Reframed: The Journal of Self-Reg
Volume 1, Issue 1
Inspired by questions arising from our communities, the first issue of Reframed focuses on an intensive review of existing literature in the following five areas: the domains of stress, the transition conditions between positive and negative stressors, Self-Reg reframing, Self-Reg in practice, and a review of Self-Reg measures. The overarching goal is to reframe existing research from a Self-Reg perspective, to determine what we already know and what we need to focus on next.
Sparking Reframed: A Letter from the Editor
Hide and Seek: The Challenge of Understanding the Full Complexity of Stress and Stress-Reactivity,
Stuart Shanker and Travis Francis
Good Stress Gone Bad: Transition Conditions in Transforming Stress from Negative to Positive
Stuart Shanker and Elizabeth Shepherd
Self-Reg and Reframing
Stuart Shanker and Casey Burgess
Reframing: A Literature Review
Masking Stress with Misbehaviour: A Shanker Self-Reg® Lens
Susan Hopkins and Elizabeth Shepherd
Self-Regulation, Self-Control, and the Practice of Shanker Self-Reg®
Susan Hopkins, Stuart Shanker, and Rebecca Leslie
Examining the Measures: Review of Self-Reg Components Captured in Current Measures Labelled “Self-Regulation”
Daisy Pyman and Brenda Smith-Chant
Measuring the Biological Foundations of Self-Reg: Psychophysiological Assessments of the Stress Response
Casey Burgess and Brenda Smith-Chant
Sparking Reframed: A Letter from the Editor
Dr. Stuart Shanker, DPhil
A paradigm revolution, like any revolution, needs an official organ: something to light a spark from which a fire will flare up. The political overtones of such a metaphor are by no means accidental. Thomas Samuel Kuhn chose it because he recognized how the war of ideas can sometimes be every bit as fierce as the battle for power. With one big difference. Paradigm revolutions aren’t about one side trying to seize power from another. And they are certainly not about razing everything that stands to the ground. Paradigm revolutions, unlike paradigm shifts, seek to build on what has gone before, rather than ignore, or worse still, dismiss the accumulated wisdom of earlier ages. They seek to transcend polarized debates: to achieve a dialectical synthesis, not just of previously conflicting ideas, but amongst previously disparate fields of inquiry. The result is the construction of an interdisciplinary framework that supports a whole new way of thinking about some very old questions. And that requires a forum where these new ideas can be articulated, debated, elaborated, and deepened.
Revolutions of any kind occur when existing structures and institutions are unable to cope with the proliferation of dysregulating forces. This is precisely the situation that we face today vis-à-vis the well-being of children, teens, and adults across the lifespan. The eruption of internalizing, externalizing, cognitive, and health issues is the unmistakable sign that existing attitudes and practices are unable to deal with current stresses, whatever these might be. The resulting paradigm revolution that is beginning to emerge represents a proactive response to conditions of mounting social, emotional, environmental, and technological challenges that we must not only adapt to, but also master if we are to thrive.
The metaphor for this paradigm revolution that jumps out at one here is Jean Piaget’s notion of equilibration: that is, the idea that a growing set of demands, internal as well as external, force an organism to ascend to a new level of functioning if it is to flourish. Stresses are positive when they promote such a developmental leap, negative when they cause the individual to flee from or block out those stresses.
Piaget’s concept also applies in a fundamental way to self-regulation: that is, to how the individual responds actively to new kinds and levels of stress, rather than passively submitting to them or trying to shut them out. The former response leads to growth in all its many guises, the latter to arrested development. This point clearly applies to the multiple problems that we are seeing in children, teens, and adults today, which are not necessarily the result of increasing stress levels, but are certainly exacerbated by them. The fact that prevalence numbers continue to climb inexorably may tell us that we are getting better at tracking these problems, but clearly indicates that we are stalled in our efforts to reverse these trends.
Equilibration applies not just to development, but also, no less forcefully, to how we think about and respond to these problems. Just as attractors form as a way of blocking out or avoiding certain kinds of stressful experience, so too paradigm shifts represent a sort of “intellectual attractor” in which we swing from one polarity to another in what becomes an increasingly fruitless attempt to grapple with the spate of new problems that we are seeing. But equilibration of any sort requires a surge of energy to break out of an attractor, and that is where this new journal comes in.
Reframed: The Journal of Self-Reg seeks to capitalize on the excitement that Self-Reg has generated to stimulate the sort of massive effort required for a new paradigm to take shape and to take hold. The goal here is to understand why stress can be so positive in some situations and negative in others, and what we can do to combat the latter and promote the former. What is more, the journal seeks to tap into the riches contained within existing research studies and theories, which may not have seemed to do so at the time, but which, viewed through the Self-Reg lens, significantly advance our understanding of stress and stress-reactivity: that is, contribute to, rather than conflict with the Self-Reg paradigm.
The emphasis in this new journal is on reframing as a means of fostering an ever-expanding movement. For it takes an army to sweep out the old order and install a new one, whether it’s a paradigm or a form of government. This is why the metaphor of a revolution is so apposite: it is going to require as much a political as an intellectual effort to sweep aside the ancient self-control paradigm and embrace self-regulation in its place.
The self-control Old Order permeates every aspect of how we think about each stage of the life cycle: how we recognize and respond to stress-behaviours. Someone will eventually write the History of this Idea, which, only now, is becoming a distinct possibility; because only now is that history coming to an end (another allusion to Hegel!). The goal of this journal is to hasten that end.
The driving force behind any form of revolution is idealism mixed with overweening optimism. In the case of the Self-Regulation Institute, what drives us is the vision of development as constrained rather than fixed: the push to learn as much as we possibly can about the factors that obstruct development and healthy functioning and how these can be overcome. This is the reason why Self-Reg needs an official organ: not just to serve as a forum for the expression of these new ideas, but also to be the spark that will inspire a vanguard of revolutionary thinkers and practitioners.
Hide and Seek: The Challenge of Understanding the Full Complexity of Stress and Stress Reactivity
Stuart Shanker, DPhil and Travis Francis HBASC
This paper presents an interactive model of stress in place of more standard additive models. This approach considers not just the manner in which multiple stresses impinge on and magnify each other, but also the bi-directional relationship between internal state and stress-reactivity. Such an outlook has profound implications for our efforts to understand why so many children today are over-stressed.
We are constantly being asked the same question by parents and educators: “What is the major stressor on children and teens today?” Is it urbanization? Too little exercise? Too little time spent in nature? Too much screen time? Overscheduling? Too little sleep? School? Social media? Smartphones? Junk food? The fact that we get asked the same question over and over speaks to the widespread anxiety that children and teens are over-stressed, and to an intuitive awareness that the above factors are significant stressors. But we could easily ask this same question of ourselves, as no one seems immune in our over-stressed society.
What makes answering this question so difficult is that no two children – no two individuals – are the same. No two contexts are the same, and no one stays the same. What is a negative stress for one might be a positive stress for another, and what is a positive stress might easily turn negative (see “Good Stress Gone Bad,” this volume).
We clearly need to deepen our understanding of the nature of stress if we are to understand when and why someone is hyper-aroused, and what to do about it. The problem is, stress is not just ubiquitous, it is necessary, and by no means intrinsically negative. We need stress to be motivated and animated: to function at our best and develop physically, psychologically, and emotionally. Too much stress, however, impairs motivation, functioning, and development.
Hans Selye’s definition of stress continues to inform so much of our thinking on the subject “Stress is considered to be the physiological, psychological and biological reaction of the body to stimuli that requires one to consume energy in order to maintain homeostasis” (as cited in Viner, 1999 pp. 391 – 410). His point, which goes back to Cannon (1932), is that the brain responds to stress by triggering metabolic processes that keep homeostatic systems functioning within their optimal range. But there is not a single, unitary relationship between a given stress and a biological reaction. The individual is responding to multiple stressors at any one time, with multiple homeostatic systems involved. So, the answer to the opening question, in regard to the major stressors today, is: “All of the above” – even in cases where one particular stress seems to stand out.
The deeper point here is that, in place of an additive model of stress, we are really dealing with an interactive phenomenon: not just in terms of the manner in which stresses impinge on and magnify each other, but also in terms of the bi-directional relationship between internal physiological state and stress-reactivity. Whether a stress is positive or negative is as much a function of homeostatic balance as of the stresses themselves (Bernard, 1865/1957).
The fact is that there is an ongoing dance, as it were, between psychophysiological state and psychophysiological reaction. Accordingly, when we look at the above examples of major stressors, we need to consider:
- Biological factors that strongly influence stress-reactivity;
- The intensity/duration/persistence of the stressor;
- The combined effect of the totality of stressors;
- How internal state shapes the reaction to any particular stressor.
These four factors, together, underlie the variability that we see in stress-reactivity. One and the same stress can be energizing in a low energy/high tension state and damaging in a high energy/high tension state. But the major factor in these different conditions is the overall stress load. Hence, to respond in a healthy, adaptive manner to a high-stressed environment such as exists today, we need to work on all five of the domains addressed in Self-Reg: biological, emotion, cognitive, social, and prosocial (Shanker, 2016).
An illuminating analogy here is how humans have responded to the stresses of outer space. Chris Hadfield’s An Astronaut’s Guide to Life on Earth (2013) provides a fascinating account of the ingenuity that has gone into enabling humans to survive in the most challenging of conditions. The combined effort of physiologists and physicians, working alongside engineers and astrophysicists, has made it possible to accommodate stresses that just a short time ago seemed insurmountable.
What NASA has accomplished in an astonishingly short period of time, evolution has done over eons, through the gradual process of natural selection. We have an exquisitely tailored nervous system for dealing with the stresses that predominated during most of this long evolutionary history; it is the effect of too many modern stressors, combined with lifestyle changes that override the Basic Rest-Activity Cycle (Kleitman, 1963), that shifts the stress pendulum from positive to negative, from anabolic to catabolic (Gluckman & Hanson, 2008).
The fact that a child or teen responds to a stressor in a negative fashion may tell us something about that stressor; it may tell us something about that child or teen. In most cases, it tells us something about the child or teen’s overall stress load. And the stressors involved go far beyond those listed at the outset of this paper.
Over the past 50 years, researchers have sought to develop a quantitative measurement of stress, looking at everything from major life events to daily hassles. In the late 1960s, Holmes and Rahe (1967) created the Social Readjustment Rating Scale, which assigns a value to various life events (for example, the death of a spouse is assigned a value of 100). These values are then added together to yield the total amount of stress that an individual is under.
This scale, and others like it (Kranner, Coyne, Schaefer, & Lazarus., 1981; Cohen, Kamarack, & Mermelstein, 1983), were developed to assess the relationship between stress and negative health outcomes. As valuable as these stress inventories are, they do not capture the interactive depth and complexity of the full gamut of stresses that we are witnessing today. As a step towards addressing this issue, we asked the students in the Shanker Self-Reg® Master Class Level 2 to identify stresses in each of the five domains of Self-Reg. The resulting list (see Appendix) represents the fruits of their labour and the power of the Self-Reg framework. This inventory presents various stressors of which, some are easy to identify and others that involve a deeper, closer look. It is through the Self-Reg framework that these individuals have been trained to reframe their perspective on what a stressor is, as well as to recognize the signs and symptoms of stress.
We come back to our opening question of why so many children and teens today are over-stressed. In place of any sort of linear causal explanation, we need to look instead at the many different kinds of stresses that children and teens – and that we ourselves – are under, at the strain this imposes on an over-worked autonomic nervous system, and at the impact such a condition has on the capacity to cope with those positive stresses that, when the subject is properly restored, promote psychological and emotional growth. As is always the case with any seemingly intransigent problem, the first step towards a solution is understanding the full complexity of what one is dealing with.
Bernard, C. (1865/1957). An introduction to the study of experimental medicine. New York: Dover Publications.
Cannon, W.B. (1932). The wisdom of the body. New York: W.W. Norton & Co.
Cohen, S., Kamarack, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behaviour, 24(4), 385–396.
Gluckman, P., & Hanson, M. (2008). Mismatch: The lifestyle diseases timebomb. Oxford, UK Oxford University Press.
Hadfield, C. (2013). An astronaut’s guide to life on Earth. Toronto: Random House Canada.
Holmes, T., & Rahe, R. (1967). The social readjustment rating scale. Journal of Psychosomatic Research, 11 (2), 213–218.
Kleitman, N. (1963). Sleep and wakefulness. Chicago: University of Chicago Press.
Kranner, A., Coyne, J., Schaefer, C., & Lazarus, R. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4(1) pp. 1-39.
Shanker, S. (2016). Self-Reg: How to help your child (and you) break the stress cycle and successfully engage with life. Toronto: Penguin Random House Canada.
Viner, R. (1999). Putting stress in life. Social Studies of Science, 39(3), 391–410.
Shanker Self-Reg® Master Class Level 2: Stress Inventory
This stress inventory was created with the contributions of the Shanker Self-Reg® Master Class Level 2 students.
Good Stress Gone Bad: Conditions in Transforming Stress from Negative to Positive
Stuart Shanker, DPhil and Elisabeth Shepherd MSc
One of the basic precepts of Self-Reg® is that one’s level of energy and tension is critical for whether a particular stressor is experienced as positive or negative. This energy/tension state is largely a function of one’s overall stress load. Accordingly, the better we can manage our stress across five domains – biological, emotion, cognitive, social and prosocial – the better we can maximize positive arousal and performance.
Ever since Yerkes and Dodson (1908) published their “law” that performance improves with arousal but then deteriorates past a midpoint if arousal continues to increase, psychologists have sought to understand how we can manage arousal so as to maximize performance. The issue here is stress: How much is beneficial? When and why does it become detrimental? Can we transform negative into positive stress, and for that matter, prevent positive stress from turning negative? Is negative stress inherently, irrevocably negative? These are questions that lie at the very heart of Self-Reg.
Scan through the literature on stress and you find one group extolling its benefits (see, for example, McGonigal, 2015; Abbott, 2016) and another warning of its harmful effects (see, for example, Benson, 1975; Maté, 2004). This distinction was first made by Selye (1956, 1976) when he differentiated between eustress and distress. Eustress is physically as well as psychologically and emotionally beneficial (Sapolsky, 2014, 2015), while distress is potentially damaging to the body as well as the mind (Sternberg, 2001; Beauregard, 2004; Seligman, 2012; Fogel, 2013).
The problem is, one and the same stressor can lead to eustress for one individual and distress for another. Or, what has hitherto been a positive stressor can become negative (think of over-training). And one and the same stressor can be positive on one occasion and negative on another, or sometimes both – simultaneously!
The predominant approach to dealing with this complicated issue has been cognitive: that is, following the idea that how a stressor is perceived determines whether it is experienced as positive or negative (Everly & Lating, 2012; Solomon, Smith, Robins, & Fischbach, 1987). The emphasis has been on reappraisal (Cognitive Behavioral Therapy) or non-appraisal (non-judgmental mindfulness [see Kabat-Zinn, 1990]). Both strategies have been shown to be effective. But more is needed.
Your physiological reaction to a stimulus often determines how you perceive it. Someone with a heightened sensitivity to noise might find a cocktail party overwhelming, while someone with a low sensory threshold finds the same experience invigorating. What is more, reappraisal or non-appraisal do not help those who, for example, are sent into fight-or-flight by repetitive low-frequency sounds (Willis & Malcolm, 2016). But what is especially significant about conditions like misophonia, or the effect of heightened anxiety in general, is the impact of energy and tension on sensory reactivity.
In other words, where one sits on the Thayer matrix (see Figure 1) is central to the discussion of “transition conditions.”
Figure 1 – Adapted by The MEHRIT Centre from Robert E. Thayer (1996) The Origin of Every Day Moods: Managing Energy, Tension, and Stress, this matrix depicts various energy and tension states.
Not only does the state one is in profoundly influence how a stimulus is experienced, but the reverse is also true: that is, the stimuli you are subjected to profoundly influences the state you are in. One and the same stress can be experienced as positive if you are in a high energy/low tension state (HE/LT) and negative if you are in a low energy/high tension state (LE/HT). Hence it follows that the better one can identify and reduce the stressors that lead to LE/HT, the better one can manage how a particular stress is experienced.
The big complication in all this is that to exercise these self-regulation skills involves not just physical or emotional stress, but also cognitive, social, and prosocial stress (Shanker, 2016). These multiple stresses are inextricably bound together, constantly impinging on and intensifying one another. Hence the Yerkes-Dodson Law (see Figure 2) is a function of all five of the major stress domains addressed in Self-Reg.
Figure 2 – A conceptualization of the inverted-U, identifying the transition of positive stress to negative stress along arousal. Peak performance is achieved when energy depletion and recovery is balanced.
In Daniel Goleman’s (2012) terms, the far-left side of the curve represents “disengagement,” the far right side represents “frazzle,” and the midpoint represents “flow.” Flow denotes a balance between energy expenditure and recovery (sympathetic and parasympathetic processes). Too much stress leads to an excessive buildup of dopamine and cortisol (Csíkszentmihályi, 1990; Landhäußer & Keller, 2012), which in turn triggers limbic braking: the reason why performance declines markedly (Shanker, 2017).
What this means is that managing transition conditions has to be looked at through a holistic lens. That is, we need to identify the major stressors that are contributing to a state of hyper-arousal. Any particular stress that stands out needs to be understood within the complex of stresses the individual is dealing with (see “Hide and Seek,” this volume). To transform negative into positive stress, or prevent the reverse, one needs to look at all five domains of stress, identifying both hidden and overt stressors, as well as the interactional effect of stressors on one another (Shanker, 2016).
The development of stress-awareness is especially important. All too often, we only become aware that something is a negative stress when we have gone well past the midpoint of the arousal curve. But rather than trying to suppress or inoculate a negative stress, Self-Reg teaches us to view our response to a negative stressor as an invaluable signal as to where we sit on the arousal curve. And that in itself is a key to transforming negative stress into positive. By asking ourselves those perennial Self-Reg questions, “Why?” and “Why now?,” we can reframe a negative stressor. Even a seriously negative stress can be made positive provided we grasp what it is telling us.
In other words, from a Self-Reg perspective, the “transition conditions” that are such a pivotal aspect of self-regulation are as much a function of what we know as what we see and do. Or rather, what we know shapes what we see and do (see “Reframing,” this volume).
Abbott, S. (2016). Stress is good. Bloomington, IN: Xlibris.
Beauregard, M. (2004). Consciousness, emotional regulation and the brain. Amsterdam: John Benjamins Publishing Company.
Benson, H. (1975). The relaxation response. New York: Harper Collins.
Csíkszentmihályi, M. (1990). Flow: The psychology of optimal experience. New York: Harper Collins.
Everly, G. Jr., & Lating, J. (2012). A clinical guide to the treatment of the human stress response (3rd ed.). New York: Springer.
Fogel, A. (2013). Body sense: The science and practice of embodied self-awareness. New York: W.W. Norton and Company.
Goleman, D. (2012). The sweet spot for achievement: What’s the relationship between stress and performance? Retrieved from https://www.psychologytoday.com/blog/the-brain-and-emotional-intelligence/201203/the-sweet-spot-achievement
Kabat-Zinn, J. (1990). Full catastrophe living: Using the wisdom of your body and mind to face stress, pain, and illness. New York: Delacorte.
Landhäußer, A., & Keller, J. (2012). Flow and its affective, cognitive, and performance-related consequences. In S. Engeser (Ed.), Advances in flow research (pp.65–86). New York: Springer.
Maté, G. (2004). The cost of hidden stress: When the body says no. Toronto: Random House Canada.
McGonigall, K. (2015). The upside of stress. New York: Penguin Random House.
Sapolsky, R.M. (2015). Stress and the brain: Individual variability and the inverted-U. Nature Neuroscience, 18(10), 1344–1346.
Seligman, M. (2012). Flourish: A visionary new understanding of happiness and well-being. New York: Free Press.
Selye, H. (1956). The stress of life. New York: McGraw-Hill.
Selye, H. (1976). The stress of life (Rev. ed.). New York: McGraw-Hill.
Shanker, S. (2016) Self-Reg: How to help your child (and you) break the stress cycle and successfully engage in life. Toronto: Penguin Random House Canada.
Shanker, S. (2017). Reframing IQ: Releasing your child’s “limbic brakes”. Psychology Today, April 24 2017. Retrieved from https://www.psychologytoday.com/blog/self-reg/201704/reframing-iq
Solomon, S.D., Smith, E.M., Robins, L.N., & Fischbach, R.L. (1987). Social involvement as a mediator of disaster-induced stress. Journal of Applied Social Psychology, 17(12), 1092–1112.
Sternberg, E. (2001). The balance within: The science connecting heath and emotions. New York: W. H. Freeman and Company.
Yerkes, R.M., & Dodson, J.D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18, 459–482.
Willis, O. & Malcolm, L. (2016). Misophonia: When chewing, sniffing, tapping sounds are more than just annoying. ABC News, Sept 8 2016. Retrieved from http://www.abc.net.au/news/health/2016-09-09/misophonia-when-sounds-are-more-than-just-annoying/7826708
Self-Reg and Reframing
Stuart Shanker, DPhil and Casey Burgess MA
Shanker Self-Reg® always begins with reframing. The concept of reframing is grounded in the work of Ludwig Wittgenstein. Reframing constitutes an “aspect-shift” in how we see and categorize the world around us. Perceptual, experiential and creative components are all involved. The current paper explores each of these strands and how they are woven together. It then discusses how reframing applies, not only to behaviour, thought and emotion, but even to scientific research and theories.
Shanker Self-Reg® always begins with reframing. See a child as misbehaving and you are likely to respond in a harsh, even punitive manner. See the same child as exhibiting stress behaviour and you respond by helping that child to self-regulate. What is at stake here is not a different method of “managing a child’s behaviour.” The lesson here is rather: “See a child differently and you see a different child.”
There are three essential components involved in reframing: perceptual, experiential, and creative. By digging into existing research about reframing, our goal here is not only to understand each of these strands, but also to see how they are woven together.
My thinking about reframing was originally inspired by what Wittgenstein says about “aspect-shifts.” See a philosophical question differently, he argues, and you arrive at a whole new type of answer. Wittgenstein summed up what he had in mind here when he told Con Drury that he was thinking of using a quotation from King Lear, “I’ll teach you differences,” as the motto for Philosophical Investigations. This line captures the essence of Wittgenstein’s contribution to philosophy: the distinctions that he draws, for example, between science and philosophy, or between grammatical and empirical propositions, or between first-person and third-person psychological utterances. The reason why this sort of philosophical investigation is so important is because of the metaphysical confusions that result from failing to clarify these often very subtle logical distinctions (see Hacker 1972).
We could easily use the same quotation, “I’ll teach you differences,” as a motto for Self-Reg. Here too we are concerned with vitally important differences: for example, between self-control and self-regulation; misbehaviour and stress behaviour; oppositional defiance and fight-or-flight; compliance and freeze; lying and confabulation; not listening and not processing; lack of effort and limbic braking. However, in this case what is at stake is not resolving some metaphysical confusion, but the well-being of children and teens.
The idea behind reframing in this psychological context is that when, for example, you look at a child’s behaviour through a self-control lens you may see opposition, but look at it through a Self-Reg lens and what you see is fight-or-flight. Yet reframing is not some sort of “optometrical” phenomenon: the result of changing from the green-tinted glasses of self-control to the rose-tinted glasses of Self-Reg. The perceptual shift operating here involves a fundamental shift in categorization (Goldstone, 1994).
This is an idea that goes all the way back to Plato: specifically, his idea that we see the world through concepts, which we acquire – implicitly – when we learn how to speak (Baker & Hacker, 1980). We see what our parents teach us to see: our culture shapes what we perceive. When one sees a cow one might see a sacred creature; a demon; a food source; a dumb brute; a sentient creature that thinks and feels pain.
Each of these “ways of seeing” is embedded in a network of concepts (Goffman, 1974). When we reframe something, we are consciously shifting from one framework to another, and an essential aspect of the reframing is learning how to map the new conceptual terrain (Berger, 2008). For example, misbehaviour is internally related to the cluster of concepts that apply to purposeful actions: intentionality, choice, explanation, justification, responsibility. It is because of these conceptual links that we automatically think of punishment as a way of responding to misbehaviour. But stress behaviour belongs to the category of non-purposeful behaviour: that is, behaviours that are caused by sub-cortical processes. Stress behaviour is tied to arousal, tension, energy depletion, neurohormones, and in general, limbic processes. Hence our automatic reaction to stress behaviour is to understand and down-regulate.
When we reframe, we are not simply looking at the same thing through a different lens. What we see is completely different, and we begin to understand what this is by exploring the new conceptual links. But, whereas for philosophy reframing is essentially a logical exercise, for Self-Reg reframing involves an experiential shift that is every bit as powerful as the cognitive. It is not just what we see that is so different, but, essential to this process, what we feel.
These two aspects of reframing – perceptual and experiential – are bound up with one another. The more we can tease apart the conceptual links in the new framework, the more profoundly our emotional response to a child or teen is affected. But what changes most of all is what we start to ask. For reframing not only involves a new way of answering old and established questions, but also raises entirely new ones and leads us in directions that were not, and likely could not have been discerned in the old framework.
The third essential aspect of Self-Reg reframing is that it sparks off creative new ways of thinking: about children and teens, about ourselves, about theories and experimental paradigms. In this sense, reframing is like a closure impossibility proof in mathematics (Shanker, 1988): it closes off one line of thinking (for example, self-control) while opening up a completely new one (self-regulation). This is the reason why Gödel’s theorem was so important for mathematicians and why Self-Reg is so important for parents and educators. We truly do not know where a reframing is going to lead: only that this is a path that we must follow.
Following the path of reframing the existing scientific literature about stress and stress management will be a key element in Self-Reg research. Through re-perceiving and re-experiencing rigorous research, and finding creative ways of thinking about how this research touches current challenges, we can take our understanding of stress and Self-Reg in hitherto unseen directions with productive results for all.
Baker, G.P., & Hacker, P.M.S. (1980). Wittgenstein: Understanding and meaning. Oxford: Blackwell.
Berger, J. (2008). Ways of seeing. London: Penguin.
Goffman, E. (1974). Frame analysis. New York: Harper & Row.
Goldstone, R.L. (1994). Influences of categorization on perceptual discrimination. Journal of Experimental Psychology, 123, 178–200.
Hacker, P.M.S. (1972). Insight and illusion: Themes in the philosophy of Wittgenstein. Oxford: Clarendon Press.
Shanker, S. (Ed.). (1988). Gödel’s theorem in focus. London: Routledge.
Reframing : A Literature Review
Casey Burgess MA
Dr. Shanker’s paper (this volume) examines the concept of reframing – the idea that there are multiple meanings of everything that may be perceived. Goffman’s Frame Analysis (1974) explains reframing as an examination of the terms of the organization of experience. While frames can help us to understand other perspectives, difficulties with framing can arise due to ambiguity or framing errors caused by engaging in inaccurate premises without trying to determine what is really going on. Often, among scientific disciplines, a lack of communication can occur when one field accepts a hypothesis and another discredits it (Entman, 1993), leaving in its wake a need for cross-disciplinary research to push understanding forward.
Reframing has often been seen in scientific literature, leading to new research-grounded ways of using existing knowledge for advancement in various fields. Reframing is evident, for instance, in the following: a shift to a heliocentric view of the universe (Weinert, 2014); a shift to seeing one thing in two ways (Berger, 1972); a shift in the narrative of battered women from decline to growth (Arnold & Ake, 2013); a shift from segregation to mainstreaming to inclusion (Terzi, 2014); and a shift from division and scripted understanding to unscripted truth and reconciliation (Tovares, 2016). Hundreds of articles can be found explaining how a new frame for a problem can lead to new approaches and interventions in risky behaviour (Lustig & Sung, 2013; Pingel, Bauermeister, Johns, Eisenberg, & Leslie-Santana, 2013), domestic violence (Arnold & Ake, 2013; Behr, Grit, Bal, & Robben, 2015), leadership (Murray & Clark, 2013; Raelin, 2016), education (Masocha, 2015; Winstone & Millward, 2012), and developmental disabilities (Grinker, 2015; Lester & Paulus, 2014).
Historically, self-regulation itself has been framed in a variety of ways, from being seen as behaviour-based and involving compliance and will prior to the 1950s to being seen in the light of emergent and contemporary research (1950-1990), which became focused on cognition and its implications to development (Post, Boyer, & Brett, 2006). Expansionist research, from 1990 to the present, shows self-regulation to be linked to every category of psychology with a wide range of contexts and directions (Post et al., 2006). With 447 different uses of the term self-regulation in the literature (Burman, Green, & Shanker, 2015), a consistent framework grounded in rigorous science is needed to approach society’s growing problems, such as increases in physical health issues like obesity and autoimmune diseases, internalizing and externalizing problems, risky behaviour, and poor education outcomes (“People For Education 2010 Keynote Address, ” 2010).
The Shanker Self-Reg® framework (Shanker, 2012, 2016) provides a reframing of existing behaviour-based models of child and adult well-being by looking at known developmental trajectories with a neuropsychological lens and defining self-regulation as the body’s ability to respond to and recover from stressors. Reframing human development and well-being in this manner opens the door to a potential shift in responses and interventions for child development, both typical and exceptional.
Research on the process of reframing is very limited, but does center around the art of thinking differently, based on Kuhn’s ideas of paradigm revolution: trying to determine why we do the things we do, and how we might do them differently (Benammar, 2012). Little research goes into any detail on reframing beyond this work. Future research is warranted on the process by which we can look at existing knowledge with a new lens, in order to expand on its application within developmental psychology and child intervention.
Arnold, G., & Ake, J. (2013). Reframing the narrative of the battered women’s movement. Violence Against Women, 19(5), 557–578. doi:http://dx.doi.org/10.1177/1077801213490508
Behr, L., Grit, K., Bal, R., & Robben, P. (2015). Framing and reframing critical incidents in hospitals. Health, Risk & Society, 17(1), 81–97. doi:http://dx.doi.org/10.1080/13698575.2015.1006587
Benammar, K. (2012). Reframing – The Art of Thinking Differently. [Kindle e-book]: Uitgeverij Boom
Berger, J. (1972). Ways of seeing. London: Penguin Books.
Burman, J.T., Green, C.D., & Shanker, S. (2015). On the meanings of self-regulation: Digital humanities in service of conceptual clarity. Child Development, 86(5), 1507–1521.
Entman, R.M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51–58.
Goffman, E. (1974). Frame analysis. New York: Harper & Row.
Grinker, R.R. (2015). Reframing the science and anthropology of autism. Culture, Medicine and Psychiatry, 39(2), 345–350. doi:http://dx.doi.org/10.1007/s11013-015-9444-9
Lester, J.N., & Paulus, T.M. (2014). “That teacher takes everything badly”: Discursively reframing non-normative behaviors in therapy sessions. International Journal of Qualitative Studies in Education, 27(5), 641–666. doi:http://dx.doi.org/10.1080/09518398.2013.805446
Lustig, D.F., & Sung, K.K. (2013). Dissolving borders: Reframing risk, delinquent peers, and youth violence. Children and Youth Services Review, 35(8), 1197–1205. doi:http://dx.doi.org/10.1016/j.childyouth.2013.02.013
Masocha, S. (2015). Reframing black social work students’ experiences of teaching and learning. Social Work Education, 34(6), 636–649. doi:http://dx.doi.org/10.1080/02615479.2015.1046429
Murray, J., & Clark, R.M. (2013). Reframing leadership as a participative pedagogy: The working theories of early years professionals. Early Years: An International Journal of Research and Development, 33(3), 289–301. doi:http://dx.doi.org/10.1080/09575146.2013.781135
People For Education 2010 Keynote Address. (2010). People for Education Conference 2010. Toronto, ON.
Pingel, E.S., Bauermeister, J.A., Johns, M.M., Eisenberg, A., & Leslie-Santana, M. (2013). “A safe way to explore”: Reframing risk on the Internet amidst young gay men’s search for identity. Journal of Adolescent Research, 28(4), 453–478. doi:http://dx.doi.org/10.1177/0743558412470985
Post, Y., Boyer, W., & Brett, L. (2006). A historical examination of self-regulation: Helping children now and in the future. Early Childhood Education Journal, 34(1), 5–14. doi:10.1007/s10643-006-0107-x
Raelin, J.A. (2016). Imagine there are no leaders: Reframing leadership as collaborative agency. Leadership, 12(2), 131–158. doi:http://dx.doi.org/10.1177/1742715014558076
Shanker, S. (2012). Calm, alert, and learning: Classroom strategies for self-regulation. Toronto: Pearson Education Canada.
Shanker, S., Barker, T. (2016) Self-Reg: How To Help Your Child (And You) Break The Stress Cycle and Successfully Engage With Life. Penguin Random House Canada
Terzi, L. (2014). Reframing inclusive education: Educational equality as capability equality. Cambridge Journal of Education, 44(4), 479–493. doi:http://dx.doi.org/10.1080/0305764X.2014.960911
Tovares, A.V. (2016). Going off-script and reframing the frame: The dialogic intertwining of the centripetal and centrifugal voices in the Truth and Reconciliation Commission hearings. Discourse & Society, 27(5), 554–573.
Weinert, F. (2014). Lines of descent: Kuhn and beyond. Foundations of Science, 19(4), 331. doi:10.1007/s10699-013-9342-y
Winstone, N., & Millward, L. (2012). Reframing perceptions of the lecture from challenges to opportunities: Embedding active learning and formative assessment into the teaching of large classes. Psychology Teaching Review, 18(2), 31–41.
Masking Stress with Misbehaviour : A Shanker Self-Reg® Lens
Susan Hopkins EdD and Elisabeth Shepherd MSc
The concept of self-regulation has been present in the literature for over 150 years. However, many believe that self-regulation and self-control are synonymous. According to Shanker Self-Reg®, self-regulation makes self-control possible by understanding stress and effectively managing our energy and tension levels. This paper discusses and provides evidence in support of the key constructs of Shanker Self-Reg®, including the 5 Steps of The Shanker Method® (reframe, recognize, reduce, reflect and respond), the distinction between stress behaviour and misbehaviour, the importance of reframing what is often viewed as misbehaviour as stress behaviour, and much more.
An adolescent tells her mother she doesn’t care about school and storms out of the room, slamming the door behind her.
A student in Grade 4 punches another student in the playground for no apparent reason. This happens often.
A college student procrastinates on completing a final paper for a course and after multiple chances to submit the paper late just gives up and fails the course.
A three-year-old has a tantrum at the department store when her dad says no to buying the toy she wants him to buy.
A teacher loses her temper and yells at her students after telling them for the third time to work quietly.
A bus driver humiliates a senior citizen when she puts the wrong amount of money in for the bus trip because she didn’t know the cost of the fare had gone up.
Stuart Shanker contends that when we see a person differently, we see a different person (Shanker, 2016). Any of the examples of misbehaviour listed above can be reframed as stress behaviour, thereby deepening understanding of the roots of the behaviour and opening up new ways of responding. Shanker Self-Reg® is a five-step method for managing stress, which involves recognizing the signs of stress behaviour; identifying and reducing negative stresses; becoming aware of the signs of escalating stress; and developing customized strategies for returning to a state of being calmly focused and alert (Shanker, 2016). The difference between self-regulation and self-control, misbehaviour and stress behaviour, and the five steps of Shanker Self-Reg® are discussed in this paper.
Untangling Self-Regulation from Self-Control
Burman, Green, and Shanker (2015) documented 447 different uses of the term “self-regulation” in the psychological literature. The multiple uses of “self-regulation” were categorized into six concept-clusters: self-control, including emotional regulation and control; self-monitoring; self-management; social behavior; agency or self-determination; and self-regulated learning. According to Shanker, self-control serves as the overarching construct linking these six clusters of self-regulation definitions. It is important to note that self-control and self-regulation are not synonymous in Shanker Self-Reg®, which focuses on self-regulation. Self-regulation from the psychophysiological lens is what makes self-control possible.
Self-control became a focus in psychological research largely due to the “delay of gratification” studies that began to appear in the late 1960s (Mischel, 2014; Mischel, Ebbesen, & Raskoff Zeiss, 1972). These studies showed that problems in self-control could be detected in children as young as four, and that these problems were associated with challenges in emotion-regulation and executive functions (Eisenberg et al., 1995; Blair & Razza, 2007; Diamond & Lee, 2011). The self-control paradigm became dominant because of the longitudinal studies showing that the children identified at a young age as having poor self-control fared worse over the long run, both physically and academically, and had significantly higher rates of internalizing and externalizing disorders as young adults (Moffitt et al. 2011; Mischel, Shoda, & Rodriguez, 1989). This research led many to conclude that children should be taught in primary school how to control their impulses (Schlam, Wilson, Shoda, Mischel, & Ayduk, 2013; Diamond, Barnett, Thomas, & Munro, 2007).
The Psychophysiological Definition of Self-Regulation
In 1865, the father of modern physiology, Claude Bernard, inaugurated the scientific study of what came to be known as “self-regulation.” Bernard was interested in the mechanisms that enabled an organism to maintain a stable internal state in response to both internal and external “perturbations,” what Walter Bradford Cannon (1932) later defined as “stressors.” In its original psychophysiological sense, self-regulation refers to the way one recovers from the expenditure of energy required to deal with stressors.
In psychophysiology terms, self-regulation is a prerequisite for exercising self-control. An unstable internal state can lead to a limbic response – fight-or-flight, or freeze (a primitive neural response to threat easily misconstrued as compliance) – and impinge on the functioning of the prefrontal cortex, the part of the brain governing self-control (Porges, 2011; McEwen, 2007). The more an individual is chronically hypo- or hyper-aroused because of excessive stress, the more readily that person goes into fight-or-flight, or freeze (Lillas & Turnbull, 2008). These fight, flight, and freeze limbic states suppress, and at times “brake,” the necessary mechanisms in the prefrontal cortex for the practice of self-control.
The psychophysiological definition of self-regulation on which Shanker Self-Reg® is based refers to how effectively we manage stress. When one is over-stressed, tension increases markedly and energy reserves drop sharply. In what Shanker describes as a “red brain” state, limbic arousal is heightened, fight-or-flight responses are easily triggered, and the “blue brain” (prefrontal cortex) functions critical for learning and well-being are suppressed.
The Five Steps of Shanker Self-Reg®
Grounded in the psychophysiological science of self-regulation (Fogel, 2013; Greenspan & Shanker, 2006; McEwan, 2007; Porges, 2001, 2011) combined with a Dynamic Systems Theory lens (Van Gelder, 1998), Shanker Self-Reg® is a method for understanding and managing stress. Self-Reg was first described in detail by Shanker in Calm, Alert & Learning: Strategies for the K–6 Classroom (2012). In Shanker’s most recent writings (2016), this five-step process, practiced as part of everyday life, enhances self-regulation in children, youth, and adults:
- read the signs and “reframe” the behaviour;
- recognize the stressors across all five domains of experience – biological, emotion, cognitive, social, and prosocial;
- reduce the stressors and lighten the stress load;
- reflect – enhance stress awareness by becoming aware of what it feels like to be calm and when you’re in fight-or-flight or freeze;
- respond – develop personalized strategies to reduce tension and restore energy by figuring out what brings you back to being calm.
Reflect on the scenarios in the introduction to this article – an angry adolescent, an aggressive Grade 4 student, a procrastinating college student, a child having a tantrum, a teacher who loses their temper, and a bus driver who takes his frustration out on a senior citizen. As we reframe with Shanker Self-Reg® we ask ourselves reflectively: “why this person” and “why now?” How might their behaviour be derived from the accumulation of stressors they are experiencing? Here are two examples of seeing a person differently – through the lens of Shanker Self-Reg® – to recognize that what appears to be misbehaviour may in fact be stress behaviour:
Instead of seeing a yelling teacher, see a teacher who has been struggling with spring allergies for weeks, is worried about her ailing father’s health, has a teenage daughter who has told her she hates her three times this week, and spent lunch hour once again supporting her colleague who was in tears feeling overwhelmed with the many needs in her classroom this year that she can’t seem to meet. You will see a teacher with an excessive stress load, the signs of which leaked out through yelling in frustration, not so unlike the adolescent with the limbic utterance: “I don’t care,” when she really does, quite deeply.
Instead of seeing an abusive bus driver, see a bus driver who has been struggling with horrible traffic all day, and has spent too many hours without moving, maybe without eating, and you see, not an ogre, but someone whose needs in that moment are not that different from the three-year-old in the department store. A gentle word to him, and to the flustered senior citizen, might just be all that is needed to help them connect with each other in a way that is good for both of them.
Shanker’s work emphasizes that enhancing one’s self-regulation requires learning to understand and manage stress through an ongoing iterative and developmental process. In other words, Self-Reg is about stress and the connected energy and tension states beneath the problematic behaviours, not the behaviours themselves. Misbehaviour is a mask, it is what is visible on the surface. Using Shanker Self-Reg® we can gently begin to peer behind the mask to better understand the excessive stress that lies beneath the outward “misbehaviour.” Seeing people differently with science-informed “soft eyes” brings with it the most amazing shift: we truly do, as Shanker says, see a different person every time.
Table 1 identifies and defines many of the key concepts of the Shanker Self-Reg® Framework. This table additionally includes research describing the origins of and supporting the inclusion of these constructs.
Table 1: Key Definitions and Research Underpinning the Shanker Self-Reg® Framework
Argyle, M. (2007). Bodily communication (2nd ed.). London: Routledge.
Argyle, M., & Dean, J. (1965). Eye-contact, distance and affiliation. Sociometry, 28(3), 289–-304.
Baumeister, R.F., & Tierney, J. (2011). Willpower: Rediscovering the greatest human strength.
New York: Penguin Press.
Blair, C., & Razza, R.P. (2007). Relating effortful control, executive function, and false belief
understanding to emerging math and literacy ability in kindergarten. Child Development, 78(2), 647–663.
Brazelton, T.B. (1961). Psychophysiologic reactions in the neonate: I. The value of observation
of the neonate. The Journal of Pediatrics, 58(4), 508–512.
Brazelton, T.B., & Nugent, J.K. (1995). Neonatal behavioral assessment scale. Cambridge, UK:
Cambridge University Press.
Burman, J.T., Green, C.D., & Shanker, S. (2015). On the meanings of self-regulation: Digital
humanities in service of conceptual clarity. Child Development, 86(5), 1507–1521.
Cannon, W.B. (1932). The wisdom of the body. New York: W.W. Norton & Company.
Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in
children 4 to 12 years old. Science, 333(6045), 959–964.
Diamond, A., Barnett, W.S., Thomas, J., & Munro, S. (2007). Preschool program improves
cognitive control. Science, 318(5855), 1387–1388.
Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., & Garnero, L. (2010). Inter-brain
synchronization during social interaction. PLoS ONE, 5(8), e12166.
Dunsworth, H.M., Warrener, A.G., Deacon, T., Ellison, P.T., & Pontzer, H. (2012). Metabolic
hypothesis for human altriciality. Proceedings of the National Academy of Sciences of the United States of America, 109(38), 15212–15216.
Eisenberg, N., Fabes, R.A., Murphy, B., Maszk, P., Smith, M., & Karbon, M. (1995). The role of
emotionality and regulation in children’s social functioning: A longitudinal study. Child Development, 66(5), 1360–1384.
Ekman, P. (1970). Universal facial expressions of emotion. California Mental Health Research
Digest, 8(4), 151-158.
Feldman, R. (2012). Oxytocin and social affiliation in humans. Hormones and Behavior, 61,
Field, T., Diego, M., & Hernandez-Reif, M. (2010). Prenatal depression effects and
interventions: A review. Infant Behavior and Development, 33(4), 409–418.
Fogel, A. (1993). Developing through relationships. Chicago: University of Chicago Press.
Fogel, A. (2013). Body sense: The science and practice of embodied self-awareness. New York
: W.W. Norton & Company.
Gould, S.J. (1976). Human babies as embryos. Natural History, 85(2), 22–26.
Grandjean, P., & Landrigan, P.J. (2014). Neurobehavioural effects of developmental toxicity.
The Lancet Neurology, 13(3), 330–338.
Greenough, W.T., Black, J.E., & Wallace, C.S. (1987). Experience and brain development.
Child Development, 58, 539–559.
Greenspan, S.I. (n.d.) Working with children who show attention problems. Scholastic, Early Childhood Today. Retrieved from http://teacher.scholastic.com/professional/childdev/attentionproblems.htm
Greenspan, S.I. (2001). The affect diathesis hypothesis: The role of emotions in the core deficit
in autism and in the development of intelligence and social skills. Journal of Developmental and Learning Disorders, 5(1), 1–45.
Greenspan, S.I., & Shanker, S. (2006). The first idea: How symbols, language, and intelligence
evolved from our primate ancestors to modern humans. Boston, MA: Da Capo Press.
Izard, C.E. (1992). Basic emotions, relations among emotions, and emotion-cognition
relations. Psychological Review, 99(3), 561–565.
King, B.J. (2009). The dynamic dance: Nonvocal communication in African great apes.
Cambridge, MA: Harvard University Press.
Lewis, T., Amini, F., & Lannon, R. (2007). A general theory of love. New York: Knopf
Doubleday Publishing Group.
Lillas, C., & Turnbull, J. (2008). Infant child mental health early intervention: A neurorelational
framework for interdisciplinary practice. New York: W.W. Norton & Company.
MacLean, P. (1990). The triune brain in evolution: Role in paleocerebral functions. New York
: Springer Publishing.
McCain, M.N., Mustard, J.F., & Shanker, S. (2007). Early years study 2: Putting science into
action. Toronto: Council for Early Child Development.
McEwen, B.S. (1998). Stress, adaptation, and disease: Allostasis and allostatic load. Annals of
the New York Academy of Sciences, 840(1), 33–44.
McEwen, B.S. (2007). Physiology and neurobiology of stress and adaptation: Central role of
the brain. Physiological Reviews, 87(3), 873–904.
Mischel, W. (2014). The marshmallow test: Mastering self-control. New York: Little, Brown
Mischel, W., Ebbesen, E.B., & Raskoff Zeiss, A. (1972). Cognitive and attentional mechanisms
in delay of gratification. Journal of Personality and Social Psychology, 21(2), 204–218.
Mischel, W., Shoda, Y., & Rodriguez, M.L. (1989). Delay of gratification in
children. Science, 244(4907), 933.
Moffitt, T.E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R.J., Harrington, H., Caspi, A.
(2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 2693.
Porges, S.W. (2001). The polyvagal theory: Phylogenetic substrates of a social nervous system.
International Journal of Psychophysiology, 42(2), 123–146.
Porges, S.W. (2011). The polyvagal theory: Neurophysiological foundations of emotions,
attachment, communication, self-regulation. New York: W.W. Norton & Company.
Portmann, A. (1941). Die Tragzeiten der Primaten und die Dauer der Schwangerschaft beim
Menschen: ein Problem der vergleichenden Biologie. Rev. Suisse Zool, 48, 511–518.
Schlam, T.R., Wilson, N.L., Shoda, Y., Mischel, W., & Ayduk, O. (2013). Preschoolers’ delay of
gratification predicts their body mass 30 years later. The Journal of Pediatrics, 162(1), 90–-93.
Seligman, M.E. (1972). Learned helplessness. Annual Review of Medicine, 23(1), 407–412.
Selye, H. (1956). The stress of life. New York: McGraw-Hill.
Selye, H. (1976). Stress without distress. In G. Serban (Ed.), Psychopathology of Human
Adaptation (pp. 137–146). New York: Springer Publishing.
Shanker, S. (2016). Self-Reg: How to help your child (and you) break the stress cycle and
successfully engage with life. Toronto: Penguin Random House.
Tronick, E., Adamson, L.B., Als, H., & Brazelton, T.B. (1975). Infant emotions in normal and
pertubated interactions. Paper presented at the Society for Research in Child Development, Denver, CO.
Van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain
Sciences, 21(05), 615–628.
Waddington, C.H. (1942). Canalization of development and the inheritance of acquired
characters. Nature, 150(3811), 563–565.
Zahn, R., Moll, J., Paiva, M., Garrido, G., Krueger, F., Huey, E.D., & Grafman, J. (2009). The
neural basis of human social values: Evidence from functional MRI. Cerebral Cortex, 19(2), 276–283.
Self-Regulation, Self-Control, and the Practice of Shanker Self-Reg®
Susan Hopkins EdD, Stuart Shanker DPhil, and Rebecca Leslie BEd
The Shanker Self-Reg® framework, based on the psychophysiological understanding of self-regulation and the hierarchy of human stress responses, differs from programs rooted in self-control-based conceptions of self-regulation. Self-Reg is a process rather than a program. It applies to everyone —children, youth and adults — rather than students in general, or specific age groups or subgroups of students. Self-Reg encourages reflective thinking that helps people understand and respond to stressors and internal states in order to bring online the brain mechanisms that enable exercise self-control, learning and overall well-being. These differences are illuminated through a comparison of Self-Reg to two popular self-regulation programs.
One of the central tenets in all the work we do with parents and educators is that Self-Reg is a process and not a program. But why not both? Why is it so important to draw this distinction, and if it is so important that we avoid seeing Self-Reg as a program, what sort of process is it and how is this mastered?
The key to answering this question lies in the fundamental Self-Reg principle: “See a child differently and you see a different child.” Recognize, for example, when a child is exhibiting stress behaviour rather than misbehaving, and you immediately begin to consider what the stresses might be that are over-loading this child. But so much more is involved here than a simple change in perspective. If you see that a child is in fight-or-flight rather than being oppositional, everything about your interaction with that child instantly changes: what you feel and how you respond; the subtle cues that you give off in your tone of voice, facial expression, gestures, and so on; and what the child feels and how the child responds.
Such an “aspect-shift” does not come easily, or all at once. For one thing, we ourselves have a limbic system that is easily aroused by a child’s arousal, and our own fight-or-flight reaction is easily triggered when this happens. For another, we have a deeply entrenched mindset that sees all stress behaviour as misbehaviour, and therefore as a sign of weakness or self-indulgence. Then there is the fact that we have a scientific culture telling us that it is relatively straightforward to produce the behaviours that we want, and if you are not seeing the intended results you need to try harder. And there are insidious thoughts, like: “Spare the rod and spoil the child,” or “That’s how I was raised and I turned out okay.”
In practicing Self-Reg, we are forever asking: “Why am I seeing this behaviour or reaction?”: bearing in mind that every child is different and every situation is different. Accordingly, Self-Reg is inherently flexible and adaptive. It demands observation, reflection, trial and error, and self-awareness. It sees the learning curve as open-ended and, for that matter, endless. And above all, it seeks to transform a me–them relationship, where children or students are seen as needing to be controlled, to an I–thou relationship that is nurtured by understanding and insight.
All of these reasons help those of us who are practitioners to understand why it is so important to see Self-Reg as a process and not a program. One of the biggest problems with many programs – even, as we’ll see below, programs that are explicitly intended to foster self-regulation – is that they may actually impede the sort of reflective thinking that Self-Reg inspires. There is a danger here that the authoritarian mindset towards children gets transferred to those following the program. That is, the drive for inquisitiveness is replaced by the demand for fidelity; thinking that should be creative becomes regimented. And the demarcation between self-control and self-regulation is blurred: in some cases, even obliterated.
Distinguishing Between Programs for Self-Control and Self-Regulation as a Process
There is a fundamental conceptual distinction between self-control and self-regulation. It is a critical distinction because the two concepts are frequently confounded (Shanker, 2016). As a result, it is not at all clear, when a program of some kind has shown to have had some positive effects on children’s attention, mood, or behaviour, whether these were due to the self-control or the self-regulation component, or possibly a synergistic effect between the two. The second reason why it is vital to be clear on this distinction is that it brings to the fore the question of what we should be endeavouring to teach: is it self-control, or is it self-regulation?
The answer for Self-Reg is clearly self-regulation. Self-regulation is, in fact, what makes self-control possible. In a state of heightened stress, a child is unable to benefit from training designed to foster self-control. The key to changing a child’s trajectory is to identify and reduce his stress load, rather than trying to teach better self-control; the latter emerges naturally as a result of improved self-regulation. But this in turn raises the question: How do we “teach” self-regulation at a universal level, especially considering the additional challenge that what is a stressor for one child may not be for another, and that, even for the same child, what may be a stressor in one moment may not be in another when the child is in a different physical or emotional state.
It is this individual variability of stress-reactivity that represents our greatest challenge as we undertake to institute and assess universal approaches to enhancing self-regulation. Clinical studies have demonstrated that it is indeed possible to enhance a child’s self-regulation, and that doing so results in meaningful developmental changes (Casenhiser, Binns, McGill, Morderer, & Shanker, 2015; Casenhiser, Shanker, & Steiben, 2013). But again, do we take such a clinical approach to scale to help those children who might be struggling, but also to enhance the self-regulation of all children? And if so, how?
To complicate the issue still further, what proves to be effective might vary from person to person, classroom to classroom, school to school, community to community. So rather than thinking of instituting a universal self-regulation program, we need to think of self-regulation as an educational process.
One response has been to teach children various types of relaxation and meditation practices in order to help them learn how to control their thoughts and emotions. Here too we see mixed results: many children find such activities taxing because it’s dysregulating in some way. The dysregulation is unique to the individual. Perhaps the child is not yet developmentally ready to focus on her breathing, or to sit still for longer than a few minutes because she has not yet developed sufficient “emotional intelligence” to understand, much less identify and express, what she is feeling, or because of sensory-motor compromises that render these exercises highly stressful rather than calming.
It is understandable that educators might be drawn to programs that promise a “quick fix” to problems in self-regulation, but our experience has been that there is no such thing: especially with children. All too often, the quick fix in question turns out to be trying to teach the children about self-regulation, as opposed to helping them learn how to self-regulate. Take a concept like calmness, which is actually quite complicated: it has a physical component (the feeling of relaxed muscles, your heart and breathing slowing down); an emotional component (the enjoyment of the feeling of calming down); and a cognitive element (the awareness of what one is experiencing). Without a mastery of all three elements, children can easily confuse being quiet with being calm. They really don’t know, in their body, what “calm” means, let alone regard this as a pleasant state. In fact, just the opposite is often the case: they might comply, but only from a wish to please their teacher, or because of the power dynamics, and not from any genuine awareness of their tension and a desire to release it. So, it turns out once again that it is self-control that we are unwittingly working on, and not self-regulation.
A self-control focus overlooks the major question of whether such programs are beneficial. Even if it were shown that teaching children to control their impulses is effective in primary school, as has been argued, we would still be left with the serious question of whether it would be beneficial to attempt something similar in early learning centres and older grades, or whether using behaviour modification techniques to try to instil self-control in children might lead to problems in mood, attention, and behaviour.
The child might well become more compliant – at least for the short term – as a result of such self-control oriented practices, while undergoing and even becoming habituated to a state of heightened arousal. It is important to keep in mind that being quiet and still should not be conflated with being calm and attentive. These concepts belong to very different families with very different histories and, indeed, are subserved by very different parts of the brain. The former is concerned with acquiring the “cognitive competencies” (Mischel, 2014) and even the “willpower” (Baumeister & Tierney, 2011) to inhibit impulses and ignore distractions; the latter is concerned with understanding and reducing the causes of heightened arousal that leads to impulsivity and distractibility (Shanker, 2012). If learning and well-being are our priorities, we are after calm and attentive; quiet and still denotes compliance (or worse, a “freeze” response to stressors), and these states seldom live side by side.
Comparing Approaches to Developing Students’ Self-Regulation
Programs targeting outcomes of self-regulation are used in schools. Consider the challenges discussed of the common confounding of self-control and self-regulation in the psychophysiological sense and it becomes clear that there are not only different approaches to developing self-regulation in students, but also different theoretical foundations of these programs. While they may appear to be interchangeable, they may in fact be working on very different priorities. Two common programs we have come across in North American schools that address self-regulation are: Zones of Regulation® and MindUp®. With a goal of clarifying similarities and differences among these self-regulation–focused approaches, we undertook a scan of these two programs alongside the Shanker Self-Reg® framework (see Figure 1).
Figure 1: Comparison of Programs against Shanker Self-Reg®
The Shanker Self-Reg® framework, which is based on the psychophysiological understanding of self-regulation, is set apart from the other two programs compared in Figure 1 because it addresses the hierarchy of stress responses as outlined by Porges (2001): social engagement, fight-or-flight, and freeze. Where MindUp and Zones of Self-Regulation require a state of control to self-manage, engage in, and respond to the stressors experienced in a given moment or in reflection, the practice of Self-Reg encompasses the fight, flight, and freeze states, and neuroception in general – the limbic system’s response to feeling unsafe. Note that this is different than thinking about or talking about these states. In Self-Reg the focus is on responding to these states. This is precisely the reason why self-regulation makes self-control possible: it brings the brain mechanisms involved “online” and ensures they are able to respond with control when needed, although it should be noted that, the better the child self-regulates, the less the need for self-control.
As is clear from the foregoing, the reflective practice of Self-Reg is about so much more than just instituting an attitudinal shift; it is about developing an understanding of behaviour that is grounded in the recent advances that have occurred in psychophysiology and neuroscience. The more we learn about the dynamic interplay between neocortical and subcortical processes, the clearer it becomes that the emphasis on self-control is often misplaced and, in many cases, harmful.
To this end, The MEHRIT Centre (TMC) provides resources on Self-Reg, including blogs, printable information sheets, videos, podcasts, a parenting magazine, newsletters, and presentations. Opportunities to take courses and become certified in the Foundations of Self-Reg, or to become a facilitator of others’ learning the method, are available, as are face-to-face learning options and online communities.
Our experience to date has been that it is only by engaging in this sort of intensive learning experience that self-regulation truly comes to life: not simply in terms of the “new way of seeing children” described above, but also in terms of a new way of seeing ourselves, our own stresses, and the need to work on our own self-regulation. And this may be the most important point of all: Self-Reg is a process of change. And it applies to all: children, teens, parents, teachers, young adults, seniors. Everyone.
Baron-Cohen, S. (2006). The hyper-systemizing, assortative mating theory of autism. Progress in Neuro-Pyshcopharmacology & Biological Psychiatry, 30(5), 865-872.
Baumeister, R.F., & Tierney, J. (2011). Willpower: Rediscovering the greatest human strength. New York: Penguin Press. Baumrind, D. (1967). Child care practices anteceding three patterns of preschool behavior. Genetic Psychology Monographs, 75(1), 43-88.
Buron, K.D., & Curtis, M. (2004). The incredible 5 point scale: Assisting students with autism spectrum disorders in understanding social interactions and controlling their emotions. Shawnee Mission, KS: Autism Asperger Publishing Company.
Cannon, W.B. (1932). The wisdom of the body. New York, NY: W. W. Norton & Co.
Casenhiser, D.M., Binns, A., McGill, F., Morderer, O., & Shanker, S.G. (2015). Measuring and
supporting language function for children with autism: Evidence from a randomized control trial of a social-interaction-based therapy. Journal of Autism and Developmental Disorders, 45(3), 846–857. doi:http://dx.doi.org- /10.1007/s10803-014-2242-3
Casenhiser, D.M., Shanker, S.G., & Stieben, J. (2013). Learning through interaction in children with autism: Preliminary data from a social-communication-based intervention. Autism, 17(2), 220–241. doi:http://dx.doi.org/10.1177/13623-61311422052.
Dawson, P. & Guare, R. (2009). Smart but scattered: The revolutionary “executive skills” approach to helping kids reach their potential. New York, NY: Guilford Press.
Fogel, A., King, B., & Shanker, S. (2007). Human Development in the Twenty-First Century: Visionary Ideas from Systems Scientists. Cambridge, UK: Cambridge University Press.
Frith, U. (1989). Autism: Explaining the Enigma. Oxford, England: Blackwell Publishing House.
Gould, S. J. (1977). Ontogeny and Phylogeny. Cambridge, MA: Harvard University Press.
Greenspan, S. (1997). The Growth of the Mind and the Endangered Origins of Intelligence with Beryl Benderly. Perseus Books.
Klin, A., Jones, W., Schultz, R., & Volkmar, F. (2003). The enactive mind, or from actions tocognition: lessons from autism. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 358(1430), 345-360. doi: 10.1098/rstb.2002.1202
Kopp, C. (1982). Antecedents of self-regulation: A developmental perspective. Developmental Psychology, 18(2), 199-214.
Kuypers, L.M. (2011). The Zones of Regulation®: A curriculum designed to foster self-regulation and emotional control. San Jose, CA: Think Social Publishing.
Maclean, P. (1990). The Triune Brain in Evolution: Role in Paleocerebral Functions. New York, NY: Springer.
Maloney, J., Schonert-Reichl, K., Whitehead Arruda, J., & Lawlor, M. (2016). A mindfulness-based social and emotional learning curriculum for school-aged children: The MindUP Program mindfulness-based interventions and social and emotional learning. In K. Schonert-Reichl & R. Roeser (Eds.), Handbook of mindfulness in education: Integrating theory and research into practice (pp. 313-334). New York, NY: Springer-Verlag. doi: 10.1007/978-1-4939-3506-2_20
Mischel, W. (2014). The marshmallow test: Mastering self-control. New York: Little, Brown and Co.
Porges, S. (2001). The polyvagal theory: Phylogenetic substrates of a social nervous system. International Journal of Psychophysiology, 42(2), 123–146. doi: 10.1016/S0167-8760(01)00162-3.
Portmann, A. (1961). Animals as social beings. New York, NY: Harper.
Schonert-Reichl, K. A. (2014). SEL implementation and sustainability: What does the research say? Prepared for The Hawn Foundation.
Schonert-Reichl, K. A., Oberle, E., Lawlor, M. S., Abbott, D., Thomson, K., Oberlander, T. F., Diamond, A. (2015). Enhancing cognitive and social-emotional development through a simple-to administer mindfulness-based school program for elementary school children: A randomized controlled trial. Developmental Psychology, 51, 52-66.
Shanker, S. (2012). Calm, alert and learning: Classroom strategies for self-regulation. Toronto: Pearson Canada.
Shanker, S. (2016). Self-Reg: How to help your child (and you) break the stress cycle and successfully engage with life. Toronto: Penguin Random House.
The Hawn Foundation. (2011a). The MindUP curriculum (Grades Pre-K–2): Brain-focused strategies for learning – and living. New York: Scholastic.
The Hawn Foundation. (2011b). The MindUP curriculum (Grades 3–5): Brain-focused strategies for learning – and Living. New York: Scholastic.
The Hawn Foundation. (2011c). The MindUP curriculum (Grades 6–8): Brain-focused trategies for learning – and living. New York: Scholastic.
Prizant, B. M., Wetherby, A. M., Rubin, E., Laurent, A, C., & Rydell, P. J. (2006). The SCERTS Model: Volume I assessment. Baltimore, MD: Brookes Publishing.
Prizant, B. M., Wetherby, A. M., Rubin, E., Laurent, A, C., & Rydell, P. J. (2006). The SCERTS Model: Volume II program planning and intervention. Baltimore, MD: Brookes Publishing.
Schore, A. Affect regulation and the origin of the self: The neurobiology of emotional development. London, England: Routledge.
Winner, M. G. (2000). Inside out: What makes a person with social cognitive deficits tick? San Jose, CA: Social Thinking Publishing.
Winner, M. G. (2005). Think social! San Jose, CA: Social Thinking Publishing.
Williams, M. S. & Shellenberger, S. (1996). “How does your engine run?” A leader’s guide to the alert program for self-regulation. Albuquerque, NM: TherapyWorks, Inc.
Examining the Measures:
Review of Self-Reg Components Captured in Current Measured Labelled “Self-Regulation”
Daisy Pyman HBA and Brenda Smith-Chant PhD
An examination of measures designed to assess self-regulation was undertaken to assess how well they are aligned with Shanker Self- Reg®. A literature search turned up 11 tools designed to provide measures of self-regulation. Analysis revealed that all tools primarily addressed prefrontal cortex functions and none adequately addressed all five domains of the Shanker Self-Reg® framework (biological, emotion, cognitive, social and prosocial). The cognitive domain was the focus of most of the tools reviewed, although the authors noted a historical trend towards conceptualizing self-regulation as a multi-domain concept. Full alignment with Self-Reg would require measures to address the dynamic nature of all five domains along with subcortical processes and the interplay between subcortical and neocortical systems and processes.
In Self-Reg, Shanker defines self-regulation as the ability to manage stressors across five domains: biological, emotion, cognitive, social, and prosocial, and then to subsequently recover (2016). This definition is based on the psychophysiological view of self-regulation developed by historic theorists and researchers through to current scientists (see Shanker, 2016 for a review). A stressor, by definition, is any stimulus that triggers a physiological response that serves to keep an internal homeostatic system (or systems) operating within its optimal functional range. The energy expended in such processes must be restored in order to avoid slipping into a state of allostatic overload, in which recovery is compromised and mood, behaviour, learning, and physical health are impaired.
One of the big challenges when doing research in this area is that there have been 447 different definitions of self-regulation (Burman, Green, and Shanker, 2015). These definitions belong to different disciplines (for example, education or mental health) that are focused on different contexts (for example, university learning or preschool behaviour). Many view self-regulation as a dispositional and domain-specific attribute (for example, a cognitive ability or set of cognitive abilities) that is relatively static (for example, emotional intelligence). Self-Reg, in contrast, sees the ability to manage stress as a fluid and dynamic process that is continually refined across the lifespan.
Measures developed to serve other disciplines often do not align with the psychophysiological view of self-regulation on which Self-Reg is based. The problem here is that those seeking to assess Self-Reg may gravitate towards published tools that are labelled as self-regulation, but are addressing something completely different. It is clear, then, that to advance the science of Self-Reg, we need to address the question: Are any of these tools, or a combination of these tools, or parts of these tools aligned with Shanker’s definition of self-regulation?
Measures of Self-Regulation and Self-Reg
To resolve this question, we undertook an analysis of the existing literature. The analysis involved an examination of measures that included the term self-regulation, self-regulating, self-regulatory, or self-regulated in the title. Using Google Scholar as our primary database, due to its comprehensiveness, we established inclusionary criteria for empirical research articles as follows: (a) the term self-regulation, or a variant (see above), must have been included in the title, (b) the paper had to have been published by more than one author or research group to eliminate any one-offs (tools developed for a single study but not used beyond), and (c) the measurement had to have been cited in an article within the last 20 years, to ensure that it was still in relatively current use. All articles were found using a search query for “self-regulation,” or a variant, in conjunction with one of the following terms: scale, inventory, assessment, or questionnaire. Measurements identified in this search were then submitted to an item-by-item analysis in order to identify which items, if any, tapped into biological, emotion, cognitive, social, and/or prosocial facets of Self-Reg. Additionally, the discipline or field for which the measure was primarily developed (for example, psycho-education, mental and physical health, development) was identified.
One limitation of this search was that biometric tools or assessments of stress and/or anxiety processes were not included in this analysis, although the theoretical underpinnings of Self-Reg lie in this area. For a review of biometric tools , including body- and brain-based measures, see “Measuring the Foundations of Self-Reg” (this volume). A review of tools that measure aspects of Self-Reg but do not use the term self-regulation is forthcoming.
Shanker (2016) identified five domains of stressors that require energy to be expended. We examined each of the measures to identify which domains of stressors were addressed by the assessment items. Items were identified as measuring biological stressors if they assessed either internal states (for example, quality of sleep, appetite and diet, feelings of illness/wellness, individual differences, disabilities) or potential sensory irritations (for example, eye fatigue, noise, visual screen-time, sensory processing issues). Items that assessed the experience of feeling or coping with emotion (for example, upset/unease, homesickness, test anxiety, excitement) were classified as tapping the emotion domain. By far the most often assessed was the cognitive domain. Items were identified as cognitive when they measured either cognitive processes (for example, attention, motivation, distractibility, metacognition) or thinking-based strategies or assessments (for example, self-efficacy, problem-solving, self-appraisals). The social domain included items that asked about the impact of interpersonal interactions on the individual (for example, personal and professional/educational relationships, social interactions with others, giving and receiving social cues). Finally, items were assessed as tapping the prosocial domain if they assessed the impact of empathy or social mores on the individual (for example, the impact of others’ stress or distress, feeling of societal expectations, cultural expectations). The items were classified according to domain by each author independently. Conflicts in the classification were resolved using consensus.
Measures were identified as “psycho-education” if they were designed to assess self-regulation skills in educational settings (for example, school, childcare, learning environments). Measures that were designed to measure self-regulation as a general life skill or for clinical populations (for example, those with addictions) were categorized as “mental health” tools. Finally, tools were categorized as Electrophysiology, T. F. o. t. E. S. o. C. t. N. A. S. o. P. (1996). Heart rate variability standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065. doi:10.1161/01.CIR.93.5.1043 “health” measures if they were designed for general medical use (for example, for those with illness or injury) or for use within fitness settings (for example, exercise, lifestyle). A summary of the domains of stressors assessed in each domain is outlined in Table 1.
Table 1: Representation of Shanker’s Five Domains of Self-Reg across Popular Self-Regulation Measures
Analysis of Measures
The analysis of the identified measures revealed a historical trend towards increasingly conceptualizing self-regulation as a multi-domain construct and considering the context of the behaviour within the tool. Measures developed in the 1980s focused predominantly on the metacognitive aspects of self-regulation alongside the application of cognitive strategies, both of which are cognitive domain factors. For example, in one of the first self-regulation measures developed by Zimmerman and colleague, the focus was solely on cognitive factors, such as self-control and self-monitoring abilities and beliefs, with little consideration of intra-personal factors impacting the ability to manage stressors and cope in a learning environment (Zimmerman & Martinez-Pons, 1988; the Self-Regulated Learning Interview Schedule).
As research on self-regulation advanced into the 1990s, the instruments that were developed started to hone in on measures of very specific cognitive sub-skills, as exemplified by the Self-Regulation Scale, which assesses attention-control (Diehl, Semegon, & Schwarzer, 2006), and the Self-Regulation Questionnaire (Brown et al., 1999), which assesses cognitive skills and abilities as a mental capacity across contexts. This focus on the cognitive domain is a fundamental feature underlying all the examined measures of self-regulation. One measure that does address the other domains is the Motivational Strategies for Learning Questionnaire (MSLQ), which measures cognitive factors in a learning context (for example, education), but considers factors impacting cognition across all five domains. For example, the confluence of the emotion and cognitive domains was tapped with Item Number 19 of the scale, which asked university students to respond on a likert scale whether they have, “an upset/uneasy feeling when [they] take an exam.” The impact of prosocial learning was assessed in the Item Number, “I think about how poorly I am doing compared to other students”. The limitation with the MSLQ is that the measure focuses predominantly on the cognitive impacts of these stressors, and pays little attention to the impacts of the other domains, where only one item of 81 itemed scale adequately assessed each of the remaining four domains. Moreover, the questionnaire is limited insofar as it conceptualizes cognition as an outcome and not a stressor in and of itself. Accordingly, while the measure considers multiple domains on the surface, it reflects a definition of self-regulation that is focused on managing or controlling stressors, rather than understanding the impact of stressors across domains or the impact of subcortical processes and conceptualizing “Self-Reg” through an understanding of the “triune brain.”
Many of the measures analyzed purport to measure biological/physiological, emotion, social, and prosocial factors (see, for example, the Self-Regulation Questionnaire-Prosocial). However, a review of these measures reflected only minimal or indirect assessments of these domains. For example, in the aforementioned Self-Regulation Questionnaire-Prosocial, stressors from the prosocial domain are only indirectly assessed by items asking about cognitive appraisals of this domain (for example, “Why do you keep a promise to a friend?”) with a choice selection provided for the rationale of the question. Here, the focus is on the thinking, or cognitive assessment, rather than the assessment of the prosocial factors that are impacting the individual. The minimal or indirect reflection of non-cognitive domains underplays not only the importance of considering stressors across domains, but also how these stressors impact coping and performance. Measures with this limitation are indicated in Table 1 using an asterisk.
One important observation that we made during our analysis is that these measures typically do not include items that look at self-regulation and the impact of stressors as a dynamic and evolving process. For example, few questionnaire items attempt to contextualize the impact of stressors across domains, such as indicating the impact of a biological stressor on problem-solving. One very rare exception from the Adolescent Self-Regulatory Inventory is “I have trouble getting excited about something that is really special when I am tired”, an item that looked at the interplay between emotion and biological factors.
Given that self-regulation in the reviewed measures refers to stress-management and is focused on cognitive performance as an outcome and not as a stressor as well, these tools do not reflect the Self-Reg model as conceptualized by Shanker (2016). A measure that is consistent with Self-Reg would require a five-domain model, not simply as some sort of additive measure, but rather to reflect how domains impinge on and amplify each other. For example, what might be an intolerable emotional or social stress when the individual is experiencing a period of low-energy (for example, fatigue) and high-tension (for example, high levels of stress overall) might be tolerable and even positively arousing when the individual has high levels of energy and low levels of tension. The context is an essential consideration and must be part of the assessment.
Many of the existing measures of self-regulation assess prefrontal cortical functions (cognitive processes such as mental strategies, planning, problem-solving, and self-monitoring). There are few measures that focus on subcortical systems (for example, stress responses). Self-Reg shows us that an adequate measurement of self-regulation must capture the interplay between neocortical and subcortical processes and the inter- (biological, emotion, and cognitive) and intra-processes (social and prosocial) impacting an individual.
Burman, J.T., Green, C.D., & Shanker, S. (2015). On the meanings of self-regulation: Digital humanities in service of conceptual clarity. Child Development, 86(5), 1507–1521. doi:10.1111/cdev.12395.
Brown, J.M., Miller, W.R., & Lawendowski, L.A. (1999). The Self-Regulation Questionnaire. in L. VandeCreek & T. L. Jackson (Eds.), Innovations in clinical practice: A source book (Vol. 17, pp. 281-289). Sarasota, FL: Professional Resource Press.
Carey, K.B., Neal, D.J., & Collins, S.E. (2004). A psychometric analysis of the self-regulation questionnaire. Addictive Behaviors, 29(2), 253–260. doi:10.1016/j.addbeh.2003.08.001
Cleary, T.J. (2006). The development and validation of the self-regulation strategy inventory—
Self-report. Journal of School Psychology, 44(4), 307–322. doi:10.1016/j.jsp.2006.05.002
Diehl, M., Semegon, A. B., & Schwarzer, R. (2006). Assessing attention control in goal pursuit: A component of dispositional self-regulation. Journal of Personality Assessment, 86(3), 306-317. doi: 10.1207/s15327752jpa8603_06
Hong, E., & O’Neil Jr., H.F. (2001). Construct validation of a trait self-regulation model. International Journal of Psychology, 36(3), 186–194. doi: 10.1080/00207590042000146
Moilanen, K.L. (2007). The adolescent self-regulatory inventory: The development and validation of a questionnaire of short-term and long-term self-regulation. Journal of Youth and Adolescence, 36(6), 835–848. doi:10.1007/s10964-006-9107-9
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. doi: 10.1037/0022-0618.104.22.168
Ryan, R.M., & Connell, J.P. (1989). Perceived locus of causality and internalization: Examining reasons for acting in two domains. Journal of Personality and Social Psychology, 57(5), 749. doi:10.1037/0022-
Schwarzer, R., Diehl, M., & Schmitz, G.S. (1999). Self-regulation scale. Retrieved from http://www.fu-berlin.de/gesund/skalen
Shanker, S. (2016). Self-Reg: How to help your child (and you) break the stress cycle and successfully engage with life. Toronto: Penguin Random House Canada.
Smith-Donald, R., Raver, C.C., Hayes, T., & Richardson, B. (2007). Preliminary construct and concurrent validity of the Preschool Self-Regulation Assessment (PSRA) for field-based research. Early Childhood Research Quarterly, 22(2), 173–187. doi:10.1016/j.ecresq.2007.01.002
Toering, T., Elferink-Gemser, M.T., Jonker, L., van Heuvelen, M.J., & Visscher, C. (2012). Measuring self-regulation in a learning context: Reliability and validity of the Self-Regulation of Learning Self-Report Scale (SRL-SRS). International Journal of Sport and Exercise Psychology, 10(1), 24–38. doi:10.1080/1612197X.2012.645132
Zimmerman, B. J., & Martinez-Pons, M. (1988). Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology, 80(3), 284-290. doi: 10.1037/0022-0622.214.171.1244
Measuring the Foundations of Self-Reg: Bio-physiological Assessments of the Stress Response
Casey Burgess MA and Brenda Smith-Chant PhD
The Self-Reg Framework (Shanker, 2016) is a comprehensive model of self-regulation grounded in psychophysiology. This paper presents a literature review of existing physiological measures of self-regulation and discusses their potential for research relevant to the Self-Reg framework. Although many of these brain- and heart-based measures provide indicators of a stress response, few are able to provide information about the causes of the stress response and the ability of the individual to respond and recover. We also note challenges inherent in physiological measurements of self-regulation including the invasiveness and complexity in collecting physiological data and their limitations in assessing self-regulation as a process. The tools reviewed have potential in measuring some of the key aspects of the stress response and provide opportunities for future applications of use of physiological measurement.
The Self-Reg framework represents a comprehensive model of self-regulation that is grounded in the psychophysiological tradition initiated by Claude Bernard (1865), Walter Cannon (1939), and Hans Selye (1946), and culminating in the work of Steven Porges (2011). Assessments of the stress response based in this framework encompass all five Self-Reg domains – biological, emotion, cognitive, social, and prosocial. This paper focuses on measures in the biological domain.
Our bodies provide evidence of the stress response. For example, during a sympathetic autonomic response to stress (often termed the fight-or-flight or freeze response), the body prepares for threat with physiological changes that include heart rate changes, pupil dilation/visual perceptual responses, and neurological processing shifts. With modern technology, these physiological indicators of a stress response can be measured. In this paper, we review three major categories of physiological measures that indicate a stress response – eye-related, brain-related, and heart-related (Mandrick, Peysakhovich, Rémy, Lepron, & Causse, 2016), as well as a few additional measures beyond these categories,and critique their usefulness as indicators of a stress response for Self-Reg research.
The responsivity of the eye to autonomic arousal (pupil dilation, gaze, attentional capture) has long been established. Stress-related eye-based measurement includes using technology in the following areas: (a) tracking specific eye movements (focus of visual attention); (b) gauging eye blink response to capture an individual’s level of physiological arousal based on the timing and magnitude of the startle response; and (c) using pupillometry, which involves measuring the involuntary diameter/dilation of the pupil when an individual is exposed to specific stimuli (Beatty & Kahneman, 1966). Each of these measures can be used to gauge a stress response, but some measures, such as eye gaze, can also provide information about the visual and social stressors impacting an individual.
Eye gaze is an example of an eye-based measure that provides information about stressors. Eye tracking software can be used to track eye gaze to determine an individual’s focus of attention on specific stimuli, such as specific parts of another person’s face (Bal et al., 2010). For example, eye gaze between mother and child dyads can be tracked as a behaviour related to mental health and co-regulation (Warnock, Craig, Bakeman, Castral, & Mirlashari, 2016). Eye tracking can provide clues about what an individual may be looking at, but it provides limited information about the impact of that visual information on the stress response.
Eye blink has been related to stress and has been better established as an indicator of an autonomic response. White and her colleagues (2014) studied eye blink responses elicited by startle and suggested a dimensional model with neurophysiological foundations. They found that the rate of blinking increased when stress increased. Eye blink can be measured concurrently with eye gaze with the addition of a camera that captures images of the blink during the tracking process.
Similarly, pupillometry, has also been validated as an indicator of affective processing (Mandrick et al., 2016). The measurement of a pupil’s diameter can also be captured with the recording of the individual’s eye. Pupillometry can indicate the presence of high mental effort and threat (that is, stressful sound) with an increase in tonic pupil diameter and decrease in phasic pupil response (Mandrick et al., 2016).
Eye-based measures, as outlined above, can provide limited but important information about the interplay of stressors and their physiological impact, a key principle underlying Self-Reg. Eye-tracking may only provide data on where an individual is looking, but, with the addition of information from observing eye blink and pupillometry, measurement of eye responses can provide information about an autonomic stress response. However, currently these measures cannot be taken naturalistically. The equipment and techniques they require are currently lab-based and individuals may demonstrate increased responses simply as a result of the unfamiliar lab experience itself.
The issue of responsivity to the assessment location and/or to the equipment is also a consideration for brain-based measurements. Empirical literature on brain-based measurement (neuroimaging) and how it relates to stress is much more robust than that on eye-based physiological measurement. Neuroimaging includes magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). All of these systems can be used to infer activity in the brain that reflects psychophysiological processes.
MRI allows researchers to examine static and moving images demonstrating neural activation when a subject is presented with specific stimuli. It can be used to examine prefrontal activation associated with the body’s stress system. For example, the presence of a close social companion reduces neural activity in regions associated with negative affect, threat, fear, or pain, such as the right anterior insula and superior frontal gyrus, and activates areas of the prefrontal cortex that help an individual down-regulate and reduce a fight-or-flight response, such as the ventromedial (Hostinar & Gunnar, 2015). MRI can be used to measure the autonomic limbic and prefrontal neural responses to meditation and mindfulness (Annells, Kho, & Bridge, 2016; Boccia, Piccardi, & Guariglia, 2015). It can also be used to measure temporal (in-time) responses to imagined social situations (Zahn et al., 2009), as well as brain activity during socio-moral tasks requiring interpretive judgement across ages (Barrasso-Catanzaro & Eslinger, 2016; Weiskopf et al., 2004; Weiskopf et al., 2003).
fMRI also enables researchers to study images of neural activation in response to specific stimuli. Real-time fMRI feedback has been used to allow people to observe and control changes to their neural responses (Weiskopf et al., 2004; Weiskopf et al., 2003). It shows promise for enabling the study of the potential ability to connect behavioural and cognitive responses with physiological brain activation, particularly for anterior cingulate cortex (ACC) activation and its role in self-regulation.
The newest means of neural measurement is diffusion tensor imaging, which shows the interconnectivity between brain areas (Annells et al., 2016; Tang, Holzel, & Posner, 2015). Although quite new, DTI shows potential in regard to self-regulation’s interconnected nature and, in the future, may provide clues about how the domains might be neurologically connected within the different areas of the brain (that is, how the limbic system may be connected to the social, emotional, or executive functioning/cognitive centres of the brain).
EEG measures electrical activity of the brain by recording brain waves via a net-like cap embedded with sensors. Where fMRI data is based on blood flow within the preceding four to six seconds, the event-related potential (ERP) measured by EEG is a more direct measure of neural activity connected to real-time stimuli (Amodio, Bartholow, & Ito, 2014). Also, because it is quite tolerant of movement, more realistic stimuli can be used, including real people and videos (Van Hecke et al., 2009), which is more appropriate to capturing the process of individual self-regulation in response to a variety of naturalistic stressors across domains. Moreover, EEG is silent, unlike MRI and fMRI, and much more portable than many other techniques, making it effective for the measurement of self-regulation within familiar daily environments.
In relation to aspects of self-regulation, including individual response to stimuli based on an individual’s current state, as well as the resultant effortful control, EEG has been used to measure brain indications of inhibitory control and cortical activation across ages (Lamm & Lewis, 2010), neural response to the faces of others (Van Hecke et al., 2009), neural response to risky performance (Segalowitz et al., 2012), effortful control in terms of the prefrontal cortex’s connection to voluntary control (Smith, Diaz, Day, & Bell, 2016), structural brain maturation of impulse control, attention, executive function (Fjell et al., 2012), and amygdala activity involved in self-regulation via neurofeedback (Meir-Hasson et al., 2016).
Unfortunately, MRI and fMRI studies are limited as measures of Self-Reg because of their lab requirements, making them unusable in naturalistic settings where self-regulation can be measured with high validity, and also because the known noise and confines of the scanners can affect an individual’s physiological arousal levels (Annells et al., 2016). The frightening and unfamiliar experience of being assessed with MRI and fMRI can confound the data and undermine the ability to form conclusions about the process of self-regulation.
EEG is much more mobile and flexible than MRI or fMRI. Portable systems are available that can be used outside the lab, although the placement of the cap and the recording devices for the sensors does limit the use of EEG. As well, a long-standing limitation of EEG is that the data can only indicate that the brain has been activated in a relatively general location, not the nature of why that response occurred (Amodio et al., 2014). Not only that, but also a skilled technician is needed to interpret the data generated by an EEG. Notwithstanding these limitations, EEG is particularly promising as a method that is readily combined with other measures of physiological responses.
Contemporary research in self-regulation is focused on the brain–body connection (Bal et al., 2010; Geisler & Kubiak, 2009; Patriquin, Lorenzi, & Scarpa, 2013; Porges et al., 2013; Porges & Furman, 2011). In other words, our nervous system connects our heads, where we read our environment for safety cues, and our hearts, where we experience physiological responses. Each helps the other in perceiving and responding to incoming stressors. The research uses measures of vagal tone, including Heart Rate Variability (HRV) to understand the psychophysiological foundation of self-regulation, as described below.
Polyvagal Theory describes how risk and safety cues that are continually monitored by our nervous systems influence our physiology and psychology (Porges, 2011). This refers to our ability to remain in a calm, alert, self-regulated autonomic state (Porges, 2007). Porges’ work (2007, 2009, 2011, 2015) requires much technological and statistical precision in using electrocardiogram (ECG or EKG) data to calculate a measure called vagal tone, which is thought to provide an important marker of human self-regulatory ability and adaptation to environmental challenges.
Heart Rate Variability
Researchers have used HRV measures to examine many different aspects of self-regulation, including the following:
- physical health (Kim et al., 2015; Huikuri et al., 1999; Grieco, Colberg, Somma, Thompson, & Vinik, 2014);
- mental health and internalizing disorders (Cicchetti et al., 2014; Bradley et al., 2010; Scott & Weems, 2014; Bosch, Riese, Ormel, Verhulst, & Oldehinkel, 2009),
- self-control (Geisler & Kubiak, 2009);
- aggression (Gower & Crick, 2011);
- addictions and risky behaviour (Quintana, Guastella, McGregor, Hickie, & Kemp, 2013; Kniffin et al., 2014; Buckman, White, & Bates, 2010);
- social interaction (Shahrestani, Stewart, Quintana, Hickie, & Guastella, 2014; Movius & Allen, 2005);
- emotion regulation/control (Hastings et al., 2008; Pu, Schmeichel, & Demaree, 2010; Guy, Souders, Bradstreet, DeLussey, & Herrington, 2014; Davis, Quiñones-Camacho, & Buss, 2016);
- mindfulness, yoga, or other body–mind interventions, including breathing (Tang et al., 2009; Peng et al., 2004; Delgado-Pastor, Perakakis, Subramanya, Telles, & Vila, 2013; Courtney, Cohen, & van Dixhoorn, 2011);
- executive function and/or cognitive function (Thayer, Hansen, Saus-Rose, & Johnsen, 2009; Marcovitch et al., 2010);
- cognitive appraisal effects on the body (Luecken, Appelhans, Kraft, & Brown, 2006 ; Denson, Grisham, & Moulds, 2011).
A promising area of vagal tone research is in the ability to observe the dynamic interaction of physiological response in social interactions, particularly between a parent and child. Research looking at vagal tone of both parent and child engaged in interactions is robust (Williams & Woodruff-Borden, 2015; Suveg, Shaffer, & Davis, 2016; Smith, Woodhouse, Clark, & Skowron, 2016; Moore, 2009; Lunkenheimer et al., 2015; Gunning, Halligan, & Murray, 2013; Feldman, Weller, Sirota, & Eidelman, 2002; Ferrer & Helm, 2013; Feldman, 2007a; Feldman, 2007b; Diamond, Fagundes, & Butterworth, 2012; Calkins, Smith, Gill, & Johnson, 1998). The volume of work in this area suggests a strong affiliation with the interbrain connection between parent and child, and illustrates the parent’s role in co-regulating a child right from birth, or even prenatally. Research in this area is critical to self-regulation, demonstrating the initial foundations of the development of self-regulation from an evidence-based physiological measure.
Because of the statistical complexity of interpreting vagal tone data, many published reports require critical analysis to draw consistent conclusions, as different analysis methods can result in different interpretations of the same data (Lewis, Furman, McCool, & Porges, 2012). Publications often lack precision and accurate editing of artifact (electrical activity coming from places other than the brain, such as jaw clenching), and measures of vagal tone are often misinterpreted (Porges, 2007). While the need for such precision renders this measure inaccessible to many researchers, there are international guidelines available for the quantification and accurate interpretation of vagal tone (Electrophysiology, 1996), as well as a very recent article that provides details on the practical aspects of using vagal tone in a clinical lab setting (Laborde, Mosley, & Thayer, 2017). Both of these are practical resources for advancing the quality and volume of upcoming research in this area.
The greater issue is that measures of vagal tone including HRV require relatively expensive equipment and considerable technical skill to implement. They are unwieldly or impossible to use in natural settings. Participants typically find the assessment process stressful. This impacts their state of psychophysiological arousal and can confound the measurement of the stress response. This is particularly true of children and vulnerable adults. As such, these measures are often too expensive, invasive, and technologically complex to use outside of research laboratories.
Other Physiological Measures
While the above categories of psychophysiological measurement emerged from the review of the literature, there are other measures relevant to self-regulation and/or the stress response as described in the following section.
Measuring cortisol levels can provide researchers with information about the stress system in mammals. The Hypothalamic–Pituitary–Adrenal (HPA) axis, part of the mammalian stress system connecting the limbic and cortical systems, releases hormones stimulating the production of cortisol, which enters all the cells of the body and brain. Its receptors can mobilize energy for action and create memory for threats, but this wears on the immune system (Hostinar & Gunnar, 2015). Cortisol levels follow a diurnal rhythm, but dysregulation of this pattern can cause internalizing and externalizing problems (Ursache, Noble, & Blair, 2015). The cortisol stress response is adaptable in the short term, but dysregulation occurs when the HPA axis is over-activated, resulting in allostatic overload shown by altered diurnal patterns of cortisol (Dich, Doan, & Evans, 2015). Cortisol can be measured via collection of overnight urine (Dich et al., 2015), hydrocellulose sponges (Ursache et al., 2015), or more often an oral saliva swab (Borelli, West, Weekes, & Crowley, 2014; Schonert-Reichl & Lawlor, 2010; Verner et al., 2010), making it accessible within a variety of familiar environments.
Electrodermal activity (EDA) is a means of measuring the electrical conductivity evident on the skin’s surface, including fingertip sensors (Wilson, Lengua, Tininenko, Taylor, & Trancik, 2009), or facial skin temperature changes (Eum, Eom, Park, Cheong, & Sohn, 2014), where increased sweat gland hydration (fingertips) and blood flow (facial) are known to correlate with autonomic (sympathetic) nervous system activity.
Some areas of research use an analogy comparing self-regulation in mammals to a muscle, where fatigue occurs with use, requiring recovery (Muraven & Baumeister, 2000). Depletion of glucose levels can represent self-regulatory fatigue because stressors cause the body to expend glucose in the form of energy (Evans, Boggero, & Segerstrom, 2016). If self-regulation depends on depletable energy, some of this must be derived from glucose and there is much evidence that exercising self-control – which remains a separate and secondary construct to self-regulation – reduces glucose in the bloodstream and impairs later regulatory ability (Gailliot, 2015).
Cortisol measurement has been used in research to look at moderating conditions of stress, such as socioeconomic status (Ursache et al., 2015), mindfulness (Schonert-Reichl & Lawlor, 2010), stress vulnerability (Jirikowic, Chen, Nash, Gendler, & Carmichael Olson, 2016), teacher burnout (Oberle & Schonert-Reichl, 2016), temperament and personality (Blair, Peters, & Granger, 2004), competitive pressure (Verner et al., 2010), and dyadic relationships like those between a parent and child (Borelli et al., 2014; Hatfield & Williford, 2016). As such, cortisol measurement is a well-validated indicator of a stress response. With self-regulation being couched in our fluctuating autonomic responses to threats or stressors in our environments (and their contribution to potential allostatic overload), cortisol may be a good measure contributing to our understanding of the physiological aspects of self-regulation.
While cortisol can be a primary physiological mediator of stress, however, there is no gold standard methodology of measuring allostatic overload, and in terms of self-regulation, this process may be more complex than stress itself – self-regulation may be a potential moderator (Dich et al., 2015). Cortisol measures can be either very invasive (if using blood) or moderately invasive (if using urine or a swab/sponge), but cortisol levels must be contextualized by both time of day (due to natural daily fluctuations) and baseline rates that vary from individual to individual. Analysis of cortisol is undertaken in a lab, which requires resources. Cortisol, however, can be collected in relatively natural settings rather than the lab only.
EDA measures can look at general sympathetic arousal or skin response connected to specific stimuli, and has been shown to correlate positively with high arousal and threat-based distress, and negatively with externalizing problems and delay of gratification (Wilson et al., 2009). This method of data collection as it relates expressly to self-regulation, however, is sparse and lacking conclusive evidence.
There is some promise in self-regulation being associated with physiological changes in parasympathetic nervous system activity connected to the conservation of resources and to the visceral organs of the body, but there are limitations to using them as measures of the self-regulation framework, including: the reliance of these changes on specific self-control tasks as opposed to self-regulation; normative fluctuations in blood glucose; inconsistent rates of glucose absorption; inconsistent performance on complex mental tasks that may improve following exercise (that is, decreased blood glucose); and the fact that comparative research in this area may not generalize to humans. Once again, the measurement of glucose is moderately invasive, but requires baseline levels, context of time of day, and lab-based analysis to create meaningful data, just as with cortisol measurement. This restricts the use of these measures to those with the technical resources to do such analyses.
Bio-physiological measurement of the stress response has been used and validated in clinical and laboratory research. There are three main challenges of using this form of assessment in Self-Reg research. First, many of the direct measures of the stress response require specialized equipment (for example, heart- or eye-tracking monitors) or interpretive processes (for example, lab analysis or contrasts to baseline states). There are promising developments towards accessible and affordable biometric devices, yet it remains unclear whether these devices are either sufficiently accurate or informative enough to help assess stress responses as part of a self-regulatory process. A second issue is that many of these measures are invasive and can be intimidating for people. As such, the assessment may become a stressor in and of itself and confound the information about stressors of interest. The third issue is the most challenging. Self-Reg is a dynamic process that is reflective and responsive to context.
Static measures of most biometric responses present only a snapshot of a response in time. Some measures can track psychophysiological processes over time (for example, fMRI, MRI, eye tracking, heart rate), but only heart rate measures can be used outside of the lab or for extensive periods of time. These measures are only of physiological response. Assessments of Self-Reg require careful analysis beyond the physiological to understand all five domains of the framework as a dynamic system: biological, emotion, cognitive, social, and prosocial. Bio-physiological measures offer Self-Reg researchers what may be a piece of that Self-Reg assessment puzzle.
Amodio, D.M., Bartholow, B.D., & Ito, T.A. (2014). Tracking the dynamics of the social brain: ERP approaches for social cognitive and affective neuroscience. Social Cognitive & Affective Neuroscience, 9(3), 385–393.
Annells, S., Kho, K., & Bridge, P. (2016). Meditate don’t medicate: How medical imaging evidence supports the role of meditation in the treatment of depression. Radiography, 22(1), e54.
Bal, E., Harden, E., Lamb, D., Van Hecke, A.V., Denver, J.W., & Porges, S.W. (2010). Emotion recognition in children with autism spectrum disorders: Relations to eye gaze and autonomic state. Journal of Autism and Developmental Disorders, 40(3), 358–370. doi:10.1007/s10803-009-0884-3
Barrasso-Catanzaro, C., & Eslinger, P.J. (2016). Neurobiological bases of executive function and social-emotional development: Typical and atypical brain changes. Family Relations, 65(1), 108.
Beatty, J., & Kahneman, D. (1966). Pupillary changes in two memory tasks. Psychonomic Science, 5(10), 371–372. doi:10.3758/BF03328444
Blair, C., Peters, R., & Granger, D. (2004). Physiological and neuropsychological correlates of approach/withdrawal tendencies in preschool: Further examination of the behavioral inhibition system/behavioral activation system scales for young children. Developmental Psychobiology, 45(3), 113–124.
Boccia, M., Piccardi, L., & Guariglia, P. (2015). The meditative mind: A comprehensive meta-analysis of MRI studies. BioMed Research International, 2015, 1–11. doi:10.1155/2015/419808
Borelli, J.L., West, J.L., Weekes, N.Y., & Crowley, M.J. (2014). Dismissing child attachment and discordance for subjective and neuroendocrine responses to vulnerability. Developmental Psychobiology, 56(3), 584.
Bosch, N.M., Riese, H., Ormel, J., Verhulst, F., & Oldehinkel, A.J. (2009). Stressful life events and depressive symptoms in young adolescents: Modulation by respiratory sinus arrhythmia? The TRAILS study. Biological Psychology, 81(1), 40–47. doi:10.1016/j.biopsycho.2009.01.005
Bradley, R.T., McCraty, R., Atkinson, M., Tomasino, D., Daugherty, A., & Arguelles, L. (2010). Emotion self-regulation, psychophysiological coherence, and test anxiety: Results from an experiment using electrophysiological measures. Applied Psychophysiology and Biofeedback, 35(4), 261–283. doi:10.1007/s10484-010-9134-x
Brown, J.M., Miller, W.R., & Lawendowski, L.A.. (1999). The self-regulation questionnaire. In J. B. Allen, E. M. Wolf, & L. VandeCreek (Eds.), Innovations in clinical practice: A source book (pp. 281–293). Sarasota, FL: Professional Resources Press.
Buckman, J.F., White, H.R., & Bates, M.E. (2010). Psychophysiological reactivity to emotional picture cues two years after college students were mandated for alcohol interventions. Addictive Behaviors, 35(8), 786–790. doi:10.1016/j.addbeh.2010.03.017
Cannon, W. B. (1932). The wisdom of the body. New York: W.W. Norton & Company, Inc.
Cicchetti, D., Natsuaki, M.N., Shanahan, L., Calkins, S.D., Keane, S.P., Kelleher, R., & Suffness, R. (2014). Trajectories of internalizing symptoms across childhood: The roles of biological self-regulation and maternal psychopathology. Development & Psychopathology, 26(4pt2), 1353–1368. doi:10.1017/S0954579414001072
Courtney, R., Cohen, M., & van Dixhoorn, J. (2011). Relationship between dysfunctional breathing patterns and ability to achieve target heart rate variability with features of “coherence” during biofeedback. Alternative Therapies in Health and Medicine, 17(3), 38–44.
Davis, E.L., Quiñones-Camacho, L.E., & Buss, K.A. (2016). The effects of distraction and reappraisal on children’s parasympathetic regulation of sadness and fear. Journal of Experimental Child Psychology, 142, 344–358. doi:10.1016/j.jecp.2015.09.020
Delgado-Pastor, L.C., Perakakis, P., Subramanya, P., Telles, S., & Vila, J. (2013). Mindfulness (Vipassana) meditation: Effects on P3b event-related potential and heart rate variability. International Journal of Psychophysiology, 90(2), 207–214. doi:10.1016/j.ijpsycho.2013.07.006
Denson, T., Grisham, J., & Moulds, M. (2011). Cognitive reappraisal increases heart rate variability in response to an anger provocation. Motivation & Emotion, 35(1), 14–22. doi:10.1007/s11031-011-9201-5
Diamond, L.M., Fagundes, C.P., & Butterworth, M.R. (2012). Attachment style, vagal tone, and empathy during mother–adolescent interactions. Journal of Research on Adolescence, 22(1), 165–184. doi:10.1111/j.1532-7795.2011.00762.x
Dich, N., Doan, S., & Evans, G. (2015). Children’s negative emotionality combined with poor self-regulation affects allostatic load in adolescence. International Journal of Behavioral Development, 39(4), 368–375.
Eum, Y.-J., Eom, J.-S., Park, M.-S., Cheong, E.N., & Sohn, J.-H. (2014). Changes in facial skin temperature induced by joy or fear stimuli. International Journal of Psychophysiology, 94(2), 243–243. doi:10.1016/j.ijpsycho.2014.08.934
Evans, D.R., Boggero, I.A., & Segerstrom, S.C. (2016). The nature of self-regulatory fatigue and “ego depletion.” Personality & Social Psychology Review, 20(4), 291–310. doi:10.1177/1088868315597841
Feldman, R. (2007a). Parent–infant synchrony and the construction of shared timing; Physiological precursors, developmental outcomes, and risk conditions. Journal of Child Psychology & Psychiatry, 48(3/4), 329–354. doi:10.1111/j.1469-7610.2006.01701.x
Feldman, R. (2007b). Parent–infant synchrony: Biological foundations and developmental outcomes. Current Directions in Psychological Science, 16(6), 340–345. doi:10.1111/j.1467-8721.2007.00532.x
Feldman, R., Weller, A., Sirota, L., & Eidelman, A.I. (2002). Skin-to-skin contact (kangaroo care) promotes self-regulation in premature infants: Sleep-wake cyclicity, arousal modulation, and sustained exploration. Developmental Psychology, 38(2), 194.
Ferrer, E., & Helm, J.L. (2013). Dynamical systems modeling of physiological coregulation in dyadic interactions. International Journal of Psychophysiology, 88(3), 296–308. doi:10.1016/j.ijpsycho.2012.10.013
Fjell, A.M., Walhovd, K.B., Brown, T.T., Kuperman, J.M., Chung, Y., Hagler, D.J. Jr, Venkatraman, V., Roddey, J.C., Erhart, M., McCabe, C., Akshoomoff, N., Amaral, D.G., Bloss, C.S., Libiger, O., Darst, B.F., Schork, N.J., Casey, B.J., Chang, L., Ernst, T.M., Gruen, J.R., Kaufmann, W.E., Kenet, T., Frazier, J., Murray, S.S., Sowell, E.R., van Zijl, P., Mostofsky, S., Jernigan, T.L., Dale, A.M.; (2012). Multimodal imaging of the self-regulating developing brain. Proceedings of the National Academy of Sciences of the United States, 48, 19620.
Gailliot, M.T. (2015). Energy in the air and psychological work: Examining the relationship between oxygen and self-regulation. Journal of Articles in Support of the Null Hypothesis, 11(2), 37–46.
Geisler, F.C.M., & Kubiak, T. (2009). Heart rate variability predicts self-control in goal pursuit. European Journal of Personality, 23(8), 623–633. doi:10.1002/per.727
Gower, A.L., & Crick, N.R. (2011). Baseline autonomic nervous system arousal and physical and relational aggression in preschool: The moderating role of effortful control. International Journal of Psychophysiology, 81(3), 142–151. doi:10.1016/j.ijpsycho.2011.06.001
Grieco, C.R., Colberg, S.R., Somma, C.T., Thompson, A.G., & Vinik, A.I. (2014). Acute effect of breathing exercises on heart rate variability in Type 2 diabetes: A pilot study. Journal of Alternative & Complementary Medicine, 20(8), 642–648. doi:10.1089/acm.2013.0280
Gunning, M., Halligan, S.L., & Murray, L. (2013). Contributions of maternal and infant factors to infant responding to the Still Face paradigm: A longitudinal study. Infant Behavior & Development, 36(3), 319–328. doi:10.1016/j.infbeh.2013.02.003
Guy, L., Souders, M., Bradstreet, L., DeLussey, C., & Herrington, J.D. (2014). Emotion regulation and respiratory sinus arrhythmia in autism spectrum disorder. Journal of Autism and Developmental Disorders, 10, 2614. doi:10.1007/s10803-014-2124-8
Hastings, P.D., Nuselovici, J.N., Utendale, W.T., Coutya, J., McShane, K.E., & Sullivan, C. (2008). Applying the polyvagal theory to children’s emotion regulation: Social context, socialization, and adjustment. Biological Psychology, 79(3), 299–306. doi:10.1016/j.biopsycho.2008.07.005
Hatfield, B.E., & Williford, A.P. (2016). Cortisol patterns for young children displaying disruptive behavior: Links to a teacher-child, relationship-focused intervention. Prevention Science: The Official Journal of the Society for Prevention Research. doi:10.1007/s11121-016-0693-9
Hostinar, C.E., & Gunnar, M.R. (2015). Social support can buffer against stress and shape brain activity. AJOB Neuroscience, 6(3), 34–42. doi:10.1080/21507740.2015.1047054
Huikuri, H.V., Mäkikallio, T., Airaksinen, K.E.J., Mitrani, R., Castellanos, A., & Myerburg, R. J. (1999). Measurement of heart rate variability: A clinical tool or a research toy? Journal of the American College of Cardiology, 34(7), 1878–1883. doi:10.1016/S0735-1097(99)00468-4
Jirikowic, T., Chen, M., Nash, J., Gendler, B., & Carmichael Olson, H. (2016). Regulatory behaviors and stress reactivity among infants at high risk for fetal alcohol spectrum disorders: An exploratory study. Journal of Mental Health Research in Intellectual Disabilities, 9(3), 171.
Kim, S., Rath, J.F., McCraty, R., Zemon, V., Cavallo, M.M., & Foley, F.W. (2015). Heart rate variability biofeedback, self-regulation, and severe brain injury. Biofeedback, 43(1), 6–14. doi:10.5298/1081-5937-43.1.10
Laborde, S., Mosley, E., & Thayer, J.F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research – Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology. Feb 20. doi:10.3389/fpsyg.2017.00213
Lamm, C., & Lewis, M.D. (2010). Developmental change in the neurophysiological correlates of self-regulation in high- and low-emotion conditions. Developmental Neuropsychology, 35(2), 156–176. doi:10.1080/87565640903526512
Lewis, G.F., Furman, S.A., McCool, M.F., & Porges, S.W. (2012). Statistical strategies to quantify respiratory sinus arrhythmia: Are commonly used metrics equivalent? Biological Psychology, 89(2), 349–364. doi:10.1016/j.biopsycho.2011.11.009
Luecken, L. J., Appelhans, B. M., Kraft, A., & Brown, A. (2006). Never far from home: A cognitive-affective model of the impact of early-life family relationships on physiological stress responses in adulthood. Journal Of Social & Personal Relationships, 23(2), 189. doi:10.1177/0265407506062466
Lunkenheimer, E., Tiberio, S.S., Buss, K.A., Lucas-Thompson, R.G., Boker, S.M., & Timpe, Z.C. (2015). Coregulation of respiratory sinus arrhythmia between parents and preschoolers: Differences by children’s externalizing problems. Developmental Psychobiology, 57(8), 994–1003. doi:10.1002/dev.21323
Mandrick, K., Peysakhovich, V., Rémy, F., Lepron, E., & Causse, M. (2016). Neural and psychophysiological correlates of human performance under stress and high mental workload. Biological Psychology. 121( Part A), 62–-67. doi:10.1016/j.biopsycho.2016.10.002
Marcovitch, S., Leigh, J., Calkins, S.D., Leerks, E.M., O’Brien, M., & Blankson, A.N. (2010). Moderate vagal withdrawal in 3.5-year-old children is associated with optimal performance on executive function tasks. Developmental Psychobiology, 52(6), 603.
Meir-Hasson, Y., Keynan, J.N., Kinreich, S., Jackont, G., Cohen, A., Podlipsky-Klovatch, I., Hendler, T. & Intrator, N. (2016). One-class FMRI-inspired EEG model for self-regulation training. PLoS ONE. 11(5), doi:10.1371/journal.pone.0154968
Moore, G.A. (2009). Infants’ and mothers’ vagal reactivity in response to anger. Journal of Child Psychology & Psychiatry, 50(11), 1392–1400. doi:10.1111/j.1469-7610.2009.02171.x
Movius, H.L., & Allen, J.J.B. (2005). Cardiac vagal tone, defensiveness, and motivational style. Biological Psychology, 68(2), 147–162. doi:10.1016/j.biopsycho.2004.03.019
Muraven, M., & Baumeister, R.F. (2000). Self-regulation and depletion of limited resources: Does self-control resemble a muscle? Psychological Bulletin, 126(2), 247–259.
Oberle, E., & Schonert-Reichl, K.A. (2016). Stress contagion in the classroom? The link between classroom teacher burnout and morning cortisol in elementary school students. Social Science & Medicine, 159, 30–37. doi:10.1016/j.socscimed.2016.04.031
Patriquin, M.A., Lorenzi, J., Scarpa, A. & Bell, M.A. (2014) Developmental trajectories of respiratory sinus arrhythmia: Associations with social responsiveness. Developmental psychobiology, 56(3), 317-326.
Peng, C. K., Henry, I.C., Mietus, J.E., Hausdorff, J.M., Khalsa, G., Benson, H., & Goldberger, A.L. (2004). Heart rate dynamics during three forms of meditation. International Journal of Cardiology, 95(1), 19–27. doi:10.1016/j.ijcard.2003.02.006
Porges, S.W. (2007). The polyvagal perspective. Biological Psychology, 74(2), 116–143. doi:10.1016/j.biopsycho.2006.06.009
Porges SW. (2009). The polyvagal theory: New insights into adaptive reactions of the autonomic nervous system. Cleveland Clinic Journal of Medicine, 76, 86-90.
Porges SW (2011). The Polyvagal Theory: Neurophysiological Foundations of Emotions, Attachment, Communication, and Self-regulation. New York: WW Norton.
Porges S.W. & Furman S.A. (2011). The early development of the autonomic nervous system provides a neural platform for social behavior: A polyvagal perspective. Infant and Child Development, 20,106-118.
Pu, J., Schmeichel, B.J., & Demaree, H.A. (2010). Cardiac vagal control predicts spontaneous regulation of negative emotional expression and subsequent cognitive performance. Biological Psychology, 84(3), 531–540. doi:10.1016/j.biopsycho.2009.07.006
Quintana, D.S., Guastella, A.J., McGregor, I.S., Hickie, I.B., & Kemp, A.H. (2013). Heart rate variability predicts alcohol craving in alcohol dependent outpatients: Further evidence for HRV as a psychophysiological marker of self-regulation. Drug & Alcohol Dependence, 132(1/2), 395–398. doi:10.1016/j.drugalcdep.2013.02.025
Schonert-Reichl, K.A., & Lawlor, M.S. (2010). The effects of a mindfulness-based education program on pre- and early adolescents’ well-being and social and emotional competence. Mindfulness, 1(3), 137–151. doi:10.1007/s12671-010-0011-8
Scott, B.G., & Weems, C.F. (2014). Resting vagal tone and vagal response to stress: Associations with anxiety, aggression, and perceived anxiety control among youths. Psychophysiology, 51(8), 718–727. doi:10.1111/psyp.12218
Segalowitz, S.J., Santesso, D.L., Willoughby, T., Reker, D.L., Campbell, K., Chalmers, H., & Rose-Krasnor, L. (2012). Adolescent peer interaction and trait surgency weaken medial prefrontal cortex responses to failure. Social Cognitive and Affective Neuroscience, 7(1), 115–124. doi:10.1093/scan/nsq090
Selye, H. (1946). The general adaptation syndrome and the diseases of adaptation 1. The Journal of clinical endocrinology & metabolism, 6(2), 117-230.
Shahrestani, S., Stewart, E.M., Quintana, D.S., Hickie, I.B., & Guastella, A.J. (2014). Heart rate variability during social interactions in children with and without psychopathology: A meta-analysis. Journal of Child Psychology & Psychiatry, 55(9), 981–989. doi:10.1111/jcpp.12226
Smith, C.L., Diaz, A., Day, K.L., & Bell, M.A. (2016). Infant frontal electroencephalogram asymmetry and negative emotional reactivity as predictors of toddlerhood effortful control. Journal of Experimental Child Psychology, 142, 262–273. doi:10.1016/j.jecp.2015.09.031
Smith, J.D., Woodhouse, S.S., Clark, C.A.C., & Skowron, E.A. (2016). Attachment status and mother–preschooler parasympathetic response to the strange situation procedure. Biological Psychology, 114, 39–48. doi:10.1016/j.biopsycho.2015.12.008
Tang, Y.-Y., Holzel, B.K., & Posner, M.I. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 4, 213. doi:10.1038/nrn3916
Tang, Y.-Y., Ma, Y., Fan, Y., Feng, H., Wang, J., Feng, S. , Lu, Q., Hu, B., Lin, Y., Li, J., Zhang, Y., Wang, Y., Zhou, L. & Fan, M. (2009). Central and autonomic nervous system interaction is altered by short-term meditation. Proceedings of the National Academy of Sciences of the United States of America, 106(22), 8865.
Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. (1996). Heart Rate Variability: Standards of measurement, physiological interpretation, and clinical use. Circulation 93(5), 1043-1065. doi:10.1161/01.CIR.93.5.1043
Thayer, J.F., Hansen, A.L., Saus-Rose, E., & Johnsen, B.H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral integration perspective on self-regulation, adaptation, and health. Annals of Behavioral Medicine: A Publication Of The Society Of Behavioral Medicine, 37(2), 141–153. doi:10.1007/s12160-009-9101-z
Ursache, A., Noble, K.G., & Blair, C. (2015). Socioeconomic status, subjective social status, and perceived stress: Associations with stress physiology and executive functioning. Behavioral Medicine, 41(3), 145–154. doi:10.1080/08964289.2015.1024604
Van Hecke, A.V., Lebow, J., Bal, E., Lamb, D., Harden, E., Kramer, A., Denver, J., Bazhenova, O. & Porges, S.W. (2009). Electroencephalogram and heart rate regulation to familiar and unfamiliar people in children with autism spectrum disorders. Child Development, 80(4), 1118–1133. doi:10.1111/j.1467-8624.2009.01320.x
Verner, M., Conzelmann, A., Lehnert, K., Seiler, R., Wassmer, A., & Rammsayer, T. (2010). Subjective stress in female elite athletes and non-athletes: Evidence from cortisol analyses. Europe’s Journal of Psychology, 6(4), 56-70.
Warnock, F.F., Craig, K.D., Bakeman, R., Castral, T., & Mirlashari, J. (2016). The relationship of prenatal maternal depression or anxiety to maternal caregiving behavior and infant behavior self-regulation during infant heel lance: An ethological time-based study of behavior. BMC Pregnancy and Childbirth, 1. doi:10.1186/s12884-016-1050-5
Weiskopf, N., Scharnowski, F., Veit, R., Goebel, R., Birbaumer, N., & Mathiak, K. (2004). Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). Journal of Physiology (09284257), 98(4–6), 357–373. doi:10.1016/j.jphysparis.2005.09.019
Weiskopf, N., Veit, R., Erb, M., Mathiak, K., Grodd, W., Goebel, R., & Birbaumer, N. (2003). Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): Methodology and exemplary data. NeuroImage, 19(3), 577. doi:10.1016/S1053-8119(03)00145-9
White, S.W., Keonig, K., Scahill, L. (2007). Social skills development in children with autism spectrum disorders: a review of the intervention research. Journal of Autism and Developmental Disorders, (37), 1858-1868
Williams, S., & Woodruff-Borden, J. (2015). Parent emotion socialization practices and child self-regulation as predictors of child anxiety: The mediating role of cardiac variability. Child Psychiatry & Human Development, 46(4), 512–522. doi:10.1007/s10578-014-0492-0
Wilson, A.C., Lengua, L.J., Tininenko, J., Taylor, A., & Trancik, A. (2009). Physiological profiles during delay of gratification: Associations with emotionality, self-regulation, and adjustment problems. Journal of Applied Developmental Psychology, 30(6-), 780–790.
Zahn, R., Moll, J., Paiva, M., Garrido, G., Krueger, F., Huey, E. D., & Grafman, J. (2009). The neural basis of human social values: Evidence from functional MRI. Cerebral Cortex, 19(2), 276–283. doi:10.1093/cercor/bhn080