تنظیم احساسات در زوج های بالغ: خلق و خوی، وابستگی و پاسخ HPA به تضاد
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|38821||2007||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Biological Psychology, Volume 76, Issues 1–2, September 2007, Pages 61–71
Abstract Difficulty managing the stress of conflict in close relationships can lead to mental and physical health problems, possibly through dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, the neuroendocrine stress response system. Temperament, an individual characteristic, and attachment, a dyadic characteristic, have both been implicated in emotion regulation processes and physiological reactivity, yet there is no clear consensus on how the two work together to influence the stress response, especially after childhood. The present study investigated the ways in which temperament and attachment together predict HPA response in emerging adult couples. Analyses using multilevel modeling (HLM) found that partners’ dyadic fit on attachment avoidance impacted females’ cortisol response patterns, and attachment avoidance further moderated the effect of males’ emotionality on both their own and their partners’ cortisol. Results are discussed in terms of emotional coregulation processes in romantic attachment.
Introduction Although the experience and regulation of emotion has generally been considered a personal phenomenon, there is growing recognition of the importance of interpersonal relationships for the amplification and repair of emotional states (Diamond and Aspinwall, 2003). More attention has been focused on psychobiological regulation processes in infant–caregiver dyads (e.g., Schore, 1996), yet intimate partnerships throughout life provide a context for the regulation of emotional and physical well-being (Cacioppo, 1994 and Hofer, 1984), making biobehavioral coregulation in adult romantic dyads a key target for further study. The way in which a person or couple responds to stressful situations has important implications for mental and physical health; an inability to cope with the stresses that inevitably arise within relationships sets the stage for distressed relationships, which in turn increase the risk for internalizing disorders (Coyne et al., 2002 and Davila et al., 2003) and morbidity/mortality (e.g., Helgeson, 1991 and Hibbard and Pope, 1993). Physiological stress response, as measured by the output of the hypothalamic-pituitary-adrenal (HPA) axis, offers a promising route by which relationship stress might lead to such disorders, given that dysregulation of this system consistently characterizes depressive and/or anxiety disorders (e.g., Butler and Nemeroff, 1990 and Young et al., 2004), as well as impaired cardiovascular and immune function (Glaser and Kiecolt-Glaser, 1994 and Kuhn, 1989). To understand differences in couples’ responses to conflict and the health implications of these responses, we propose a biopsychosocial model of emotion regulation, in which a combination of intra- and interpersonal characteristics shapes one's response to stressors across various psychobiological systems. In this paper, we seek to illuminate a central part of the pathway from interpersonal stress to health outcomes by investigating how components of romantic partners’ emotion regulation systems work together within and across partners to predict their neuroendocrine response to conflict. Two variables that should be important for regulating emotion in relationship conflict are temperament, an individual characteristic that dictates innate emotionality, and attachment, a dyadic characteristic that describes how a person uses close relationship partners to attenuate distress in the face of threat. Although these two variables have often been linked, both conceptually and empirically, there is no clear consensus on how the two factors uniquely contribute to stress response, especially on the physiological level. Additionally, most of the research on temperament and attachment focuses on infants or children, rather than on adolescents or adults, even though these factors are assumed to remain relatively stable and influential across development. The current study was designed to address this gap in the emotion regulation literature by testing effects of emerging adult partners’ temperament and attachment on their HPA activity during conflict, considering both main effects and interactions within and across partners. 1.1. HPA response and emotion regulation in couples The HPA axis releases the adrenocortical steroid hormone cortisol in response to stress, particularly situations that involve social threat and an element of uncontrollability (Dickerson and Kemeny, 2004). The relevance of this system as a marker of emotion regulation is suggested by its association with subjective distress (Miller et al., 2007). In addition, dysregulation of the HPA system has commonly been found in depression and/or anxiety disorders (e.g., Gold et al., 1988 and Young et al., 2004), which entail a breakdown of the capacity to regulate negative emotion. In particular, increased cortisol reactivity to stressful situations and/or slower recovery to baseline levels, as well as chronically elevated cortisol levels, have been implicated in internalizing disorders (e.g., Heim et al., 1998 and Miller et al., 2007). These features, suggestive of stress hyperactivation, also mark risk for multiple physical ailments, such as diabetes, hypertension, cancer, and cardiovascular disease (McEwen, 1998). Romantic relationships provide a context for the occurrence and consequences of psychosocial stress that activates the HPA system, and ineffective physiological regulation may be both driven by and a contributor to distressed relationships. A series of studies by Kiecolt-Glaser and colleagues (Heffner et al., 2004 and Kiecolt-Glaser et al., 2003; see also Kiecolt-Glaser and Newton, 2001, for a review) have found that partners’ HPA reactivity/recovery patterns relate to their behavior in conflict interactions and relationship quality, with differences in newlyweds’ HPA reactivity prospectively predicting troubled marriages 10 years later. Given these links among exaggerated physiological stress response, disturbed relationships, and poor mental/physical health, we must begin to explore specific pathways of risk for HPA dysregulation and associated negative outcomes in young couples. Evidence from the above studies points to a role for both individual partners’ emotional characteristics and their ability to use one another for support in explaining HPA differences during conflict, suggesting that temperament and attachment may each contribute to couples’ stress response. 1.2. Temperament and emotion regulation Temperament refers to a set of biologically based traits that appear early in life and show at least moderate consistency throughout life (Vaughn and Bost, 1999). As a psychobiological variable, temperament both influences and is influenced by physiological responses to events (Gunnar and Mangelsdorf, 1989). Measures of temperament often include some measure of emotionality (e.g., Buss and Plomin, 1984, Goldsmith and Campos, 1986, Rothbart, 1989 and Thomas and Chess, 1977), with a focus on the tendency toward negative emotional experience and/or expression. The emotionality dimension taps a weakness in the regulation of negative affect that may involve more extreme and/or extended negative reactivity to internal or external stimuli. At the physiological level, emotionality has been found to relate to higher cortisol reactivity to stress (Gunnar et al., 1989, van Bakel and Riksen-Walraven, 2004 and Zobel et al., 2004). This relationship may be buffered by social context, though, with children high in negative emotionality more likely to show cortisol elevations under conditions of less than optimal care (Gunnar and Donzella, 2002). The role of emotionality in adult stress reactivity/recovery remains largely unexplored, though the definition of temperament suggests that it remains an important factor in emotion regulation throughout life. Study in older samples is needed to clarify what emotionality means for mature psychobiological regulation, as well as how relationship factors may buffer or exacerbate its effects. 1.3. Attachment and emotion regulation The way in which an individual uses relationships with others in stressful situations may also be conceptualized as a factor in emotion regulation, and attachment is sometimes discussed as an affect regulation strategy. Bowlby's (1973) definition of attachment as an evolutionarily adaptive bond between infant and caregiver has been extended to other close relationships, including romantic relationships (Hazan and Shaver, 1987), in which the attachment figure provides the sense of “felt security” important for the successful regulation of negative affect (Sroufe and Waters, 1977). Although early approaches to attachment quality distinguished people in terms of categories, more recent approaches find that individual differences in attachment can best be captured by the two dimensions of anxiety, which refers to desire for closeness coupled with anxiety about abandonment, and avoidance, which refers to discomfort with closeness and dependency ( Fraley et al., 2000). These attachment styles are associated with distinct emotion regulation strategies; whereas secure individuals are able to acknowledge negative feelings and cope with them with the help of others, avoidant individuals attempt to deactivate and deny negative emotion, and anxious-ambivalent individuals show a hyperactivation of distress ( Shaver and Mikulincer, 2002). 1 In the context of relationships, secure partners are better able to seek and provide support in anxiety-producing situations (Simpson et al., 1992), and they behave in more constructive ways to resolve conflict (Kobak and Hazan, 1991 and Senchak and Leonard, 1992). Not only do secure individuals report less discomfort and more satisfaction in conflict situations, they are also rated by outsiders as showing less negative affect and more constructive conflict management behaviors (Feeney, 1998). Anxious-ambivalent partners, on the other hand, report and display greater distress during conflict, and avoidant partners have lower-quality interactions (Simpson et al., 1996). These differences appear to translate into differences in psychobiological regulation; both anxiety and avoidance have been found to predict heightened physiological stress reactivity in adults (Carpenter and Kirkpatrick, 1996 and Feeney and Kirkpatrick, 1996). Effects on the HPA system, in particular, have been most clearly documented in children, with security predicting lower baseline cortisol levels (Gunnar et al., 1996) and lower adrenocortical stress reactivity (Spangler and Schieche, 1998). Only one study, conducted with a large subsample of the current study, investigated attachment-related differences in adult's HPA reactivity; main effects of partners’ anxiety and avoidance on cortisol response to conflict tended to confirm the role of anxiety in hyperactivating the stress response and avoidance in deactivating it (Powers et al., 2006). Although this work provides a starting point for understanding the biological sequelae of romantic partners’ attachment styles, significant questions remain regarding the fit between temperament and attachment and between partners’ temperament/attachment characteristics, as determinants of HPA reactivity and recovery. 1.4. Moderating effects Recent work in emotion regulation has suggested the importance of not only main effects of individual difference variables, but also constellations of such variables within persons and across persons in a dyadic context (Diamond and Aspinwall, 2003). Several studies have found that attachment security moderates relations between infant temperament and HPA reactivity, with cortisol elevations evident only in the temperamentally vulnerable children who are also insecurely attached (Nachmias et al., 1996, Schieche and Spangler, 2005 and Spangler and Schieche, 1998). These indications suggest that attachment may serve as a buffering or exacerbating factor in emotionality, a hypothesis that has yet to be tested in older samples. Beyond within-partner moderating influences of attachment on temperament, there may be cross-partner moderating effects, or “goodness of fit” on temperament and attachment characteristics that are meaningful for each partner's HPA reactivity. Both the temperament and attachment literatures show evidence of partner–partner interactive effects on adjustment, and researchers in developmental psychobiology have framed the human attachment dyad as a mutually regulating unit (e.g., Gunnar and Donzella, 2002 and Schore, 2000). Particularly in the context of a conflict, partners must be able to transition together between self-regulation and interactive regulation to achieve a mutually rewarding “dyadic state of consciousness” (Tronick et al., 1998), making the contribution of each partner's regulatory capacities important for the couple's coregulation of emotion. 1.5. The current study The present study was designed to test an integrated model of emotion regulation within developing romantic relationships by investigating links among temperament, attachment, and HPA response to a conflict discussion in emerging adult couples. Based on the evidence reviewed above concerning the role of emotionality and attachment in emotion regulation, we hypothesized that attachment anxiety would strengthen associations (hyperactivating) between partners’ emotionality and HPA activation, whereas attachment avoidance would weaken such associations (deactivating). In addition to temperament-attachment interactions, partner–partner interaction effects were expected. Specifically, dysregulation (in the sense of high negative emotionality and/or attachment insecurity) of one partner was hypothesized to exacerbate the stress-promoting effects of such characteristics in the other partner; conversely, a more regulated style in one partner could buffer the effects of the other partner's dysregulation.
نتیجه گیری انگلیسی
3. Results To address the research questions, a series of HLM models were estimated. First, a baseline model with no predictors at level 2 was fit to the data to illustrate average male and female outcome scores, the correlation between partners’ scores, and the proportion of variability to be explained at level 2. Next, sets of predictors were added to build a full model that included all hypothesized main effects and interactions. Because a large number of predictors were entered and tested simultaneously, increasing the possibility of a type I error, a more conservative alpha level of p ≤ .01 was used to determine which effects should be considered further. To create a baseline cortisol model against which other explanatory models could be tested, variables that could affect cortisol measurements (e.g., medications, blood contamination, etc.) were entered as predictors of male and female intercept, linear, and quadratic terms. Based on this model, male and female partners’ cortisol levels during the discussion were significantly correlated (tau = .31), with nonsignificant associations between partners’ instantaneous rates of change during the discussion (tau = −.01) and their deceleration rates across the sampling period (tau = .002). Beyond these average trajectories, though, there was significant variability across couples in all male and female cortisol parameters, which could be explained by adding predictors at level 2. 3.1. Self × partner model The full model, which included main effects and self × partner interactions on emotionality, anxiety, and avoidance, yielded a significant improvement in fit compared to baseline, χ2(54) = −78.82, p < .05. This model explained 8.3% of the variance in female and 10.7% of the variance in male cortisol levels (intercept), 4.4% of female and 11.3% of male instantaneous rates of change (linear slope), and 3.4% of female and 9.6% of male deceleration rates in cortisol trajectories (quadratic terms). As seen in Table 3, attachment anxiety exerted a main effect on cortisol trajectories, particularly for males; higher anxiety was associated with higher cortisol levels during the discussion for both partners, and for males it also related to less recovery during the discussion/later cortisol peak, as well as a steeper reactivity/recovery curve overall. The female partner's emotionality also predicted less recovery during the discussion/later peak for the male. One self × partner effect reached our significance criterion; the interaction of his and her attachment avoidance predicted female cortisol level during the discussion. An examination of predicted cortisol trajectories plotted at high (75th percentile) and low (25th percentile) values of his and her avoidance indicated that female cortisol was highest if partners were concordant for higher (or lower) avoidance (see Fig. 3). Viewed from another angle, the female's own avoidance related most strongly to differences in her cortisol (higher) if her partner was highly avoidant. Table 3. Self × partner temperament, attachment predicting cortisol trajectories Cortisol parameter T3 level T3 slope Quadratic Coeff. S.E. p Coeff. S.E. p Coeff. S.E. p Female partner Intercept −1.693 .04 <.001 −.121 .02 <.001 −.188 .03 <.001 Blood .415 .12 .001 Antibiotic .448 .18 .01 −.332 .13 .01 Birth control .151 .07 .04 Male avoidance −.041 .06 .47 −.004 .03 .88 .045 .04 .24 Female avoidance .051 .06 .36 −.045 .03 .10 −.047 .04 .23 Male anxiety −.009 .05 .85 .026 .02 .25 .038 .03 .23 Female anxiety .134 .05 .005 .023 .02 .33 −.020 .03 .54 Male emotionality .024 .03 .36 .008 .01 .55 .002 .02 .89 Female emotionality −.022 .02 .31 .002 .01 .87 −.002 .02 .88 Male × female avoidance .163 .07 .01 −.001 .006 .85 −.049 .04 .28 Male × female anxiety −.042 .04 .31 −.012 .03 .72 .004 .03 .88 Male × female emotionality .004 .01 .72 .003 .02 .90 −.0004 .01 .96 Male partner Intercept −1.676 .04 <.001 −.289 .02 <.001 −.206 .03 <.001 Blood .445 .14 .002 Time asleep .062 .02 .01 Allergy medication .329 .12 .008 Male avoidance .014 .06 .80 .008 .03 .80 −.035 .04 .42 Female avoidance .060 .06 .28 −.008 .03 .79 −.006 .04 .89 Male anxiety .129 .04 .006 .063 .02 .01 −.104 .04 .005 Female anxiety .020 .05 .68 −.030 .03 .25 .051 .04 .17 Male emotionality −.002 .03 .96 −.032 .01 .03 .043 .02 .04 Female emotionality −.005 .02 .83 .034 .01 .006 −.019 .02 .27 Male × female avoidance .068 .06 .30 .008 .04 .82 −.063 .05 .22 Male × female anxiety −.034 .04 .42 .048 .02 .04 .032 .03 .33 Male × female emotionality .023 .01 .05 −.011 .01 .10 −.007 .01 .44 Table options Male and female partner avoidance interact to predict females’ cortisol levels ... Fig. 3. Male and female partner avoidance interact to predict females’ cortisol levels during conflict: predicted trajectories shown at high (75th percentile) and low (25th percentile) values of male and female avoidance. Figure options 3.2. Temperament × attachment model The full model including main effects of partners’ emotionality, anxiety, and avoidance, as well as emotionality × anxiety/avoidance interactions, provided a significant improvement in fit compared to baseline, χ2(60) = −96.98, p < .01. This model explained 10.5% of the variance in female and 11.3% of the variance in male cortisol levels, 8.8% of female and 8.2% of male instantaneous rates of change, and 10.9% of female and 11.8% of male deceleration rates in cortisol trajectories (see Table 4). Table 4. Self, partner temperament × attachment predicting cortisol trajectories Cortisol parameter T3 level T3 slope Quadratic Coeff. S.E. p Coeff. S.E. p Coeff. S.E. p Female partner Intercept −1.658 .05 <.001 −.124 .02 <.001 −.187 .03 <.001 Blood .418 .12 .001 Antibiotic .391 .18 .03 −.314 .13 .01 Birth control .122 .07 .08 Male avoidance −.007 .05 .90 −.004 .03 .89 .024 .04 .52 Female avoidance .046 .05 .40 −.042 .03 .11 −.040 .04 .29 Male anxiety −.007 .04 .87 .021 .02 .34 .040 .03 .19 Female anxiety .125 .04 .008 .029 .02 .19 −.029 .03 .34 Male emotionality .024 .02 .35 .011 .01 .38 .001 .02 .94 Female emotionality −.019 .02 .38 .001 .010 .90 −.002 .01 .89 Male emotionality × avoidance −.104 .03 .003 −.017 .02 .32 .077 .02 .001 Female emotionality × avoidance −.018 .02 .44 .009 .01 .45 −.003 .02 .86 Male emotionality × anxiety −.014 .02 .48 −.015 .01 .11 −.012 .01 .36 Female emotionality × anxiety .015 .02 .42 .017 .01 .06 −.016 .01 .20 Male partner Intercept −1.64 .05 <.001 −.277 .03 <.001 −.255 .04 <.001 Blood .422 .14 .004 Time asleep .062 .02 .01 Allergy medication .315 .12 .01 Male avoidance .047 .06 .40 .008 .03 .79 −.052 .04 .24 Female avoidance .050 .06 .37 −.017 .03 .59 −.0004 .04 .99 Male anxiety .131 .04 .005 .055 .02 .03 −.110 .04 .003 Female anxiety .001 .046 .98 −.021 .03 .43 .070 .04 .06 Male emotionality .002 .03 .94 −.036 .01 .02 .046 .02 .03 Female emotionality .005 .02 .82 .030 .01 .01 −.024 .02 .16 Male emotionality × avoidance −.084 .03 .01 −.008 .02 .68 .041 .03 .13 Female emotionality × avoidance .006 .02 .80 −.001 .01 .97 .002 .02 .92 Male emotionality × anxiety .002 .02 .92 −.014 .01 .20 .014 .02 .36 Female emotionality × anxiety −.011 .02 .56 .002 .01 .86 .027 .02 .07 Table options Not surprisingly, the same main effects of attachment anxiety (on both partners’ cortisol) and of female emotionality (on male cortisol) were found as above. A significant interaction of male emotionality × attachment avoidance impacted both his own and his partner's cortisol level during the discussion, as well as her deceleration rate. As depicted in Fig. 4, male avoidance tended to lower his cortisol only if he was high in emotionality; at lower levels of emotionality, avoidance was associated with higher cortisol. The effect of male negative emotionality × avoidance on female partner cortisol was in the same direction but even stronger, with the highest female cortisol levels expected when her partner was high in emotionality and low in avoidance (see Fig. 5). In addition, male avoidance appeared to relate to slower female deceleration rates if he was high, but not low, in emotionality. Male emotionality and avoidance interact to predict their own cortisol levels ... Fig. 4. Male emotionality and avoidance interact to predict their own cortisol levels during conflict: predicted trajectories shown at high (75th percentile) and low (25th percentile) values of male emotionality and avoidance. Figure options Male emotionality and avoidance interact to predict female partners’ cortisol ... Fig. 5. Male emotionality and avoidance interact to predict female partners’ cortisol levels and deceleration rates during conflict: predicted trajectories shown at high (75th percentile) and low (25th percentile) values of male emotionality and avoidance.