, 21, 23}. At the time of writing, the ICU and BART tasks can be obtained online, and the Stoplight can be obtained by contacting the authors ²². Unrelated to the aims of the present study, participants were asked to bring two friends of the same gender and completed the tasks either in their presence or not.

Statistical analysis

MultiLevel Data Manipulations were conducted in MLwiN 2.30 (University of Bristol, 2014), resulting in an outcome variable for each task that was adjusted to be equated across peer group membership. Descriptive statistics, zero-order Perason correlations and p-values were calculated using JASP 0.8.2.0 ²⁴. An online tool was used to calculate Bayes factors ²⁵.

Results

Figure 1 shows scatterplots of the relations among the three variables. There was a significant zero-order correlation between the tasks, r=0.22, p<0.001, but not between the ICU scores and either the BART, r=0.033, p=0.397, N=657, BF ₁₀=1/8.09 or the driving task, r=0.013, p=0.738, N=672, BF ₁₀=1/11.00. More importantly, a multiple linear regression, with the risk-taking tasks predicting ICU scores, showed no significant relation to the BART, β=0.033, t=0.824, p=0.410, or the driving task, β= -0.000, t= -0.012, p=0.990. The overall fit was F (2, 654)=0.359, p=0.698; R ²=0.001, R=0.033, N=657, BF ₁₀=1/60.22. In a Bayesian analysis, this is considered strong evidence for the null hypothesis ^{25, 26}.

Figure 1.

Scatterplots of the relations among the three variables.

Scores were adjusted for peer-level clustering, since participants were recruited with two friends.

We also examined the comparability of our sample to others. The mean ± SD for total ICU (21.62 ± 7.85) was comparable to previous community and at-risk samples. For example, our scores were similar to those from a community sample (male, 25.25 ± 7.90; female, 21.76 ± 9.4) ²⁷, as well as to youths from a residential facility (25.74 ± 7.95) ²⁸.

Discussion

The results of this study rule out a specific theory about why CU traits are related to risky real-world behaviors including illegal behavior. People with CU traits are not more likely to engage in risky behavior in a lab setting, so real-world risky behaviors are unlikely to be driven by risk-seeking for its own sake. More broadly, this is a worked demonstration that differences in reward and punishment responsivity on a task do not necessarily imply differences in overall risk-taking, even in the same dataset. On the basis of previously reported findings ^{13, 17} and our re-analyses, we conclude that these two concepts should not be used interchangeably in interpreting risk-taking results.

There are potential alternative explanations for why people with high CU traits tend to do risky things, like having unprotected sex. For one, they may simply place different values on the outcomes of catching a disease and/or seeking bodily sensations. However, an interaction between CU traits and antisocial behavior (i.e., conduct disorder) has shown effects on laboratory risk-taking ²³. One broad possibility is that CU traits do not operate singly, since psychopathy is multifaceted, and some factors of psychopathy appear to be more reliably related to risk taking than others ^{14, 30}.

People who engage in antisocial behavior suffer legal, educational and socio-economic consequences ³¹, and we know CU traits predict antisocial behavior ^{32, 33}. Thus, further research is needed to understand the mechanisms by which people with CU traits (i) engage in antisocial behavior, and (ii) fail to care about the consequences of their behavior on themselves and on other people. The present study sheds light on one part of this, by showing that one obvious idea of how CU traits and illegal behavior relate is not tenable.

Data availability

The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2018 Centifanti LCM and Negen J

Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Dataset 1. Subject demographic information, together with Inventory of Callous–Unemotional Traits score and results of the tasks. Data are provided in raw form and peer-level adjusted format within the same spreadsheet ²⁹. Condition: peer present, 1; peer absent, 0; Subject ID, anonymized participant ID number; Female: female gender, 1; male gender, 0; Age, age in years; BART Pumps AdjAvg_raw data, adjusted average pumps; Peer group level adjusted BART Pumps, peer-group level-adjusted adjusted average pumps; Peer ID, peer group membership ID number; Stoplight Intersections_raw data, number of intersections entered on the Stoplight driving task; Peer group level adjusted Stoplight, peer-group level-adjusted number of intersections entered on the Stoplight driving task; Total ICU, number of CU traits using the Inventory of Callous-Unemotional Traits.

DOI: 10.5256/f1000research.14623.d201818 ²⁹