Observations both in the CIMBA Leadership Labs and coaching interventions suggest that controlling for individual differences in self-regulation has a lot to contribute to improving results in both the research and its applications to development and learning.

Emotion and self-regulation

In exploring this notion, it is perhaps best to first consider the relationship between emotion regulation and self-regulation. At the CIMBA Leadership Labs, we have observed that transitions to and the maintenance of leadership is heavily influenced by what we refer to as an individual’s level of “mental complexity,” which is the ability to perceive, identify, understand, and successfully manage both his or her emotions and the emotions of others (see Olsson et al., 2014)\[1\]. Effective leaders harness and direct the power of emotion to build trust and improve follower satisfaction, morale, and motivation, and thus enhance overall organizational effectiveness (Riggio & Reichard, 2008)\[2\].

From this perspective, it is not surprising that emotion regulation has increasingly garnered interest as a component of an effective leadership development intervention strategy. The focus of neuroleadership more generally, and in our discussion here more specifically, is in understanding how those development and learning resources might be allocated most efficiently.

In this sense, emotion regulation is seen as belonging to a larger family of processes whereby an individual—the leader—exerts control over her own behavior in adapting to the social group through responses to social stimuli (defined by SCARF). In fact, modern emotion regulation research has drawn considerable inspiration from theories of human self-regulation and cognitive control (for example, Carver & Scheier, 1999; Rueda et al., 2005; Diamond, 2013; Bridgett et al., 2013)\[3\]. From the standpoint of a development intervention, we too have observed that successful emotion regulation strategies have as a precursor an individual’s self-regulatory ability.

We recognize that the psychology and neuroscience of explicit emotion regulation have been fruitfully studied for over two decades, yielding much understanding of the neural mechanisms of emotions and behavioral control (Ochsner & Gross, 2005; Gross, 2007; Ochsner & Gross, 2008; Ochsner et al., 2012)\[4\]. Brain imagining research argues persuasively that the right ventrolateral prefrontal cortex (rVLPFC) is the neural region commonly recruited across many different forms of both self-control (Cohen et al., 2013)\[5\] and emotion regulation (Goldin et al., 2013)\[6\].

Across a wide variety of fields, self-regulation has been identified as a contributor to adaptive and adverse outcomes in children, adolescents, and adults, affecting such emotion-influenced behaviors as coping skills, social competence, interpersonal relations, and self-esteem, as well as impulsivity, self-control, self-discipline, mind wandering, and time management, among a significant list of other behaviors (See, Bridgett et al., 2013; Smallwood, 2013)\[7\].

Both observation and research show that there is also remarkable variability in individual adjustments to emotion, particularly anxiety and stress (Ong et al., 2006)\[8\]. It has long been our belief, a belief supported increasingly by both our data and the research of others (see Niles et al., 2013)\[9\], that individual differences in self-regulatory ability predict the success of an emotion regulation deployment.

Against this research, we at the CIMBA Leadership Labs believe that accounting for individual differences in self-regulatory ability is important not only in intervention strategies, but also in the research.

A look at the study

To our knowledge, a large study controlling for self-regulatory ability had not been performed prior to this year—and certainly not one involving an experiment outside the laboratory. In what we believe is a first, a very recent study by Xu et al. (2014)\[10\] found that individual differences in self-regulation can predict employees' safety behaviors in the workplace. That is, instead of developing an experiment to test worker responses to a defined worker place stimulus, the research team first controlled for individual self-regulatory ability using recognized psychometric assessment instruments.

The study found that employees with low self-regulatory ability were more influenced by System 1 cognitive processes, while employees with high self-regulatory ability were guided more by System 2 cognitive processes. Clearly, both System 1 and System 2 cognitive processes influence worker behavior, but through different pathways.

• System 1 cognitive processes affect behavior through an impulsive and spontaneous process, largely driven by habit (good or bad).

• System 2 cognitive processes were seen to drive behavior through a deliberative and reflective process, in which automatic, habitual impulses are inhibited, and the employee’s behavior is guided by conscious thought and analysis.

In other words, the study strongly suggests that it is the relative mix of workers with low and high self-regulatory ability and not the type of safety intervention strategy implemented that most influences safety behaviors in the workplace.

The results are further generalized in the table below.

With specific regard to the efficient allocation of development and learning resources, given the measurable impact of individual differences in self-regulation behavior, the Xu et al. 2014 study suggests that intervention strategies may be more effective for differing subgroups of employees. Those employees with higher self-regulatory abilities may benefit more from traditional interventions focused on information-based techniques and courses. Employees with lower self-regulatory ability may benefit from interventions that attempt to strengthen self-regulation, either through coaching, a mindfulness program (see Teper et al., 2013)\[11\] or targeted computer-based brain exercises (see Schweizer et al., 2013; Onraedt et al., 2014)\[12\]. The same reasoning applies whether you are working to overcome behavioral barriers or to build leadership competencies. 

Looking at it from a research respective may serve to further clarify the importance and application of this study. Suppose our intent is to impose a particular learning intervention on a large participant pool and to test its effectiveness. If the intervention is technical in nature, the results will be significantly different if the relative mix of participants is low versus high in self-regulatory ability.

Should we base employee selection on self-regulatory ability? We think most good talent directors already do—largely unconsciously. Still, there are some tasks, particularly those requiring a measureable degree of independence, where a low regulatory person may be most productive. Our concern is that the issue needs to be taken into account in both research and in learning interventions. We also found that it can be effectively addressed through targeted interventions intent on building self-regulatory ability.

CIMBA Leadership Labs incorporates neuroscience, social psychology, cutting-edge technology, and one-on-one coaching to actualize participants’ personal behavioral goals. We are bringing together the newest advances in science and technology to create a system whose participants achieve evidenced, visible personal growth that is directly applicable to their role in a team environment. 

REFERENCES

\[1\] Olsson, A., Carmona, S., Downey, G. Bolger, N. & Ochsner, K. N. (in press). Learning biases underlying individual differences in sensitivity to social rejection. Emotion.

\[2\] Riggio, R.E., & Reichard, R.J. (2008). The emotional and social intelligences of effective leadership: An emotional and social skill approach. Journal of Managerial Psychology, 23(2), 169-185.

\[3\] Carver, C. S., & Scheier, M. F. (1999). Themes and issues in the self-regulation of behavior. Advances in Social Cognition, 12, 1-105; Rueda, M. R., Posner, M. I., & Rothbart, M. K. (2005). The development of executive attention: Contributions to the emergence of self-regulation. Developmental Neuropsychology, 28(2), 573-594; Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135-168; Bridgett, D. J., Oddi, K. B., Laake, L. M., Murdock, K. W., & Bachmann, M. N. (2013). Integrating and differentiating aspects of self-regulation: Effortful control, executive functioning, and links to negative affectivity. Emotion, 13(1), 47.

\[4\] Ochsner, K. N., Silvers, J. A., & Buhle, J. T. (2012). Functional imaging studies of emotion regulation: A synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences, 1251(1), E1-E24; Ochsner, K.N. & Gross, J.J. (2008). Cognitive emotion regulation: Insights from social cognitive and affective neuroscience. Current Directions in Psychological Science, 17, 153–158; Ochsner, K.N. & Gross, J.J. (2005). The cognitive control of emotion. Trends in Cognitive Science, 9, 242–249.

\[5\] Cohen, J. R., Berkman, E. T., & Lieberman, M. D. (2013). Intentional and incidental self-control in ventrolateral PFC. In D. T. Stuss & R. T. Knight (Eds.) Principles of Frontal Lobe Function (2nd ed) (pp. 417-440), New York: Oxford University Press.

\[6\] Goldin, P., Ziv, M., Jazaieri, H., Hahn, K., & Gross, J. J. (2013). MBSR vs aerobic exercise in social anxiety: fMRI of emotion regulation of negative self-beliefs. Social cognitive and affective neuroscience, 8(1), 65-72.

\[7\] Bridgett, D. J., Oddi, K. B., Laake, L. M., Murdock, K. W., & Bachmann, M. N. (2013). Integrating and differentiating aspects of self-regulation: Effortful control, executive functioning, and links to negative affectivity. Emotion, 13(1), 47; Smallwood, J. (2013). Distinguishing how from why the mind wanders: a process–occurrence framework for self-generated mental activity. Psychological Bulletin, 139(3), 519.

\[8\] Ong, A.D., Bergeman, C.S., Bisconti, T.L., & Wallace, P.A. (2006). Psychological resilience, positive emotions, and successful adaptations to stress in later life, Journal of Personality and Social Psychology, 91, 730-749.

\[9\] Niles, A. N., Mesri, B., Burklund, L. J., Lieberman, M. D., & Craske, M. G. (2013). Attentional bias and emotional reactivity as predictors and moderators of behavioral treatment for social phobia. Behavioral Research and Therapy_, 51_, 669-679.

\[10\] Xu, Y., Li, Y., Ding, W., & Lu, F. (2014). Controlled versus automatic processes: Which Is dominant to safety? The moderating effect of inhibitory control. PloS One, 9(2), e87881.

\[11\] Teper, R., Segal, Z. V., & Inzlicht, M. (2013). Inside the Mindful Mind How Mindfulness Enhances Emotion Regulation Through Improvements in Executive Control. Current Directions in Psychological Science, 22(6), 449-454.

\[12\] Schweizer, S., Grahn, J., Hampshire, A., Mobbs, D., & Dalgleish, T. (2013). Training the emotional brain: improving affective control through emotional working memory training. The Journal of Neuroscience, 33(12), 5301-5311; Onraedt, T., & Koster, E. H. (2014). Training working memory to reduce rumination. PloS One, 9(3), e90632.