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Designing Positive PsychologyTaking Stock and Moving Forward$

Kennon M. Sheldon, Todd B. Kashdan, and Michael F. Steger

Print publication date: 2011

Print ISBN-13: 9780195373585

Published to Oxford Scholarship Online: May 2011

DOI: 10.1093/acprof:oso/9780195373585.001.0001

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Positive Psychophysiology

Positive Psychophysiology

The Body and Self-Regulation

(p.25) 3 Positive Psychophysiology
Designing Positive Psychology

Suzanne C. Segerstrom

Timothy W. Smith

Tory A. Eisenlohr-Moul

Oxford University Press

Abstract and Keywords

Self-regulation refers to control over one's emotions, thoughts, and behaviors. Failure of self-regulation contributes to many important individual and societal problems, including problems with eating, spending, interpersonal violence, sexual promiscuity, and alcohol and drug use. Evidence points to a general pool of self-regulatory capacity. This chapter suggests that this capacity depends on, is reflected in, and affects physiology. That is, self-regulation is literally embodied. This assertion seems obvious with regard to the central nervous system, but less so with regard to peripheral physiology. Nonetheless, there is evidence that peripheral regulation of physiological parameters such as blood glucose and heart rate is intertwined with central regulation of the self. The chapter presents a brief overview of physiological systems involved in self-regulation, reviews the empirical links between self-regulation and physiology in several domains, and then suggests directions for future research.

Keywords:   self-regulation, physiology, self-regulatory capacity, blood glucose, heart rate


Self-regulation refers to control over one’s emotions, thoughts, and behaviors. Failure of self-regulation is cited as contributing to many important individual and societal problems, including problems with eating, spending, interpersonal violence, sexual promiscuity, and alcohol and drug use (Baumeister & Vohs, 2004). It is less commonly recognized that self-regulation is also important for optimal functioning. At an individual level, enhancing positive emotion as well as dampening negative emotion appears to require regulatory strength (Demaree, Robinson, Everhart, & Schmeichel, 2004). Savoring positive experiences, for example, both amplifies associated positive affect and requires control over one’s attention and stream of thought to do so (Bryant, 2003; Segerstrom, Roach, Evans, Schipper, & Darville, 2010). At an interpersonal level, the abilities to avoid offense, help, cooperate, and manage self-presentation both contribute to more positive social interactions and require self-regulatory strength (e.g., Finkel et al., 2006; Muraven, 2008; Vohs, Baumeister, & Ciarocco, 2005).

Self-regulation can also contribute to resilience in the face of negative experience. Resilience can arise from several sources: limited exposure to negative experiences, diminished emotional and physiologic reactivity to negative experiences, accelerated recovery from negative experiences, and greater restoration of adaptive resources after such experiences (A. Smith & Baum, 2003; Uchino et al., 2007; Williams et al., in press). For example, effective self-regulation of stress and emotion is evident in people’s choices of situations to enter and avoid, as well as in their active management of their expressive behavior during social interactions that might otherwise become strained (Gross, 2001; Williams et al., in press). Some self-regulatory strategies (e.g., attentional redeployment, cognitive reappraisal) can attenuate negative emotional responses and physiological reactivity during demanding or threatening situations, as well as facilitate a more rapid and complete return of those responses to normal, pre-stressor levels following such situations. Self-regulation also influences subsequent restorative processes that rebuild the individual’s adaptive resources, such as high-quality sleep (Hall et al., 2008), exposure (p.26) to natural environments (Parsons, 2007), and social interactions that induce positive moods and affirm a sense of connection and care (A. Smith & Baum, 2003). Hence, individual differences in resilience reflect effects of self-regulation on exposure, reactivity, recovery, and restoration, ultimately moderating otherwise unhealthy effects of negative experiences.

An abundance of empirical evidence points to factors that cause limitations in the ability to self-regulate. For example, the ability to self-regulate is impaired after an initial act of self-regulation, a phenomenon called self-regulatory fatigue or “ego depletion” (Muraven & Baumeister, 2000). This fatigue is not limited to particular domains—regulation of any domain (including, for example, emotion, behavior, speech, attention, or choice) can result in fatigue in any other domain. Therefore, the evidence points to a general pool of self-regulatory capacity. In the present chapter, we suggest that this capacity depends on, is reflected in, and affects physiology. That is, self-regulation is literally embodied. This assertion seems obvious with regard to the central nervous system (e.g., Compton et al., 2008; Inzlicht & Gutsell, 2007), but less so with regard to peripheral physiology. Nonetheless, there is evidence that peripheral regulation of physiological parameters such as blood glucose and heart rate is intertwined with central regulation of the self. Therefore, we focus here on these peripheral processes. We give a brief overview of physiological systems involved in self-regulation, review the empirical links between self-regulation and physiology in several domains, and then suggest directions for future research.

Measurement and Meaning of Physiological Parameters Related to Self-Regulation

Because readers may not be familiar with how physiological parameters that we later relate to self-regulation are measured, manipulated, and interpreted, we provide a brief orientation to three parameters: one metabolic (blood glucose and glucose regulation), one neuroendocrine (cortisol), and one cardiovascular (heart rate variability). This is clearly not an exhaustive list of physiological parameters that may be relevant to self-regulation, but it is nearly exhaustive of the parameters that have been linked to self-regulation. Later, we expand on this narrowness as something to be remedied as this research moves forward.


All brain processes rely on the metabolism of glucose for fuel (Siesjö, 1978). Though the brain constitutes only 2% of the body’s mass, it consumes roughly 21% of the blood’s glucose (Elia, 1992). Furthermore, there is evidence that higher-order, goal-oriented functions such as self-regulation rank among the most energetically expensive of the brain’s processes, making glucose availability and transport particularly relevant to these tasks (Fairclough & Houston, 2004).

Blood glucose levels are typically measured in milligrams per deciliter (mg/dL) using a glucose meter to analyze a small drop of blood. Blood glucose can be also manipulated by glucose injection or ingestion, most precisely through the use of a glucose clamp technique in which blood glucose levels are “clamped” at hypoglycemic, euglycemic, or hyperglycemic levels by the intravenous administration of glucose or insulin (Defronzo, Tobin, & Andres, 1979). The latter technique allows for the systematic study of physiological, cognitive, and emotional phenomena associated with different blood glucose levels. Because people with diabetes frequently experience low blood glucose and exhibit poor glucose tolerance, some studies of the relationship between blood glucose and self-regulation compare people with and without diabetes.

Blood glucose tolerance, a measure of the effectiveness of glucose transport into cells, is measured after an overnight fast by the administration of glucose followed by periodic assessment of blood glucose levels. A pattern of high blood glucose after glucose administration followed by a return to baseline levels indicates good glucose tolerance, whereas a pattern of abnormally high blood glucose levels after glucose administration followed by a dip below baseline indicates poor glucose tolerance. Glycosylated hemoglobin (hemoglobin A1c) is a long-term measure of the effectiveness of blood glucose control, assessing effectiveness over the last 2–3 months (Koenig et al., 1976). Because it is less sensitive to short-term changes, this measure is especially useful for identifying those with less effective use of glucose. It has been suggested that levels of A1c not exceed 6% for normal individuals (American Diabetes Association, 2008). A1c can be measured using a simple A1c monitor to analyze a small drop of blood.

(p.27) Cortisol

The primary function of cortisol is to mobilize glucose for use in the body by initiating its release from the liver and muscles (Lovallo & Thomas, 2000). Cortisol release follows a normal diurnal pattern, peaking in the morning and falling throughout the day with slight increases associated with meal times. The spontaneous release of additional cortisol by the hypothalamic-pituitary-adrenal (HPA) axis in response to environmental stimuli is most often attributed to a “stress response” elicited by physical or psychosocial stressors (Hennessey & Levine, 1979). However, cortisol levels and patterns of release may also be associated with energy regulation in general. First, cortisol release may change the energetic priorities in the body even in the absence of stress. Second, cortisol release during the exertion of self-regulatory effort may compensate for or anticipate energy that might be required for “fight or flight” or “pause and plan,” that is, self-regulation (Segerstrom, Hardy, Evans, & Fantini, in press). Although the spontaneous release of additional cortisol makes a short-term increase in energy possible, it does so in part by slowing or inhibiting long-term processes such as tissue repair, digestion, and reproduction.

Cortisol levels can be manipulated pharmacologically by administering metyraprone, an inhibitor of glucocorticoid synthesis that decreases cortisol levels, and hydrocortisone, a synthetic glucocorticoid (e.g., Lupien et al., 2002). Cortisol may be measured in blood, urine, and saliva. Only 3%–5% of total blood cortisol is unbound and biologically active; in urine and saliva, only this active free cortisol is present. If measurement by blood sample is necessary, Lovallo and Thomas (2000) recommend an indwelling catheter, inserted 45–60 minutes before the first blood sample is taken. Urine, collected most often in 24-hour samples, may also be used to measure cortisol. Urinary cortisol, which shows good correlations with plasma levels of free cortisol (Moleman et al., 1992), is often used in research examining chronic stressors, as it allows for time-integrated sampling. Salivary cortisol, which also correlates highly with plasma levels of free cortisol (Aardal & Holm, 1995), can be assessed using various simple collection methods. Given the accuracy, ease, and noninvasiveness of salivary cortisol measurement, it is generally considered to be the most desirable way to measure cortisol, especially when one is interested in transitory changes in HPA activity (Kirschbaum & Hellhammer, 1989).

Autonomic Nervous System

Although the process of self-regulation and its physiological outcomes have been assessed through a variety of aspects of autonomic nervous system responses, cardiac parasympathetic activity plays a central role in current theory and research. The heart is dually innervated by the sympathetic and parasympathetic branches of the autonomic nervous system, with opposing effects on heart rate. At rest, parasympathetic influences predominate, as combined pharmacological blockade or surgical denervation of sympathetic and parasympathetic inputs results in increased heart rate. Respiratory sinus arrhythmia (RSA) provides a noninvasive index of parasympathetic activation of the heart. Heart rate accelerates as an individual inhales because the parasympathetic inhibition of the heart is briefly dampened. Heart rate slows down again as parasympathetic inhibition returns when the individual exhales. This oscillation in heart rate across respiratory cycles is RSA, and its magnitude in turn can be used to measure the magnitude of parasympathetic activity. A variety of specific methods are used to measure RSA, though the most common and readily accessible methods quantify the degree of variability in heart rate that falls within the likely frequency range of respiration (i.e., 9–24 cycles per minute). This index, called high frequency heart rate variability (HRV), is the most commonly used index of parasympathetic activation. It can be obtained with relatively simple psychophysiological assessment methods for measuring heart rate and software for extracting the degree of HRV corresponding to the respiratory cycle (for a review, see Thayer, Hansen, & Johnsen, 2008). RSA is the primary contributor to total variability in heart rate, as specific measures of high frequency HRV (i.e., RSA) correlate highly with other, less specific measures such as the root mean square of the successive differences in the interbeat interval (Allen, Chambers, & Towers, 2007; Berntson, Lozano, & Chen, 2005). In the rest of this chapter, we will use the more general label of HRV to refer to results of the relevant empirical studies, which used various means of extracting the parasympathetically controlled variability in heart rate.

(p.28) Taking Stock

What do we know so far about the relationship of self-regulation to physiology? We begin by reviewing relevant theories that link heart rate variability and glucose, respectively, to self-regulatory ability. We then turn to the empirical evidence. Although this is a relatively new area of research, there are studies that address physiological correlates and predictors of self-regulation in several domains: executive cognitive function, repetitive thought (e.g., worry), emotion, and social relationships.

Psychophysiological Theories of Self-Regulation: Polyvagal Theory and Neurovisceral Integration

Two prominent models of the psychophysiology of self-regulation emphasize the close connections of parasympathetic processes in general and HRV in particular to the brain bases of self-regulation. Polyvagal Theory (Porges, 2001, 2007) and the Neurovisceral Integration Model (Hagemann, Waldstein, & Thayer, 2003; Thayer & Lane, 2000) both note that a set of structures and circuits within the pre-frontal regions of the brain called the ventral vagus complex plays a key role in parasympathetic nervous system modulation of emotion and expressive behavior, as well as related physiological responses. Efferent parasympathetic nerves innervate the specific organs involved in expressive behavior (e.g., facial expressions and vocalization). These aspects of emotion and expressiveness, in turn, are central in social interaction. Ongoing parasympathetic inhibition of these responses can be altered quickly as individuals vary the level and tone of their engagement with the social environment. In these perspectives, relatively stable resting levels of HRV reflect the individual’s capacity for regulation, whereas short-term changes reflect temporary application (i.e., HRV increase) or withdrawal (i.e., HRV decrease) of the parasympathetic “brake” on sympathetic activation and related expressive behavior (c.f., Segerstrom & Solberg Nes, 2007). For example, HRV increases as individuals attempt to regulate the experience or expression of negative emotion in response to aversive stimuli (Butler et al., 2006).

The Polyvagal and Neurovisceral Integration perspectives are based in large part on the fact that neural circuits supporting self-regulation of emotion and social behavior and parasympathetic influences on the heart are co-localized in the brain, comprising an integrated system (Porges, 2007; Thayer & Lane, 2009). Anesthesia of the pre-frontal cortex region involved in regulation of emotion and expressive behavior decreases HRV and increases heart rate as parasympathetic inhibition is withdrawn (Ahern et al., 2001). These brain regions also support aspects of attention and response inhibition that are central in executive cognitive functions, which are essential cognitive underpinnings of the modulation of emotional expression and social behavior (Ochsner & Gross, 2007; Posner & Rothbart, 2007; von Hippel, 2007). Further, HRV is correlated with performance on executive cognitive functioning tasks (Hansen, Johnsen & Thayer, 2003), and the degree of activation of related brain structures (e.g., anterior cingulate cortex) correlates with levels of HRV during performance of such tasks (Gianaros, Van Der Veen, & Jennings, 2004; Matthews et al., 2004). Hence, readily available, inexpensive, and noninvasive measures of parasympathetic influences on the heart can provide a physiologic window through which to observe self-regulatory processes.

Psychophysiological Theories of Self-Regulation: The Energy Metaphor

As the empirical evidence below will attest, higher levels of blood glucose and better glucose regulation are associated with better self-regulation and more positive and less negative outcomes related to self-regulation. Based on these findings, Gailliot and colleagues have proposed that blood glucose is a literal measure of the capacity to self-regulate (Gailliot & Baumeister, 2007; Gailliot et al., 2007). However, there are physiological complexities that suggest a more nuanced interpretation. First, glucose regulation in the brain is different than in the periphery. Most glucose transporters in the periphery (i.e., GLUT4) are insulin-regulated and not highly saturable. In contrast, most glucose transporters in the brain (i.e., GLUT3) are not insulin-regulated, are highly saturable, and therefore are completely saturated at a wide range of blood glucose levels, although transporters at the blood-brain barrier (i.e., GLUT1) are less saturable (Frayne, 2003). Second, glucose ingestion elicits a host of complex regulatory responses. For example, increases in blood glucose correlate with an increase in sympathetic nervous system activity, and this relationship may be bidirectional, as both (p.29) feeding and stress can initiate this change. Therefore, with regard to the energy theory of blood glucose,

The strength of this notion lies in its common-sense plausibility, not in scientific evidence, and so the effects may well be epiphenomenal… a plethora of neurohormonal responses are activated for glycaemic homeostasis (Gibson & Green, 2002, p. 185).

Although blood glucose may be a good marker, it is entirely possible that the effects on self-regulatory ability are not due to blood glucose per se, but to the indirect effects of blood glucose on neurohormonal responses, to its ability to index qualities of glucose metabolism and regulation, or to both. The empirical findings reviewed below should be considered in this light.

Empirical Findings: Regulation of Executive Cognitive Functions

Control over basic cognitive processes such as attention is attributed to the central executive; hence, executive cognitive functioning has been proposed as both an example of and a capability essential to self-regulation. Supporting this proposal, self-regulatory fatigue selectively and adversely affects fluid and executive cognitive functions (Schmeichel et al., 2003, 2007). Conversely, people with stronger executive cognitive functions (e.g., error control, working memory) are more effective self-regulators and experience higher levels of well-being (Hofmann, Gschwendner, Friese, Wiers, & Schmitt, 2008; Robinson, 2007).

Evidence suggests that higher availability of blood glucose, a more responsive cortisol system, and higher heart rate variability may all contribute to more effective executive control. Gailliot and Baumeister (2007) provide a review of the literature linking blood glucose to attentional control. For example, blood glucose dropped during performance of the Stroop color-word task, a test of selective attention; Stroop performance correlated inversely with blood glucose; and performance could be restored with a glucose drink (Fairclough & Houston, 2004; Gailliot et al., 2007). Other studies have linked low blood glucose and poor glucose tolerance to impaired performance on difficult, complex, or effortful tasks (Benton and Owens, 1993; Benton, Owens, and Parker, 1994; Fairclough and Houston, 2004; Owens and Benton, 1994; Schultes et al., 2005) and to many forms of self-regulatory failure (see Gailliot and Baumeister, 2007, for review).

A number of medical conditions are associated with deficits on neuropsychological measures of executive functions (Schillerstrom, Horton, & Royall, 2005). Non-insulin-dependent diabetes and insulin resistance are particularly interesting insofar as these conditions reflect poor glucose regulation. A number of large, population-based studies in several countries have found that individuals with diabetes perform more poorly on tests of executive functions and inductive reasoning (Abbatecola et al., 2004; Kuo et al., 2005; Kumari & Marmot, 2005; Qiu et al., 2006; Vanhanen et al., 1999). However, it is not clear whether these deficits are specific to executive cognitive functions (versus general cognitive functioning) or diabetes (versus hypertension). However, one study found that insulin resistance associated with poorer performance on the Trail Making Test, a test of cognitive flexibility, after controlling for Mini-Mental State Examination scores and a host of demographic and medical covariates including age, BMI, physical activity, and hypertension (Abbatecola et al., 1999).

A study examining the relationship of cortisol to executive functioning in preschoolers found a positive relationship between HPA activation and performance (Blair, Granger, and Razza, 2005). Another study found that higher levels of pretask cortisol were associated with poorer executive functioning in women but better executive functioning in men (as measured by the Wisconsin Card Sorting Test; McCormick, Lewis, Somley, & Kaham, 2007). As noted above, cortisol release could either facilitate glucose release or reflect a compensatory response to hypoglycemia.

Finally, high HRV has been associated with better performance at executive cognitive tasks. In a small sample of children in Head Start, higher HRV at rest and increases in HRV during testing were positively, albeit nonsignificantly, associated with peg-tapping and Stroop performance (r = .15–.28; Blair, 2003). Luft, Takase, and Darby (2009) found that HRV was higher during “executive” working memory tasks than in simple attention and reaction time tasks in adults. Another small study of male Norwegian Navy sailors found positive relationships between resting HRV and working memory and continuous performance test reaction time and accuracy (Hansen, Johnsen, & Thayer, 2003). (p.30) When a subgroup of sailors was restricted from physical activity (due to deployment on a submarine), their HRV and executive cognitive performance both declined relative to the subgroup that continued physical training (Hansen, Johnsen, Sollers, Stenvik, & Thayer, 2004). However, one recent population-based study (Whitehall II) did not find any relationship between HRV and cognitive performance, including executive functions (Britton et al., 2008).

In general, then, it seems that blood glucose and glucose regulation are important correlates of executive cognitive functions, including attentional control and cognitive flexibility. Furthermore, HRV seems to index the capacity to effect these functions, and the slower heart rate associated with vagal inhibition may reduce glucose consumption in the periphery and thereby affect glucoregulatory processes. Therefore, these parameters may be important for cognitive abilities that in turn help people control their thoughts, emotions, and relationships, to which we turn next. These relationships, however, are more evident in small-scale studies than in population-based studies. The smaller studies tend to be of younger people with few or none of the comorbid conditions that may complicate the relationship between physiology and cognitive self-regulatory capacity.

Empirical Findings: Regulation of Repetitive Thought

Poor self-regulation manifests in thoughts as repetitive, uncontrolled, negative thought such as worry, rumination, and intrusive thoughts. Although fewer studies have addressed the physiological substrates of these naturalistic cognitions than cognitive function as measured with neuropsychological testing (as reviewed above), there is some evidence that peripheral physiology contributes to control over naturally occurring cognition.

In one study, adults with Type 1 (insulin-dependent) diabetes had blood glucose maintained at euglycemic or hypoglycemic levels by means of the glucose clamp technique. Under hypoglycemic conditions, participants were significantly more likely to report “task-irrelevant” cognitions such as “I thought about personal worries.” Other cognitive variables such as concentration and confidence were unaffected, suggesting that control over worries is particularly correlated with blood glucose (McAulay, Deary, Sommerfield, Matthews, & Frier, 2006).

HRV has also been linked to poor control over one’s thoughts. Alcoholics with lower resting HRV also were more bothered by unwanted and intrusive thoughts (Ingaldsson, Laberg, & Thayer, 2003). Resting HRV (HF power) correlated with cognitive accessibility of intelligence words after experimentally manipulated failure at a purported intelligence test (but not after success); higher accessibility of words related to failure suggests less inhibitory control over failure-related thoughts (Geisler & Kubiak, 2009). In an ambulatory study, worry episodes were characterized by low HRV (Pieper, Brosschot, van der Leeden, & Thayer, 2007). One question is whether worry creates subjective stress, which might lower HRV, or whether episodes of HRV are permissive for worry. One piece of support for the latter interpretation is that although both stress and worry increased HR, only worry was associated with low HRV.

Empirical Findings: Regulation of Emotion

Emotion regulation refers to the process by which individuals “influence which emotions they have, when they have them, and how they experience and express these emotions” (Gross, 1998). Because emotion regulation relies on the same fatigable resources as other forms of self-regulation, it exhibits the same intraindividual variability in strength—variability that may be explained in part physiologically. Specifically, heart rate variability, blood glucose level, the effective use of glucose, and cortisol levels and patterns of release have been associated with one’s ability to regulate emotion.

A growing body of literature indicates that one is more likely to both experience and express negative emotions when blood glucose becomes low due to natural processes or experimental manipulation. In one study, blood glucose was manipulated and maintained at hypoglycemic or euglycemic levels using a hyperinsulinemic glucose clamp technique (Gold, MacLeod, Frier, & Deary, 1995). During acute hypoglycemia, subjects reported lower hedonic tone (less happiness), an increase in tense arousal, and a decrease in energy relative to their euglycemic state. In another study, individuals with diabetes who were prone to hypoglycemic attacks reported higher anxiety and lower levels of happiness than individuals with diabetes alone (Wredling, Theorell, Roll, Lins, & Adamson, 1992). In a series of studies, higher blood glucose levels—either naturally occurring or due to the effects of (p.31) a glucose drink—were associated with feeling less tense and giving fewer negative responses during a frustrating situation (Benton & Owens, 1993). Not surprisingly, the ineffective use of glucose has also been linked to poor emotion regulation. Poor glucose tolerance has been associated with many forms of psychopathology involving emotion dysregulation, especially depression (Winokur, Maislin, Phillips, & Amsterdam, 1988). Taken together, work in this area suggests that adequate glucoregulation is required for active regulation of experienced and expressed emotion.

While research addressing the relationship of cortisol and cortisol patterns to specific emotion regulation processes is virtually nonexistent, there is evidence that deviation from normal levels and patterns of secretion is associated with many disorders involving emotion dysregulation. For example, in one large cohort study, individuals with current or remitted major depressive disorder had a higher cortisol wakening response (increase in cortisol during the first 20–30 minutes of wakefulness) than those without current or remitted depression, suggesting that those with normal patterns of cortisol release may be less likely to experience major depression (Vreeburg et al., 2009). In another study, individuals with Borderline Personality Disorder—a disorder characterized by chronic and intense emotion dysregulation—had higher cortisol wakening response and higher total daily cortisol levels than healthy controls (Lieb et al., 2004). Although higher levels of real or perceived stress are likely to account for at least part of the relationship between psychopathology and abnormal cortisol values, it is also possible that chronic attempts to regulate negative affect influence cortisol release.

HRV has also predicted the ability to regulate emotions in naturalistic and laboratory settings. Higher resting vagal tone has been associated with self-reported ability to regulate emotion, lower frustration levels, and lower emotional arousal in response to daily stressors over a two-week period (Fabes & Eisenberg, 1997). Another study found that HRV mediated the relationship between perception of security in current relationships and effective recovery from a laboratory anger-recall task used to induce anger (Diamond & Hicks, 2005). Further, higher HRV has been linked to more emotional control and less hostility in romantic conflicts among those highly sensitive to rejection (Gyurak & Ayduk, 2008), as well as more approach motivation and less defensiveness among normal subjects (Movius & Allen, 2005).

Higher heart rate variability has also been associated with startle responses to acoustic startle stimuli (Ruiz-Padial, Sollers, Vila, & Thayer, 2003). The acoustic startle reflex is ordinarily potentiated by the presentation of pleasant stimuli and inhibited by the presentation of aversive stimuli. In those with low heart rate variability, however, pleasant stimuli potentiated, rather than inhibited, the startle reflex, suggesting that those with low heart rate variability do not adequately differentiate between emotional cues. Such lack of differentiation means that, for those with lower HRV, emotional cues could have less functional significance and emotion regulation could be impaired (Persad & Polivy, 1993; c.f., Barrett, Gross, Christensen, & Bevenuto, 2001). When subjects in another study were shown a film of a slaughterhouse intended to induce negative emotion, higher resting HRV predicted less negative facial response to the film as well as greater ability to exaggerate facial responses to the film (Demaree, Robinson, Everhart, & Schmeichel, 2004). In a replication and extension of this study, higher resting HRV was once again associated with less negative facial expression in the negative-mood condition (Demaree, Pu, Robinson, Schmeichel, & Everhart, 2006). Notably, self-report measures of emotional reaction to the slaughterhouse film and physiological measures of sympathetic activation during the film were similar across levels of HRV. Because self-reported negative mood and measures of sympathetic activation are associated with emotional experience, emotion per se cannot account for the finding that higher resting HRV was associated with regulation of facial expression (Gross & Levenson, 1993, 1997).

Given the relatively consistent finding that higher HRV corresponds to better emotion regulation, it is not surprising that heart rate variability is associated with many forms of psychopathology involving emotion dysregulation, including generalized anxiety disorder (GAD; Thayer, Friedman, & Borkovec, 1996), panic disorder (Friedman & Thayer, 1998), bulimia nervosa (Kennedy & Heslegrave, 1989), anorexia nervosa (Melanson, Donahoo, Krantz, Poirier, & Mehler, 2004), post-traumatic stress disorder (Blechert, Michael, Grossman, Lajtman, & Wilhelm, 2007), bipolar I disorder (Cohen et al., 2003), major depression (Agelink, Boz, Ullrich, & Andrich, 2002), and borderline personality disorder (Weinberg, Klonsky, & Hajcak, (p.32) in press). Furthermore, increases in heart rate variability have been associated with successful treatment outcomes in patients with major depressive disorder (Balogh, Fitzpatrick, Hendricks, & Paige, 1993; Chambers & Allen, 2002).

Empirical Findings: Regulation of Relationships

Warm and rewarding close relationships can be a source of resilience, but the maintenance of relationship quality often requires considerable self-regulatory effort (Snyder, Simpson, & Hughes, 2006). For example, protecting and enhancing the quality of close relationships such as marriage often requires efforts to manage the tone of potentially conflictual interactions (Halford, Lizzio, Wilson, & Occhipinti, 2007), so as to avoid the negative interaction cycles that typify troubled relationships (Fincham & Beach, 1999; Snyder, Heyman, & Haynes, 2005). Such efforts include the inhibition of angry or aggressive impulses, suppression of verbal or facial expressions of negative affect, self-calming, pursuit of more constructive problem-solving, and attempts to calm one’s partner.

As discussed above, if self-regulatory effort fatigues this limited resource, the individual may be temporarily susceptible to lapses in self-control during interactions with close relationship partners (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Muraven & Baumeister, 2000). Effortful self-regulation in social interactions outside of close relationships has been shown to lead to lapses in self-control during later, unrelated contexts. Further, prior self-regulation during non-social tasks can disrupt subsequent social functioning in several ways, including relaxing restraints on aggressive impulses, impairing self-presentation, and attenuating the tendency to be accommodating to interaction partners (DeWall, Baumeister, Stillman, & Gailliot, 2007; Finkel & Campbell, 2001; Stucke & Baumeister, 2006; Vohs, Baumeister, & Ciarocco, 2005). Given the likely greater importance relative to interactions with partners, these effects of self-regulation should be particularly apparent in interactions with strangers. However, relatively few studies have examined these behavioral processes in established relationships, and few have examined the physiological correlates of those processes.

Low resting levels of HRV are associated with insecurity in romantic relationships (Diamond & Hicks, 2005), greater social isolation (Horsten et al., 1999), and antagonistic social interaction styles (Demaree & Everhart, 2004; Sloan et al., 1994). However, one study found that stress-related decreases in HRV, but not tonic levels, were associated with the quality of social relationships (Egizio et al., 2008). In contrast, in a study of young married couples, resting HRV was positively associated with marital quality (Smith et al., in press). Further, in animal models that manipulate social isolation, disruption of social bonds reduces HRV (Grippo, Lamb, Carter, & Porges, 2007). Obviously, the association of tonic HRV as an index of self-regulatory capacity and the quality of close relationships requires additional research.

One recent study of young married couples provides an illustration of how HRV predicts and reflects social regulation in close relationships. A negative interaction task evoked a significant decrease in resting HRV from before to after the task, compared with either a positive or neutral task (Smith et al., under review). Importantly, this effect was apparent only for wives. During a discussion of an ongoing marital problem (e.g., money, in-laws, children, household responsibilities) a few minutes after this first task, wives who had previously participated in the negative interaction displayed a significant increase in HRV while discussing the issue. In contrast, women who had participated in the prior neutral or positive task displayed a significant decrease in HRV typical of stressful experiences, as did husbands regardless of the type of prior interaction. Hence, there was evidence of a depleting effect of negative interactions on self-regulatory capacity as measured by resting HRV among women but not men, as well as evidence of increased regulatory effort by these women during a subsequent marital conflict discussion. The greater effects on tonic and reactive HRV among women as compared with men could reflect the fact that in close relationships women are often more attentive to relationship quality (Acitelli, 1992; Nolen-Hoeksema & Jackson, 2001) and more active in seeking change and managing disagreements (Denton & Burleson, 2007; Vogel et al., 2007). Greater expenditure of regulatory effort by women in close relationships could contribute to sex differences in the health benefits of marriage (Kiecolt-Glaser & Newton, 2001). That is, women might benefit less from close relationships than do men, in part because their regulatory reserves are more frequently depleted by their greater effort to manage the quality of the relationship. Interestingly, in the study described above (p.33) (Smith et al., in press), a positive interaction with their spouse produced a small but significant increase in women’s resting HRV, suggesting perhaps that some of the health benefits of higher-quality close relationships could occur through the mechanism of restoring or augmenting the capacity for self-regulation.


It could be argued that effective self-regulation is a prerequisite to living the “good life.” Happiness and well-being, as well as the things that seem to bring them, especially good social relationships, are not the result of passivity but rely on active management to avoid and ameliorate the negative (e.g., sadness, conflict) and amplify the positive (e.g., happiness, cooperation). This active management is literally embodied.

The clearest theoretical model and most substantive empirical evidence is for a relationship between tonic HRV and better self-regulatory function across a wide range of domains, from performance on neuropsychological tests of executive functions to reports of marital quality. There is also preliminary evidence for a correlation between self-regulatory effort and phasic HRV (Butler et al., 2006; Segerstrom & Solberg Nes, 2007; Smith et al., in press). Overlap between the central structures that subserve self-regulation and cardiac regulation provides an anatomical rationale for this relationship, and the role of the vagus in modulating aspects of emotional and social behavior (e.g., emotional expression) provides a functional rationale. The fairly direct neurological pathway that the vagus provides between central structures and the heart, furthermore, means that HRV can be interpreted in a straightforward way as efferent parasympathetic outflow.

For the glucoregulatory parameters, including cortisol and blood glucose, both theory and evidence are suggestive but not as clear as for HRV. Low blood glucose appears to compromise performance on neuropsychological tests, particularly executive cognitive function, control over intrusive thoughts, and emotional stability. Although the studies reviewed here focused on adults, it has been argued that children are more vulnerable than adults to the cognitive effects of low blood glucose secondary to fasting because they have less capacity for glucose storage and later release (Gibson & Green, 2002). This developmental difference points to the complexity of glucoregulation. Cortisol and blood glucose are part of a larger glucoregulatory system that includes glucose transporters, an astrocytic glucose “buffer” in the brain, the sympathetic nervous system, and energy use by a host of organs whose energetic priority may be affected. As such, it may be surprising that a simple measure such as blood glucose has predictive power at all. It is possible that this measure, while not a literal indicator of self-regulatory “energy,” does reflect a regulatory state that facilitates or inhibits self-regulatory function.

Moving Forward

The study of psychophysiology has been largely dominated by a focus on negative experience, that is, stress. The literature reviewed above indicates that psychophysiology is also inextricably intertwined with the positive experiences associated with the ability to self-regulate: better cognitive function, less worry and rumination, more positive emotion, and smoother social interactions. Compared with stress psychophysiology, however, self-regulation psychophysiology is in its infancy. Moving forward, then, will require first and foremost the accumulation of work that expands and refines these early studies.

Appropriately for this stage of research, most of the extant research employs simple, noninvasive measures such as blood glucose or HRV, and studies that manipulate these measures are fewer than those that focus on naturalistic individual differences. One important question going forward is whether psychophysiological correlates of self-regulation are markers or determinants of self-regulatory capacity, and this question can be answered only with experimental studies. For example, models of the relationship between HRV and self-regulation posit that HRV merely reflects central processes that are driving self-regulatory activity. However, it is possible that, given the importance of glucose for these central processes, parasympathetic slowing of the heart is not an epiphenomenon but an important redistribution of the energetic demands of the body. The heart consists of 0.5% of the body’s mass but accounts for 9% of its caloric expenditures (Elia, 1992). Pharmacological blockade of the parasympathetic nervous system could begin to answer the question of whether cardiac slowing during self-regulation is an epiphenomenon or an important contributor to self-regulatory capacity.

(p.34) The heart and brain are not the only energetically demanding organs. Others include visceral organs, in particular the liver (2.2% of body mass; 19% of caloric expenditure) and kidneys (0.4% of body mass; 8% of caloric expenditure). It has been known for decades that during sympathetic nervous system activation associated with stress or exercise, blood flow to these organs decreases dramatically (e.g., by 50–75% in the case of the kidney) (Papillo & Shapiro, 1990) while blood flow and the fuel it carries is redistributed to large muscles. If there is a similar redistribution occurring during self-regulation, it is possible that there are peripheral changes in organs other than the heart.

For example, the immune system comprises a large, integrated system of organs (e.g., spleen), cells (e.g., natural killer cells), and molecules (e.g., cytokines) (see Clark, 2008, for an accessible overview.) Although the immune system is probably not a necessary substrate of self-regulation per se, it provides an example of how organ systems are affected by and in turn may affect self-regulation. Almost every function of the immune system, beginning with physical mobilization of cells and their relocation to the site of infection and ending with production of the proteins that will effect immunity, requires energy (Demas, 2004; Elia, 1992; Lochmiller & Deerenberg, 2000). For example, it has been recognized for almost a century that fever comes at a metabolic cost, estimated at 7–13% of total metabolism per degree Celsius. As a consequence of the energetic demands of immunity, energy availability significantly impacts immune function: Energy restriction in the diet and reductions in body fat lead to suppression of immune functions and increased risk of infection. Self-regulation and the immune system may compete. Sometimes the immune system “wins” this competition; the immune system can signal the brain about the presence of infection, and these signals adversely affect motivation to expend energy in other pursuits. In the absence of infection, however, self-regulation and motivation to pursue other goals “win,” resulting in lower immune function (Segerstrom, 2007). However, what happens to other peripheral organs during self-regulation is not known.

Also appropriately for this stage of research, most studies have focused on a single physiological parameter, such as HRV, salivary cortisol, or blood glucose, to study the relationship between physiology and self-regulatory effort and capacity. Moving forward, research should recognize how closely intertwined these parameters are and their abilities to affect each other. For example, cortisol and glucose clearly counter-regulate, so acute increases in cortisol might either reflect hypoglycemia or proactively increase glucose. Parasympathetic nervous system activity has anti-inflammatory effects, slowing not only the heart but this energetically costly function of the immune system. How these systems interact when the brain is working hard will take us beyond snapshots of the physiology of self-regulation and start to show the bigger picture.

Perhaps most important to the forward progress of this area is the recognition that demanding tasks or circumstances differ in both self-regulatory demand (i.e., the degree to which they require control over thoughts, emotions, or behavior) and stressfulness (i.e., the degree to which they elicit negative experience). When self-regulation and stress coexist, apparently perplexing results can be obtained. For example, Fairclough and Houston (2004) characterized falling glucose levels and increasing HRV during a prolonged Stroop task as “contradictory” (p. 185) insofar as the parasympathetic component was interpreted as an inverse measure of stress and effort. However, it appears that the well-characterized cardiac stress effect of the Stroop (i.e., increased heart rate) was eventually overridden by the cardiac self-regulation effect. Another example comes from the literature on stuttering. In laboratory public speaking tasks, people who stutter have a “paradoxical” reduction of heart rate compared with people who do not stutter that can be as large as 20 beats/minute (Alm, 2004, p.123). This lower heart rate has been attributed variously to anticipatory anxiety or to psychodynamic mechanisms in which stuttering is a cathartic activity or a means of need fulfillment (Alm, 2004). Again, this perplexing result is more understandable if cardiac slowing is occurring as a consequence of the self-regulatory effort associated with the attempt to inhibit stuttering. That is, decreases in heart rate among people who stutter as they face a potentially evaluative audience might reflect the increased parasympathetic inhibition of the heart accompanying effortful self-regulation. This hypothesis could be tested directly by measuring HRV. These examples serve to illustrate the importance of understanding the psychological characteristics of laboratory tasks along multiple dimensions rather than the single dimension of “stressfulness.” It may also be important to identify tasks that are relatively pure along these (p.35) dimensions in order to isolate their sometimes contradictory effects on physiological systems (Segerstrom & Solberg Nes, 2007).

Effects of evaluative threat on stress and self-regulation also illustrate the importance of studying the social psychophysiology of self-regulation. Many of our most important self-regulatory challenges—and many of the most important sources of both the positive and the negative qualities of our lives—involve personal relationships. Hence, the social psychophysiology (Cacioppo & Petty, 1983; Smith & Gerin, 1998) of self- and other regulation during interactions in important relationships is an important topic for future research. The capacity for such regulation, the manner and skill with which it is exerted, and the degree of success of such efforts are likely to be important influences of this central aspect of health and well-being. Further, the physiological processes outlined here can provide useful windows on these processes, as well as plausible links to health outcomes. Integrative measurement of physiological processes as described previously (e.g., HRV, glucose, cortisol), related cognitive processes (e.g., individual differences in executive function), and behavioral manipulations and measurements of regulatory effort in social interactions could help address important questions on the role of regulatory capacity and effort in the personal relationships. For example, do self-regulatory capacity and its fatigue contribute to well-known findings in relationship research, such as the fact that cycles of negative reciprocity in couple interactions predict negative relationship outcomes (Snyder et al., 2005)? Further, as suggested by preliminary findings described previously (Smith et al., in press), can positive relationship interactions enhance self-regulatory capacity? Lastly, do efforts to increase regulatory capacity and improve the effectiveness of regulatory effort enhance positivity in close relationships and promote related emotional and physical health benefits? Hence, a positive social psychophysiology of self-regulation and relationships is a promising future direction.

Finally, we (Segerstrom & Smith, 2006; Smith, 2006) have previously called for more research that links short- or medium-term changes in physiology to health endpoints. As research in this area advances, the link to health should be established. There is already evidence that personality dimensions associated with good self-regulation, such as conscientiousness, predict mortality (see Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007, for a meta-analytic review). Positive psychophysiology may provide the link between being good living and longevity.


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