By Casey Burgess MA and Brenda Smith-Chant PhD
This article was published as part of Reframed: The Journal of Self-Reg Volume 1, Issue 1 (2017)
Burgess, C. & Smith-Chant, B. (2017) Measuring the Biological Foundations of Self-Reg: Psychophysiological Assessments of the Stress Response. Reframe: The Journal of Self-Reg, 1(1), 91-115
Abstract
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 neu-rological 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.
Eye-Related Measures
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 Glaze
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
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.
Pupillometry
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. How-ever, 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.
Brain-Related Measures
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
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 ventro-medial (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 sociomoral tasks requiring interpretive judgement across ages (Barrasso-Catanzaro & Esling-er, 2016; Weiskopf et al., 2004; Weiskopf et al., 2003).
fMRI
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.
EEG
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 record-ing 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) mea-sured by EEG is a more direct measure of neural activi-ty 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, mak- ing 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, aswell 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 (Segalow-itz et al., 2012), effortful control in terms of the prefrontal cortex’s connectionto 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.
Heart-Related Measures
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,MGregor, 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-Cama cho, & Buss, 2016);
● mindfulness, yoga, or other body–mind interventions, in cluding 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, Grish am, & Moulds, 2011).
Vagal Tone
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; Calk- ins, 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-reg- ulation, demonstrating the initial foundations of the devel- opment of self-regulation from an evidence-based physio- logical 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, Fur- man, 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-ac-tivated, 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.
Skin Conductance
Electrodermal activity (EDA) is a means of measuring the electrical conductivity evident on the skin’s surface, including fingertip sensors (Wilson, Lengua, Tininen-ko, 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 (sym- pathetic) nervous system activity.
Glucose Depletion
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 reg-ulatory 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 rela- tively 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.
Summary
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 bio-metric 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.
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References
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 Interna- tional, 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 co- herence, 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-regula- tion 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 symp- toms 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 ratevariability 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 ofsad-ness 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 Psychophysiol- ogy, 90(2), 207–214. doi:10.1016/j.ijpsycho.2013.07.006
Denson, T., Grisham, J., & Moulds, M. (2011). Cognitive reappraisal increas-es 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.
Electrophysiology, T. F. o. t. E. S. o. C. t. N. A. S. o. P. (1996). Heart rate vari- ability standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065. doi:10.1161/01.CIR.93.5.1043
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. Interna- tional Journal of Psychophysiology, 94(2), 243–243. doi:10.1016/j.ijpsy- cho.2014.08.934
Evans, D.R., Boggero, I.A., & Segerstrom, S.C. (2016). The nature of self- regulatory fatigue and “ego depletion.” Personality & Social Psychol-ogy 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 con- ditions. 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. De- velopmental Psychology, 38(2), 194.
Ferrer, E., & Helm, J.L. (2013). Dynamical systems modeling of physiolog- ical coregulation in dyadic interactions. International Journal of Psycho- physiology, 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., Akshoo-moff, 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., Ken-et, 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-regu-lating 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 moder- ating 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 Medi- cine, 20(8), 642–648. doi:10.1089/acm.2013.0280
Gunning, M., Halligan, S.L., & Murray, L. (2013). Contributions of ma- ternal 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-fo-cused 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:10.1 080/21507740.2015.1047054
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 Cardiol- ogy, 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 car- diac vagal tone in psychophysiological research – Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psy- chology. Feb 20. doi:10.3389/fpsyg.2017.00213
Lamm, C., & Lewis, M.D. (2010). Developmental change in the neu- rophysiological correlates of self-regulation in high- and low-emo-tion conditions. Developmental Neuropsychology, 35(2), 156–176. doi:10.1080/87565640903526512
Lewis, G.F., Furman, S.A., McCool, M.F., & Porges, S.W. (2012). Statisti-cal 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 rela-tionships 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., & Blank- son, A.N. (2010). Moderate vagal withdrawal in 3.5-year-old children is associated with optimal performance on executive function tasks. Devel- opmental Psychobiology, 52(6), 603.
Meir-Hasson, Y., Keynan, J.N., Kinreich, S., Jackont, G., Cohen, A., Pod- lipsky-Klovatch, I., Hendler, T. & Intrator, N. (2016). One-class FM-RI- 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 class- room? 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 re- sponsiveness. Developmental psychobiology, 56(3), 317-326.
Peng, C. K., Henry, I.C., Mietus, J.E., Hausdorff, J.M., Khalsa, G., Ben-son, 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 reac- tions of the autonomic nervous system. Cleveland Clinic Journal of Med- icine, 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 pre- dicts spontaneous regulation of negative emotional expression and sub- sequent 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 mark-er 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-be-ing 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 electro- encephalogram 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 mind- fulness 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.
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:10.1 080/08964289.2015.1024604
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., & Ramm- sayer, 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., & Bir-baumer, N. (2003). Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): Method-ology 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 re- search. 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 medi- ating 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 emo-tionality, self-regulation, and adjustment problems. Journal of Applied De-velopmental 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 ocial values: Evidence from functional MRI. Cerebral Cortex, 19(2), 276–283. doi:10.1093/cer- cor/bhn080
