Transient Loneliness and the Perceived Provision and Receipt of Capitalization Support Within Event-Disclosure Interactions

Abstract

Research affirms that loneliness is a distressing experience with social-perceptual and behavioral consequences. Yet, little is known about consequences of transient loneliness, particularly within social interactions. The current study builds on reaffiliation motive and evolutionary models of state loneliness to investigate the effects of experimentally manipulated loneliness on individual and interaction partner perceptions during an event-sharing interaction, within 97 female dyads. Actor–partner interdependence mediation analyses revealed indirect effects for induction group (high vs. low loneliness) on positive affect, enjoyment, responsiveness, and partner positive affect, via induced state loneliness. Furthermore, state loneliness influenced actor and partner provision of responsiveness, via perceived responsiveness. Results reveal interpersonal consequences of transient loneliness, offering preliminary insight into conditions through which state perceptions of isolation may interfere with engagement in positive social interactions. Furthermore, implications for previously theorized evolutionary models of state loneliness and the reaffiliation motive are discussed.

Keywords

loneliness capitalization communication dyadic analysis

Humans have a fundamental motivation to form and maintain lasting, positive, and significant interpersonal relationships (Baumeister & Leary, 1995). This universal tendency has affective consequences, results in pathological outcomes (psychological and behavioral) when thwarted, and elicits goal-directed behavior (Sheldon & Gunz, 2009). Indeed, early theorists argue that much of human behavior is in service of meeting this important psychological need. Loneliness, or perceived social isolation, is one affective consequence of the impeded need to belong. As described by multidimensional models of loneliness, this negative experience has far-reaching affective (e.g., depression), cognitive (e.g., maladaptive perceptions), and behavioral consequences (e.g., risk-related behavior), which negatively impact psychological and physiological health and well-being (Peplau & Perlman, 1979).

Early theorists distinguish between various forms of loneliness in terms of chronicity and stability (e.g., Young, 1982). Whereas chronic loneliness is related to depression, transient loneliness is more common among nonclinical samples (Gross, Juvenon, & Gable, 2002). Despite the clear distinction among chronic/trait and transient/state loneliness, much of the existing research has focused on consequences of chronic loneliness (e.g., morbidity risk, poor cardiovascular health, poor immune system functioning; Hawkley & Cacioppo, 2010). However, recent trends have revealed that rates of transient loneliness are on the rise, with 20% to 40% of individuals in the United States reporting feeling lonely at any given time (e.g., Bekhet & Zauszniewski, 2012). Consequently, it is important for research to also explore the behavioral correlates and consequences of state loneliness.

Whereas chronic loneliness is known to be detrimental to physiological health and well-being, evolutionary models propose that transient loneliness is adaptive to the extent that it serves as a signal of damaged social ties. One component of this evolutionary model suggests that state loneliness motivates social reconnection (Qualter et al., 2015), yet little empirical work has tested how the social-cognitive, affective, and behavioral correlates of state loneliness affect initial social interactions. Previous research has examined the behavioral consequences of state rejection or ostracism, demonstrating that state rejection motivates reconnection and predicts positive evaluation of future interaction partners (Maner, DeWall, Baumeister, & Schaller, 2007). Similarly, DeWall, Maner, and Rouby (2009) show that threat of social exclusion increases selective attention to signs of social acceptance and positive social cues. These studies provide evidence for a belongingness motivation, which is thought to follow threat of social exclusion or feelings of rejection.

Although loneliness is related to social exclusion and rejection, it is functionally distinct (J. T. Cacioppo, Cacioppo, & Boomsma, 2014), as it is rooted in a perceptual deficit between what one desires in social relationships and what one actually receives. Furthermore, loneliness is thought to serve as a signal that belongingness needs are not sufficiently met (Baumeister & Leary, 1995; J. T. Cacioppo et al., 2006). As such, one may feel lonely in the absence of social rejection or ostracism. The research on social rejection, state self-esteem, and belongingness-regulation noted above might suggest that state loneliness would similarly lead to greater liking for others, more positive impressions of social targets, and a greater interest in making friends, for example (e.g., Maner et al., 2007). However, evidence demonstrates that experimentally manipulated loneliness (state loneliness) relates to similar social-cognitive and affective consequences as chronic loneliness, which promote social withdrawal (J. T. Cacioppo et al., 2014). In example, seminal research by J. T. Cacioppo et al. (2006) demonstrates that manipulations of loneliness in the lab are followed by increases in shyness, anxiety, fear of negative evaluation, depressive symptoms, and decreases in social skills and self-esteem. Otherwise stated, even at the state level, loneliness results in “paradoxically self-defeating” behavior and social cognitions, which can be costly for individuals in the moment, although they may be adaptive on an evolutionary time scale (J. T. Cacioppo et al., 2014, p. 6).

It is likely, then, that state loneliness is consequential to social functioning in more immediate social interactions. Indeed, previous work has theorized that initial social withdrawal may be necessary for state loneliness to successfully motivate reconnection, as this activates a cognitive reaffiliation process (Qualter et al., 2015). As such, immediate social interaction may costly in the moment, even among the transiently lonely. Qualter et al. (2015) call for more research to investigate how the cognitive reaffiliation process affects behavior, and in particular for researchers to examine transient loneliness and its association to the different components of the reaffiliation motive model (RAM). Although not a direct test of the RAM, the current study addresses gaps in the literature by examining social-behavioral and social-perceptual consequences of state loneliness within a lab-based social interaction. Specifically, we explore whether feelings of loneliness inhibit individuals from reaping the benefits of positive social interactions, such as the mutual disclosure of positive events (Gable & Reis, 2010). In doing so, we provide insight into avenues through which even transient experiences of loneliness may be consequential to social interactions in the moment.

Loneliness and Self-Disclosure

Previous research has demonstrated that loneliness is related to deficits in social behavior (e.g., poor social skills, social withdrawal; Peplau & Perlman, 1979) and that lonely persons rate social interactions more negatively (Hawkley & Cacioppo, 2010). These consequences have been described via a transactional model, which postulates that social-behavioral deficits (e.g., social skills) operate on the environment to exacerbate loneliness (Ernst & Cacioppo, 1998). As a part of this model, loneliness could interfere with processes and behaviors vital to the development of healthy interpersonal relationships.

One such behavior vital to intimacy development is self-disclosure (e.g., Reis & Shaver, 1988). Indeed, self-disclosure is considered a “building block” of relationships, facilitating feelings of closeness, intimacy, social connectedness, and trust (Reis & Shaver, 1988, p. 389). Loneliness relates to a lower willingness to disclose negative events (Rotenberg, 1997), and lonely persons view self-disclosure as risky, to the extent that it poses a threat of potential social rejection (Rotenberg, 1997; Wittenberg & Reis, 1986). Whereas avoidance of self-disclosure may be more comfortable in the short term, doing so prevents lonely persons from reaping the social rewards and resources of this interpersonal process. That is, the lonely are missing out on important social interactions, which could aid the development of close relationships and reduce loneliness over time.

Recent work has found that the disclosure of positive events, or capitalization, is a particularly important interpersonal process (e.g., Gable, Gonzaga, & Strachman, 2006; Langston, 1994). Sharing good news with others is associated with greater intimacy, trust, reduced loneliness, and increased life satisfaction (Gable & Reis, 2010). Importantly, the benefits of capitalization are contingent on the extent to which individuals perceive others as providing a supportive response to their positive event disclosure (perceived responsiveness; for example, Reis et al., 2010). Responses that express care, validation, and enthusiasm result in increased positive affect and well-being, above and beyond positivity reaped from the event itself. Conversely, responses that point out negatives of the event, or disregard and ignore the positive event altogether (i.e., Gable et al., 2006), are associated with greater loneliness, less satisfaction, and less intimacy.

Whereas research has explored how loneliness influences self-disclosure, this work has typically focused more generally on the disclosure of private, personal, and intimate topics, or on the disclosure of recent stressful experiences (e.g., Solano, Batten, & Parish, 1982). Less research has investigated loneliness in relation to the disclosure of positive events, or capitalization. Although research has shown that supportive capitalization interactions relate to lower levels of lonely mood (Gable & Reis, 2010), research has not considered the impact of state loneliness, experienced prior to or during a capitalization interaction, on perceptions and behaviors vital to the capitalization process.

Building on previous research, the current study examines the effects of loneliness on the perceived receipt and provision of responsive behavior, among “lonely” persons and their interaction partners. In addition, this study explores whether loneliness inhibits lonely persons and interaction partners from reaping the social and personal benefits of capitalization (i.e., liking and enjoyment of the interaction, enhanced positive affect). In doing so, this study provides insight into one pathway through which state loneliness may be problematic within initial social interactions, and thus potentially affect the motivation to seek connection with others.

Responsiveness as an Integrative Process

Theories of close relationships postulate that social interactions are transactional and dyadic in nature, shaped by the cognitions, needs, and motives of individuals in the interaction (e.g., Clark & Lemay, 2010). Furthermore, self-disclosure and perceived responsiveness are integrative processes, wherein one individual’s perceptions of partner responsiveness influence future disclosure and the provision of supportive behavior (Reis & Shaver, 1988). Interestingly, behavioral consequences of loneliness are hypothesized to function through a similar transactional process, wherein behaviors of lonely persons negatively impact the experience and behavior of interaction partners (i.e., transactional model of loneliness; J. T. Cacioppo, Fowler, & Christakis, 2009). Additional evidence suggests that loneliness is contagious, and theorists postulate that the emotional, cognitive, and behavioral consequences of loneliness contribute to the induction of loneliness in interaction partners (i.e., induction hypothesis; J. T. Cacioppo et al., 2009). For example, lonely persons typically exhibit less trust and greater hostility in their interactions. Thus, interaction partners may feel less satisfied and consequently experience loneliness.

Yet, despite the interpersonal, dyadic consequences of loneliness, existing research exploring loneliness has typically taken an individual approach, focusing on the perceptions and experiences of lonely individuals. Furthermore, work examining the transactional and contagion effects of loneliness has focused on the experiences of the chronically lonely. To gain a full understanding of how loneliness functions in an interpersonal context, it would be important to examine the perceptions and behaviors of both lonely individuals and their interaction partners in situ. The current study employs a dyadic approach by assessing the perceptions of lonely individuals and their interaction partners following a lab-based disclosure interaction. The Actor–Partner Interdependence Model (APIM; Cook & Kenny, 2005) accounts for interdependence in dyadic data, allowing one to examine the impact of a person’s causal variable on his or her own outcome variable (actor effect), as well as on the outcome variable of their partner (partner effects). In relation to the study of loneliness, the APIM would allow for the examination of how, for example, Kelley’s level of loneliness influences her own experiences during a social interaction as well as those of her partner, Molly. That is, if Kelley is feeling lonely and thus reports low levels of enjoyment during an interaction with her partner Molly, will Molly also report less enjoyment following the interaction? Will Kelley’s loneliness negatively impact Kelley and Molly’s experience in the social interaction? In the present analyses, we employed a variation of the APIM to incorporate tests for mediation. Specifically, we utilized the Actor–Partner Interdependence Mediation Model (APIMeM; Ledermann, Macho, & Kenny, 2011) to explore how an experimental loneliness induction (high vs. low loneliness) affects actor and partner outcomes via levels of state loneliness. To our knowledge, this is the first study to date to explore the dyadic effects of loneliness within face-to-face social interactions.

Current Investigation

The current study comprised a lab-based social interaction paradigm to explore the effects of transient loneliness on individual and interaction partner perceptions and behavior during the mutual disclosure of positive events. By randomly assigning participants to receive a high versus low loneliness induction, the current study provided for the direct examination of how state loneliness relates to behavioral outcomes, controlling for the effects of preexisting differences in loneliness. The decision to use high versus low loneliness induction groups was based on well-established methods of inducing loneliness and social rejection, wherein loneliness or rejection groups are compared with low loneliness (or acceptance) control groups (e.g., J. T. Cacioppo et al., 2006; Maner et al., 2007).

We predicted that participants receiving a high loneliness mood induction (i.e., “lonely” participants) would report higher levels of state loneliness, and thus lower levels of enjoyment, less liking toward their interaction partner, positive affect, and perceived responsiveness (i.e., actor indirect effects) following the interaction. Drawing from integrated models of disclosure, we anticipated that lab partners of “lonely” individuals would also report less enjoyment, liking, positive affect, and perceived responsiveness, due to their partners’ state loneliness (i.e., partner indirect effects). As an exploratory step, we also examined whether state loneliness would predict self-reported provision of responsive behavior, via individual and lab partner perceptions of received responsiveness.

Method

Participants

Participants (eligible if 18 or older, no current depression, female) were recruited from a larger online survey study to participate in a seemingly unrelated 45-min lab study for extra credit and entrance into a gift-card lottery.¹ In the current study, gender was held constant to control for potential confounds given gender differences in related processes (i.e., self-disclosure behavior and interpersonal attraction, Dindia & Allen, 1992; and self-labeling measures of loneliness and lonely mood, Borys & Perlman, 1985). The initial online survey provided data regarding participant demographics (age, gender), eligibility criteria, and individual difference variables used in a randomization check. Eligible and interested participants were provided a link to an online lab session scheduler where they could sign up for a lab session. Participants were randomly paired in lab sessions based on their selected lab time.

Out of eligible participants who expressed interest in participating in the lab study (N = 412), 334 signed up for a lab session (167 possible interaction dyads). Sixty-four percent of possible lab sessions were attended by both participants (N = 107 dyads, 214 individuals). Data from five lab sessions were eliminated, as at least one of the participants did not follow instructions during the mood manipulation activity. In addition, data from three participants were excluded from key analyses, as they correctly guessed the purpose of the study during debriefing procedures (described below). Two additional dyads were eliminated from key study analyses as they indicated knowing one another “very well” prior to the study (following procedures by Reis, Maniaci, Caprariello, Eastwick, & Finkel, 2011), thus rendering a final sample of N = 194 (97 complete interaction dyads). Average age for the final analysis sample was 25.49 years (SD = 8.61). Participants were mostly Non-Hispanic/White (70%) and pre-bachelors (93%).

Procedure

Prior to their scheduled lab session, participants were randomly assigned into high loneliness (n = 132; 66 dyads) or low loneliness dyads (n = 62; 31 dyads). In all dyads, one participant was randomly assigned to the role of “Person A.” In high lonely dyads, “Person A” received a high loneliness mood manipulation, and their lab partner received a low loneliness mood manipulation. In low loneliness dyads, both participants received a low loneliness mood manipulation. It should be noted that individuals receiving the high loneliness induction were paired with a lab partner who received a low loneliness induction; as such, there were 66 dyads with one “high loneliness” individual and a “low loneliness” lab partner; the remaining 31 dyads included individuals who had both received a social low loneliness induction. Thus, at the individual level, 128 participants received a low loneliness induction, whereas 66 received a high loneliness induction.

Individuals in both types of dyads completed three primary activities during the lab session, described below. Participants completed all surveys and individual activities in a separate room from their lab partner.

Getting-to-know-you activity

The first activity was a 5-min variation of the “Fast Friends” procedure (Aron, Melinat, Aron, Vallone, & Bator, 1997), during which participants exchanged casual getting-to-know-you questions (e.g., “Given the choice of anyone in the world, whom would you want as a dinner guest?”). The purpose of this exercise in the current study was not to facilitate interpersonal closeness, but rather to acclimate participants to the lab setting, video cameras, and to their lab partner. Given the brevity of this exercise (5-min vs. the original 45-min), we did not expect this to interfere with our loneliness induction, but rather to provide an ecologically valid context for capitalization.

Self-reflection exercise: Experimental mood induction

Following the getting-to-know-you exercise, participants were separated for a 5-min self-reflection exercise (experimental mood induction); all participants were videotaped during this activity, pending their consent. Mood induction procedures were based on previous studies employing the autobiographical recall method (J. T. Cacioppo et al., 2006; Rotenberg & Flood, 1999). The loneliness induction was pilot tested, revealing significant differences in levels of lonely mood—F(1, 61) = 5.51, p = .02, η² = .08; high loneliness: M = 1.27, SD = 1.04; low loneliness M = 0.70, SD = 0.99—as assessed via an item on the Positive and Negative Affect Schedule (PANAS; 0-not at all, 4-extremely; Watson, Clark, & Tellegen, 1988). A further description of the pilot is provided in supplemental materials.

Participants receiving the high loneliness mood manipulation were asked to spend 3 min reflecting on a series of statements adapted from the UCLA (University of California, Los Angeles) Loneliness Scale. Specific statements included the following: “Think of a time when you felt you lacked companionship. Perhaps you felt like you had no friends” and “Think of a time when you were no longer close to anyone. Perhaps you felt like you just didn’t belong.” The low loneliness induction group reflected on the following statements: “Think of a time when you felt a sense of belonging. Perhaps you were a member of a group” and “Think of a time when you felt you had someone you could share anything with. Perhaps this was a person who was or who could be your best friend” (adapted from J. T. Cacioppo et al., 2006).

Participants in both conditions were then asked to describe these experiences out loud to a video camera for an additional 2 min. Compliance for the self-reflection activity was assessed via two independent coders, blind to the inclusion of the mood manipulation. Participants were flagged as “noncompliant” if they did not follow the instructions outlined by a research assistant. Following the induction activity, participants completed a brief mood manipulation check assessing state loneliness.

Event-sharing activity: Capitalization interaction

Following the manipulation check survey, participants were reunited for a 10-min event-sharing activity. This activity was modeled after Gable et al.’s (2006) capitalization paradigm, wherein interaction partners take turns describing recent positive events. Participants were asked to engage in the conversation as if it were a normal interaction they would have with someone they knew well. In high lonely and low lonely dyads, the participant assigned to the role of “Person A” was asked to share first. The decision to assign sharing order was based on a concern for confounds related to individual differences in eagerness/willingness to share. Following this activity, participants were separated for the postexperiment survey and provided with a link to an online form where they could redeem extra credit and entry into a gift-card raffle.

Before leaving the lab, participants were fully debriefed and probed for suspicion (funnel debriefing; Bargh & Chartrand, 2000). This debriefing procedure provides participants several opportunities to reveal their awareness for the study purpose via responses to a brief questionnaire (e.g., “What did you think the purpose of this study was?”). Participants who indicated awareness that the current study was focused on loneliness and self-disclosure were excluded from key analyses. Finally, participants completed a brief self-affirmation activity to protect against long-lasting effects of the mood manipulation.

Measures

Measures described below were collected during the lab study session, apart from chronic loneliness, which was administered in the earlier online survey.

Chronic loneliness

Chronic loneliness was assessed via the 20-item UCLA Loneliness Scale (Russell, 1996) via a 4-point rating scale (1-never, 4-always). Example items included the following: “How often do you feel that you are ‘in tune’ with the people around you?” and “How often to you feel that your relationships with others are not meaningful?” Items demonstrated adequate internal consistency (α = .86).

Manipulation check

State loneliness following the mood induction was assessed via a validated two-item assessment of loneliness. Participants rated the extent to which they felt lonely (“I am feeling lonely right now,” “At this moment, I feel quite lonely”; 1-strongly disagree, 5-strongly agree; Zhou, Sedikides, Wildschut, & Gao, 2008). As suggested by Zhou et al. (2008), responses were combined to form a single-state loneliness score (α = .93). Manipulation check analyses were conducted controlling for general positive and negative affect, assessed via the Positive and Negative Affect Schedule (Watson et al., 1988). Negative affect items were summed to create a negative affect score (α = .83); positive affect items were summed to assess positive affect (α = .86).

Postinteraction positive affect

Postinteraction positive affect was assessed via the PANAS (Watson et al., 1988). Participants rated the extent to which they felt a series of positive and negative moods “now or in the past few minutes,” using a 5-point rating scale (1-very slightly/not at all, 5-extremely). General positive affect was computed by summing the positive affect items (α = .90).

Postinteraction perceived responsiveness

The four-item Perceived Responses to Capitalization Attempts Scale (PRCA; Gable et al., 2006; Gable, Reis, Impett, & Asher, 2004) was administered to assess the presence of four potential partner responses to capitalization attempts along two dimensions (i.e., active vs. passive, constructive vs. destructive), on a 5-point rating scale (1-not at all true of our interaction, 5-very much true of our interaction). The PRCA assesses the perceived receipt of active-constructive (“When I told my lab partner about my positive event she reacted enthusiastically to my good event”), active-destructive (“pointed out the potential problems or down sides of the good event”), passive-constructive (“said little, but I knew she was happy for me”), or passive-destructive responses to shared positive events (“seemed disinterested”). Because active-constructive responses have been shown to be the most adaptive form of responding, Gable et al. (2006) recommended creating a composite responsiveness score by taking the average of the three nonadaptive responses and subtracting them from the adaptive (i.e., active-constructive) response score (see also Gable et al., 2004). Thus, higher scores indicate more positive and less negative responses to capitalization attempts. Each item in this four-item scale represents a distinct category of responsiveness, only two of which were correlated in this sample (i.e., active-constructive and passive-destructive; r = –.46, p < .001). Given conventions delineated by Bollen and Lennox (1991) regarding indicators as causes (vs. effects) of a latent construct, it was not appropriate to test for internal consistency of these four items.

Perceived provision of responsive behavior

Self-reported provision of responsive behavior during the event-sharing activity was computed via 10-items developed by Kashdan, Ferssizidis, Farmer, Adams, and McKnight (2013), originally adapted from Reis’s (2003) Responsiveness Scale. Participants rated the extent to which the 10 items applied to their own response provided to their partner’s disclosure during the event-sharing activity (1-not at all, 5-very much; for example, “I was responsive to my lab partner’s needs,” and “I expressed liking and encouragement toward my lab partner.”). Following Reis (2003), items were averaged to create a perceived responsiveness measure (α = .84).

Enjoyment and partner liking

Interaction enjoyment/amusement was assessed via three-items (Reis et al., 2010). Participants rated the extent to which they enjoyed the interaction (1-strongly disagree, 9-strongly agree; for example, “I enjoyed my interaction with the other participant,” and “This interaction was a lot of fun.”). Items were averaged to create a single enjoyment/amusement score (α = .90). Partner liking was similarly assessed with four items (1-strongly disagree, 9-strongly agree; for example, “I liked the other participant,” and “The other participant was a warm person”). Items were averaged to create a single liking score (α = .88).

Prestudy familiarity

At the end of the postinteraction survey, participants indicated the extent to which they previously knew/had interacted with their lab partner prior to the study (0-not at all, 2-very well; for example, “How well did you know your lab partner before participating in the study today?”; Reis et al., 2011). In line with Reis et al. (2011), we eliminated dyads wherein both participants indicated knowing one another very well prior to the study.²

Data Analysis Strategy

To test effects of state loneliness on responsiveness and capitalization outcomes (e.g., positive affect, enjoyment, liking), we conducted a series of APIMeM analyses (Ledermann et al., 2011) within structural equation model analyses (Mplus v. 6.11; Muthén & Muthén, 2008). Structural equation modeling provides a direct approach to examining proposed indirect effects within APIMeM through the estimation of confidence intervals (CIs) estimated via bootstrapping. Recent work has demonstrated that bootstrapping offers various advantages to the estimation of indirect effects over the traditional approaches, including the absence of strict distributional assumptions of normality, particularly with smaller sample sizes (Preacher & Hayes, 2008).

As dyad members were randomly paired acquaintances, dyads in the current study were treated as indistinguishable. Kenny, Kashy, and Cook (2006) suggest treating acquaintances as indistinguishable in the absence of a meaningful distinguishing variable, advising against assigning an arbitrary distinguishing variable. We conducted a test of distinguishability using multilevel modeling, revealing that the null hypothesis that dyads were indistinguishable could not be rejected, χ²(4) = 3.34, p = .50. Thus, based on Ledermann et al. (2011), we conducted models to test indistinguishable pairwise effects of induction condition on actor state loneliness, and for state loneliness on actor and partner interaction outcomes. In our APIMeM models, actor effects refer to the direct effect of one individual’s induction conduction on her levels of state loneliness, and subsequent enjoyment (liking, positive affect, and responsiveness). Partner effects refer to the effect of one individual’s level of state loneliness on her partner’s level of enjoyment (liking, positive affect, and responsiveness).

Equality constraints were imposed among actors and partners on all direct effects ( $a_{A 1} = a_{A 2} = b_{A 1} = b_{A 2} = {c^{'}}_{A 1} = {c^{'}}_{A 2}$ , $a_{P 1} = a_{P 2}$ , $b_{P 1} = b_{P 2}$ , and ${c^{'}}_{P 1} = {c^{'}}_{P 2}$ ), to account for the indistinguishability of dyad members. We did not anticipate partner effects of condition on partner state loneliness (assessed during the manipulation check, prior to the event-sharing interaction). As such, we fixed partner effects of actor condition on partner state loneliness at zero. To account for chance correlation between dyad members’ mood manipulation and partner state loneliness, we specified a correlation between induction condition and the disturbance of the lab partners’ state loneliness score (see Figure 1). Finally, given the number of outcomes tested in the hypothesized models, controlling for Type 1 error was a concern (e.g., West, Popp, & Kenny, 2008). To address, we applied the Benjamini–Hochberg procedure (Benjamini & Hochberg, 1995) to statistically significant findings and make note of the cases where interpretation of results change as a result of the correction.

Figure 1.

APIMeM results for model predicting perceived partner responsiveness.

A post hoc power analysis determined that the final sample size of N = 194 (97 same-sex dyads) provided sufficient power to detect group differences in the loneliness induction, with a medium effect of the induction on state loneliness (η² = .08, as revealed in the pilot study) and a desired power of .80. Although no formal power analyses were conducted for the dyadic mediation models due the complexity of the method, our sample of 97 dyads was comparable with previous studies applying the APIMeM model (e.g., Lim, Shon, Paek, & Daly, 2014).

Results

Disclosure Task Descriptives

Two independent raters viewed the capitalization interaction videos and coded for the domain/topic of the events shared by each participant. Raters coded whether participants’ shared events related to a romantic partner/family/friends, other people besides partner/family/friends, school/job, or health. Of the events shared, 73% discussed friends/family/romantic partners, 37% school/work, 6% “nonclose” others, 5% individual health. There were no induction group differences in domain shared, and topic domains did not relate to state loneliness or positive affect, enjoyment, liking, responsiveness (perceived receipt and provision of responsiveness). However, there was a significant difference among shared topic categories on coder ratings of how personal disclosures were during the interaction, F(1, 121) = 2.85, p = .04, η² = .07. Post hoc tests revealed that event disclosures discussing family/friends/romantic partners were rated as significantly more personal (M = 3.50, SD = 0.71; range = 1-5) than were disclosures about school/work (M = 3.13, SD = 0.57).³

Randomization and Manipulation Checks

Summary statistics and bivariate correlations are provided in Table 1. A randomization check revealed that induction groups did not differ on prestudy chronic loneliness, F(1, 191) = 0.03, p = .86, η² = .00. Results of the manipulation check revealed significant group differences in state loneliness, F(1, 192) = 9.90, p = .002, η² = .05. Participants in the high loneliness condition reported higher state loneliness (M = 2.35, SD = 1.11), relative to individuals in the low loneliness condition (M = 1.85, SD = 1.00).

Table 1.

Means, SDs, and Bivariate Correlations Among Study Variables (N = 194).

	High Lonely Group		Low Lonely Group		Range	1	2	3	4	5	6
	M	SD	M	SD	Range	1	2	3	4	5	6
1. Lonely (MC)	2.35	1.11	1.85	1.0	1.00-5.00	—
2. Perc. RSP	2.25	1.15	2.36	1.20	−1.00-4.00	−.17*	—
3. Prov. of RSP	4.33	.48	4.22	.55	2.60-5.00	−.08	.43***	—
4. Liking	8.47	1.62	8.26	1.65	2.50-10.00	−.09	.29***	.53***	—
5. Enjoyment	8.47	1.84	8.27	1.85	1.33-10.00	−.15*	.21***	.45***	.55***	—
6. PA	33.15	8.81	31.81	8.75	10.00-50.00	−.26**	.28***	.46***	.31***	.32***	—

Note. N = 194; Lonely (MC) = loneliness manipulation check; Perc. RSP = perceived responsiveness; Prov. RSP = provision of responsiveness; PA = positive affect.

p < .05 level (two-tailed). **p < .01 level (two-tailed). ***p < .001 level (two-tailed).

As loneliness is a form of affect, it was important to establish that induction groups differed in lonely mood, unique from general levels of positive and negative affect. Group differences in postinduction negative affect (manipulation check survey) were marginally significant, F(1, 192) = 2.87, p = .092, η² = .02; group differences in postinduction positive affect were significant, F(1, 192) = 5.73, p = .018, η² = .03. The high loneliness manipulation group reported significantly less positive affect (M = 24.09, SD = 7.04) relative to the low loneliness group (M = 26.49, SD = 6.42). A series of ANCOVAs revealed that induction condition continued to relate to state loneliness, above and beyond postinduction levels of negative, F(1, 192) = 5.20, p = .024, η² = .03, and positive affect, F(1, 192) = 6.05, p = .015, η² = .03.

Actor–Partner Mediation Models

A series of APIMeM models investigated actor indirect effects of actor induction condition on actor outcomes via actor state loneliness, and partner indirect effects of actor induction on partner outcomes via actor state loneliness (Supplemental Figure 1.1).⁴ Actor effects were revealed showing that participants in the high loneliness condition reported higher levels of state loneliness than those in the low loneliness condition, b = −0.85, SE = 0.35, p = .015, bias-corrected (BC) 95% CI: [–1.53, –0.169], as predicted (see Figure 1). Loneliness was in turn predictive of lower postinteraction levels of positive affect (b = −1.02, SE = 0.19, p = .000, BC 95% CI: [–1.54, –0.484]), and perceived responsiveness (b = −0.08, SE = 0.03, p = .02, BC 95% CI: [–0.150, –0.082). There was a marginally significant difference in postinteraction positive affect among induction groups (b = −2.71, SE = 1.51, p = .072, BC 95% CI: [–4.455, 1.514]). This direct effect did not reach statistical significance, after adjusting for false discovery rate (FDR). There was also a marginally significant partner effect for state loneliness on partner positive affect (b = −0.45, p = .08, BC 95% CI: [–0.944, 0.036]); this effect was no longer significant upon adjusting for FDR. No other significant effects were revealed (Supplemental Table 2.1).

Furthermore, there were significant actor indirect effects of the mood induction on positive affect (a₁b₁ = .87, BC 95% CI: [0.212, 1.92]), enjoyment (a₁b₁ = .11, BC 95% CI: [0.012, 0.304]), and perceived responsiveness (a₁b₁ = .07, BC 95% CI: [0.012, 0.195]), via increased state loneliness (Table 2; Figure 1). Participants who received the high loneliness induction reported greater state loneliness, and thus less enjoyment, positive affect, and reported receiving a less supportive response from their lab partner during the interaction, relative to participants receiving a low loneliness induction. In addition, significant partner indirect effects were revealed for actor condition on partner positive affect (a₁b₁ = .38, BC 95% CI: [0.013, 1.237]) via actor state loneliness; those who received a high loneliness induction reported greater state loneliness, which then predicted less positive affect among interaction partners. When applying the Benjamini–Hochberg procedure (Benjamini & Hochberg, 1995), this indirect effect was no longer significant, due to attenuated partner effects from state loneliness to partner positive affect.

Table 2.

APIMeM Results for Actor and Partner Mediation Models.

Model	Estimate	SE	95% BC CI
Model	Estimate	SE	Lower	Upper
Positive affect (Y)
Simple IE
X_A→M_A→Y_A	0.87	0.42	0.212	1.093
X_A→M_A→Y_P	0.38	0.29	0.013	1.237
Total Effect
X_A→M_A→Y_A	−1.84	1.55	−4.847	1.121
X_A→M_A→Y_P	−1.09	1.51	−4.918	1.955
Enjoyment (Y)
Simple IE
X_A→M_A→Y_A	0.11	0.07	0.012	0.304
X_A→M_A→Y_P	0.06	0.06	−0.018	0.216
Total Effect
X_A→M_A→Y_A	−0.01	0.33	−0.637	0.640
X_A→M_A→Y_P	0.37	0.34	−0.285	1.058
Liking (Y)
Simple IE
X_A→M_A→Y_A	0.06	0.06	−0.015	0.409
X_A→M_A→Y_P	0.07	0.05	−0.005	0.222
Total Effect
X_A→M_A→Y_A	−0.09	0.27	−0.610	0.461
X_A→M_A→Y_P	0.19	0.28	−0.331	0.761
Received RSP (Y)
Simple IE
X_A→M_A→Y_A	0.07	0.04	0.012	0.195
X_A→M_A→Y_P	0.04	0.04	−0.007	0.154
Total Effect
X_A→M_A→Y_A	0.18	0.20	−0.208	0.585
X_A→M_A→Y_P	0.13	0.22	−0.294	0.548
Provision RSP (Y)
Simple IE
X_A→M_A→Y_A	0.02	0.02	−0.005	0.072
X_A→M_A→Y_P	0.02	0.02	−0.001	0.066
Total Effect
X_A→M_A→Y_A	−0.11	0.10	−0.300	0.075
X_A→M_A→Y_P	−0.00	0.10	−0.201	0.193

Note. Boldface point estimates indicate statistical significance (CI does not include zero). APIMeM = Actor–Partner Interdependence Mediation Model; 95% BC CI = 95% bias-corrected confidence intervals with bootstrap of 5,000; IE = indirect effect; X = induction condition, M = state loneliness. a = actor variable. p = partner variable.

Exploratory Analyses

Given associations among state loneliness and perceived responsiveness, and among the perceived receipt and provision of responsiveness, we considered the possibility that loneliness would indirectly impact the provision of supportive behavior via the perceived receipt of a supportive response. This model supports previously theorized, integrated models of responsiveness, wherein the perceived receipt of responsiveness influences the provision of future supportive behavior (Clark & Lemay, 2010). As such, we conducted an additional APIMeM model, across low loneliness and high loneliness induction groups, wherein state loneliness predicted actor and partner provision of responsiveness, via actor and partner perceived responsiveness.

Results revealed significant actor effects of state loneliness on perceived responsiveness (b = −0.08, SE = 0.03, p = .011, BC 95% CI: [–0.150, –0.020]); greater state loneliness related to the perception of receiving a less supportive response to one’s capitalization attempt. In addition, there were significant actor effects of perceived responsiveness on the provision of responsiveness (b = 0.18, SE = 0.03, p = .000, BC 95% CI: [0.125, 0.238]), such that greater perceived responsiveness related to the self-reported provision of responsiveness during the interaction. Partner effects were revealed for perceived responsiveness on partner provision of responsiveness (b = 0.10, SE = 0.030, p = .001, BC 95% CI: [0.038, 0.157]), perceiving that one’s lab partner was supportive during the event-sharing interaction related positively to the lab partner’s self-reported provision of responsive behavior. In addition, there were significant actor indirect effects (a₁b₁ = –.02, SE = 0.01, BC 95% CI: [–0.030, –0.004]) for actor state loneliness on actor self-reported provision of responsiveness via actor perceived responsiveness (Table 3). There were also significant partner indirect effects (a₁b₁ = –.01, SE = 0.01, BC 95% CI: [–0.020, –0.002]) for actor loneliness on partner provision of responsiveness, via actor perceived receipt of a supportive response. No other significant effects were revealed (see Supplemental Table 3.1).

Table 3.

Exploratory APIMeM Results for Actor and Partner Mediation Models.

Model	Estimate	SE	95% BC CI
Model	Estimate	SE	Lower	Upper
Provision of responsiveness (Model 1)
Simple IE
X_A→M_A→Y_A	–0.02	0.01	–0.030	–0.004
X_A→M_A→Y_P	–0.01	0.01	–0.020	–0.002
X_A→M_P→Y_P	−0.01	0.01	−0.024	0.003
X_A→M_P→Y_A	−0.01	0.00	−0.015	0.001
Total Effect
X_A→M_A(P)→Y_A	−0.02	0.02	−0.052	0.013
X_A→M_A(P)→Y_P	−0.02	0.02	–0.057	0.008
Perceived responsiveness (Model 2)
Simple IE
X_A→M_A→Y_A	−0.02	0.02	−0.048	0.011
X_A→M_A→Y_P	−0.01	0.01	−0.029	0.005
X_A→M_P→Y_P	–0.02	0.02	−0.054	0.006
X_A→M_P→Y_A	−0.01	0.01	−0.032	0.002
Total Effect
X_A→M_A(P)→Y_A	–0.08	0.03	–0.150	–0.020
X_A→M_A(P)→Y_P	−0.05	0.04	−0.127	0.018

Note. Boldface point estimates indicate statistical significance (CI does not include zero). APIMeM = Actor–Partner Interdependence Mediation Model; 95% BC CI = 95% bias-corrected confidence intervals with bootstrap of 5,000; IE = indirect effect; Model 1: X = state loneliness, M = perceived responsiveness, Y = provision of responsiveness; Model 2: X = state loneliness, M = provision of responsiveness, Y = perceived responsiveness. a = actor variable. p = partner variable.

As perceived responsiveness and the provision of responsiveness were assessed at the same time point, it was important to test an alternative model wherein state loneliness predicted the receipt of responsiveness via self-reported provision of responsiveness. Results revealed marginally significant actor effects of loneliness on perceived responsiveness (b = −0.06, p = .07, BC 95% CI: [–0.120, 0.006]; path c′), and significant actor and partner effects for the provision of responsiveness on perceived receipt of responsiveness. Individuals reporting that they provided a more supportive response to their lab partners’ disclosure also reported receiving a more supportive response to their own disclosure (b = 0.87, p = .000, BC 95% CI: [0.615, 1.14]). In turn, greater provision of responsiveness predicted partners’ perceived receipt of responsiveness (b = 0.44, p = .002, BC 95% CI: [0.164, 0.712]). Neither actor (b = −0.02, p = .29, BC 95% CI: [–0.052, 0.013]) nor partner effects (b = −0.02, p = .15, BC 95% CI: [–0.057, 0.008]) of loneliness on the provision of responsiveness (actor and partner paths a) reached statistical significant. Thus, there were no indirect effects of state loneliness on perceived responsiveness, via the provision of responsiveness (Model 2, Table 3).

Discussion

The current investigation examined the extent to which transient or state experiences of loneliness influence individual and interaction partner perceptions within a lab-based, capitalization interaction. Previous research has described positive event disclosure, or capitalization, as a vital relationship development and maintenance process with various intrapersonal and interpersonal outcomes (e.g., positive affect, relationship satisfaction, reduced loneliness; Gable et al., 2006; Gable & Reis, 2010). Yet, previous work examining the association between capitalization and loneliness has relied on correlational methods. In utilizing experimental randomization and a loneliness mood induction, our results provide insight into the causal direction of this relationship.

We hypothesized that experimentally induced loneliness would inhibit individuals from reaping the rewards of capitalization (e.g., positive affect, enjoyment, perceived responsiveness). Results revealed that participants receiving a high loneliness (vs. low loneliness) induction reported greater loneliness, and as a result experienced less positive affect and perceived their interaction partners as less responsive, relative to individuals receiving a low loneliness induction. Associations among state loneliness, positive affect, and perceived responsiveness held, when adjusting for multiple comparisons.

Results of the current study support the previously articulated multidimensional model of loneliness (Peplau & Perlman, 1979), wherein loneliness is related to less positive affect, more negative perceptions of social interactions, and as a result, poorer quality social interactions. This is the first study of which we are aware to examine the multidimensional model of loneliness in the context of a real social interaction, using experimental methods, thus making a substantive contribution to the existing literature. Whereas previous work has typically used the multidimensional model to describe consequences of chronic loneliness for self-disclosure and social interactions, the current study provides evidence of its application to state loneliness. Furthermore, we provide preliminary insight into how state loneliness may interfere with the self-disclosure process. That is, participants in the high loneliness condition rated interaction partners as less responsive to their event disclosure and experienced less positive affect and somewhat less enjoyment following the interaction. To our knowledge, we are the first to find a causal association between state loneliness and perceived responsiveness, following an experimental manipulation of loneliness.

As noted above, results reveal similar negative social cognitions related to state loneliness, which are typically observed among the chronically lonely. It is possible, however, that the effects of state loneliness on these cognitions are more limited to immediate social interactions. Among the chronically lonely, these cognitions may inhibit future social interaction, thus contributing to the cycle of loneliness. Indeed, evolutionary models of loneliness note the adaptive role of state loneliness in motivating social reconnection (J. T. Cacioppo et al., 2014). Although state loneliness may motivate reconnection in the long term, our results show that immediate social interaction, even if in a positive context, may be consequential to lonely persons in the moment. The RAM (Qualter et al., 2015) proposes that withdrawal activates a cognitive reframing process, thus halting the cycle of loneliness. In line with this, recent research suggests that solitude may be effective in self-regulating negative affect (Nguyen, Ryan, & Deci, 2018). Although the current study did not provide a direct test of the RAM, our results provide preliminary evidence that immediate interaction following increases in loneliness may be consequential for reconnection in the long term. Future work should explore the extent to which immediate social reconnection may be an unproductive response to state loneliness in the moment, though state loneliness may motivate eventually reconnection. That is, do increases in state loneliness result in negative outcomes within disclosure interactions only in the moment, or do these effects extend over time?

In addition, future work should explore strategies used to reduce momentary feelings of loneliness, which may not include immediate social interaction. Among these are previously theorized solitary behaviors (e.g., reflection and active solitude; Rubenstein & Shaver, 1982), which are thought to redirect internal resources toward overcoming this negative affective experience (Rokach, Bacanli, & Ramberan, 2000). More recent work has considered solitary, yet inherently “social” tasks (e.g., daydreaming about close others, nostalgia) as more effective antidotes to state loneliness (Poerio, Totterdell, Emerson, & Miles, 2016). Results of the current study support this work in showing that even positive, albeit in person, social experiences may be a hindrance to individuals feeling lonely in the moment. Future work should continue to explore the extent to which solitary (vs. social) responses to state loneliness are adaptive, reduce loneliness in the moment, and motivate reconnection over time.

The current study sought to provide support for potential crossover or contagion effects of state loneliness, specifically in the context of positive social interactions. In the current study, interaction partners were not aware that their lab partner was high versus low in loneliness, thus providing a more ecologically valid assessment of partner perceptions of “lonely” others. Results provided evidence that individuals’ levels of state loneliness negatively impacted their interaction partners’ levels of positive affect following a capitalization activity. Although partner effects (direct and indirect) revealed in the current study provide important insight into potential contagion effects of state loneliness, it should be noted that they are preliminary findings. That is, these effects were initially marginally significant and were attenuated upon adjusting for multiple comparisons made in the APIMeM model. It is likely that the levels of loneliness captured in the current study are more reflective of everyday levels of loneliness, which may not rise to the level which would create a contagion effect. Yet, partner effects are typically quite difficult to detect. Furthermore, given the complexity of the APIMeM model and our sample size, ability to detect partner effects may have been limited due to low statistical power. As such, future research should seek to replicate these effects with a larger sample, and continue exploring the contagion model in relation to state loneliness via experimental and nonexperimental research.

Results of the current study also build on research and theory describing self-disclosure and perceived responsiveness as integrative, dyadic processes, wherein individual perceptions of responsiveness influence not only future disclosure but also the provision of supportive behavior (Clark & Lemay, 2010). Supplemental analyses revealed that state loneliness affected individuals’ perceptions of responsiveness and partners’ self-reported provision of support, via the perceived receipt of responsiveness during the interaction. Results of an alternative model showed that whereas state loneliness did not directly related to the provision of support, support provision predicted participant and partner perceived receipt of a supportive responsive. A key element of Clark and Lemay’s (2010) integrative model of responsiveness dynamics includes precipitating factors, which influence perceptions of responsiveness, support seeking, and support provision. Results of the current study provide preliminary support for loneliness as one factor affecting support provision in positive social interactions. As such, findings support a positivity-deficit perspective of loneliness, wherein state loneliness interferes with the ability engage in and benefit from immediate positive social interactions.

Furthermore, the integrative model of responsiveness proposes a reciprocal relationship between the perceived receipt and provision of support within disclosure interactions (Clark & Lemay, 2010). Whereas testing this reciprocal relationship was not within the scope of this study, results are in line with previously established models of responsiveness dynamics. Future research should continue to explore associations among state loneliness, and the perceived receipt and provision of responsiveness within capitalization interactions, with the consideration of intimate couples, for whom capitalization may be particularly important to the extent that it builds intimacy and relationship quality.

Finally, our findings build on previous research demonstrating unique patterns of self-disclosure related to loneliness (e.g., Rotenberg, 1997). In particular, we extend existing loneliness research, which has shown that supportive capitalization interactions relate to lower levels of lonely mood (Gable & Reis, 2010). Our results provide evidence that loneliness experienced prior to or during a capitalization interaction interferes with supportive capitalization. In the current study, state loneliness related to negative social perceptions (i.e., less perceived responsiveness), which then translated into less supportive behavior. To the extent that perceived responsiveness and self-disclosure are building blocks of close relationships, this is problematic. Research should continue to consider how state loneliness affects expectations of responsiveness (and the desire to share) in future disclosure interactions.

Limitations

Despite the substantive contributions made by this study, there are several methodological limitations of note, including the use of stranger–participant dyads. In the context of daily life, it is likely that responses to capitalization attempts would be interpreted in the historical context of the relationship (Reis et al., 2010), which would function as an “interpretative filter” for perceptions and experiences during the disclosure interaction (Reis & Shaver, 1988, p. 378). Although capitalization interactions most likely occur within established relationships (intimate or platonic), previous studies have utilized stranger dyads to investigate capitalization and self-disclosure (e.g., Dindia, Fitzpatrick, & Kenny, 1997). In the current study, participants were randomly paired into interaction dyads, due to methodological concerns that a preestablished relationship among participants might buffer induction effects. This allowed for an unadulterated examination of how transient loneliness interferes with positive social interactions, in the absence of preexisting relationship perceptions.

A central focus of this study was to examine whether loneliness and related social and perpetual deficits interfere with relationship development strategies. That capitalization and self-disclosure more generally are key in the development of close relationships and intimacy made examining the effects of loneliness on initial disclosure interactions an important first step. Future work should examine the role of transient loneliness within positive event-disclosure interactions among established relationships, which may reveal unique effects of loneliness on perceived responsiveness in the moment, relative to preexisting expectations of responsiveness in the relationship.

An additional limitation to the generalizability of our results is the assigned order of sharing in the disclosure interaction. In the current study, we experimentally controlled for order of sharing by assigning which participant would share first in the interaction; lonely participants were always asked to share first. Previous research on chronic loneliness has demonstrated that chronically lonely persons report greater discomfort during self-disclosure interactions (e.g., Solano et al., 1982). It is thus possible “lonely” participants in the current study would have been less likely to volunteer to disclose first. As we were interested primarily in the effects of state loneliness on responsiveness and interpersonal evaluations assessed at the end of the study, we deemed it necessary to experimentally control for order of sharing. Had “high loneliness” participants shared second in the interaction, their levels of loneliness and thus perceived responsiveness may have been impacted by the positivity of their partners’ disclosure.

A limitation potentially affecting generalizability of our results is the focus on female dyads. As this was the first study to investigate the effects of state loneliness within positive event-disclosure interactions, it was important to use same-gender dyads to control for gender differences in related processes (e.g., self-disclosure behavior, interpersonal attraction, Dindia & Allen, 1992; self-labeling measures of loneliness, Borys & Perlman, 1985). Although there were no gender differences in induction effects (see Supplemental Materials), it is possible that the processes assessed in the current study differ for men and women. Indeed, existing studies of chronic loneliness and self-disclosure have compared same and opposite gender interactions, providing evidence that lonely persons exhibit variable disclosure behavior when sharing with a same versus opposite gender partner (e.g., Solano et al., 1982).

Additional consideration should be given to the generalizability of our findings to individuals within other cultures, for whom self-disclosure may have unique effects on relationship development and intimacy. Of note is evidence suggesting that individuals in non-Western cultures differ in the content and amount of self-disclosure (Chen, 1995; Schug, Yuki, & Maddux, 2010), relative to persons from Western cultures. Additional work has revealed cross-cultural differences in antecedents of and responses to loneliness (Lykes & Kemmelmeier, 2014; Rokach et al., 2000). As such, our findings may be less generalizable to the experiences of lonely persons within non-Western cultures. However, our sample was slightly more diverse than typical undergraduate student populations, in terms of age and diverse life experiences (e.g., 59% employed), and thus more generalizable to the general population. Future work should investigate the influence of state loneliness within capitalization interactions among male dyads, opposite gender dyads, and among individuals in other cultures.

One additional limitation to the generalizability of our results relates to low-to-moderate levels of state loneliness reported in the current study. Although the mood induction was effective in manipulating loneliness, levels of loneliness were below the scale midpoint within high and low loneliness conditions. Thus, results may be more generalizable to mild levels of loneliness, and thus more representative of everyday levels of transient loneliness. Furthermore, in screening for history of depression, we may have eliminated participants at risk of experiencing higher levels of loneliness. However, that our results are in line with previous theories of loneliness, despite mild levels, points toward the possibility that state loneliness predicts similar cognitive consequences as chronic loneliness. These cognitions may serve to make social interactions less enjoyable in the moment. However, the extent to which these cognitions inhibit versus facilitate future reconnection is an area of work yet to be explored. Research should continue to explore similarities and differences in processes related to chronic and state loneliness.

Finally, future work should consider the possibility that the low (vs. high) loneliness manipulation is driving results reported here. However, research in the more general study of emotions and social interactions reveals that negative (vs. positive) stimuli and social information typically exert the strongest effects on cognitions and behavior, over a range of psychological phenomena (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001; Pratto & John, 1991). As such, we suspect that it is indeed high loneliness driving differences in postinteraction positive affect and perceived responsiveness, as opposed to low loneliness or perceived belongingness. Future work should consider replicating our results, to provide further support for the effects of negative loneliness stimuli versus more positive social stimuli, on the social perceptions and behaviors examined in the current study.

Conclusion

The current study investigated the effects of transient loneliness on perceptions and behavior within a lab-based capitalization interaction. By employing an experimental dyadic design, this study offers insight into the consequences of transient loneliness in the early stages of relationship development. Results support existing multidimensional models of loneliness and expand the current understanding of loneliness to include deficits within positive relationship processes, which may make it difficult to overcome loneliness in the moment. Furthermore, results provide evidence of causal links among state loneliness and social-perceptual deficits. Finally, results provide a foundation from which future work can continue to explore the intrapersonal and interpersonal consequences of transient loneliness.

Supplemental Material

arpin_online_appendix – Supplemental material for Transient Loneliness and the Perceived Provision and Receipt of Capitalization Support Within Event-Disclosure Interactions

Supplemental material, arpin_online_appendix for Transient Loneliness and the Perceived Provision and Receipt of Capitalization Support Within Event-Disclosure Interactions by Sarah N. Arpin and Cynthia D. Mohr in Personality and Social Psychology Bulletin

Supplemental Material

Supplemental_Materials_1_Pilot,_Tables,_Figure – Supplemental material for Transient Loneliness and the Perceived Provision and Receipt of Capitalization Support Within Event-Disclosure Interactions

Supplemental material, Supplemental_Materials_1_Pilot,_Tables,_Figure for Transient Loneliness and the Perceived Provision and Receipt of Capitalization Support Within Event-Disclosure Interactions by Sarah N. Arpin and Cynthia D. Mohr in Personality and Social Psychology Bulletin

Footnotes

Acknowledgements

We gratefully acknowledge David A. Kenny, Kimberly Kahn, Todd Bodner, and Jeffrey Robinson for their helpful comments on early versions of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was based, in part, on the first author’s doctoral dissertation, which was funded by the APA Dissertation Research Award.

Notes

Supplemental Material

Supplementary material is available online with this article.

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