How much we express love predicts how much we feel loved in daily life

doi:10.21203/rs.3.rs-5010125/v1

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How much we express love predicts how much we feel loved in daily life

https://doi.org/10.21203/rs.3.rs-5010125/v1

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Feeling and expressing love in daily life are interconnected and perhaps mutually influential experiences. In this study we examined the reciprocal dynamics of feeling and expressing love and its relation to well-being using an ecological momentary assessment design. Over a four-week period, we asked participants (N = 52; 67% Female; 80% White) to report their levels of feeling loved and expressing love six times a day. Using a continuous-time process model, we explored individual differences in inertia (i.e. persistence of a process over time) and cross-influences of felt and expressed love over time. We found that increases in expressing love led to increased feelings of being loved over time; however, increases in felt love did not lead to increases in expressing love. Notably, participants with greater inertia in their felt love levels reported higher levels of overall flourishing. These results suggest new avenues for psychological well-being interventions which target increasing loving feelings through encouraging more expressions of love.

Biological sciences/Psychology

Biological sciences/Psychology/Human behaviour

love dynamics

felt and expressed love

Bayesian multilevel modeling

well-being

flourishing

In the scientific literature, love has been conceptualized as a temporally dynamic burst of emotion shared between individuals which has been elevated to the status of a “supreme emotion” (Fredrickson, 2016; Zhou et al., 2022). The theory of “positivity resonance” (Fredrickson, 2016; Major et al., 2018) posits that everyday love emerges from small acts or gestures that foster positive connections between individuals. This line of research situates love within everyday interactions, including but extending it beyond romantic contexts. Experiences of “love in everyday life” (LEL) has been linked with psychological well-being and improved sleep quality (Dickens et al., 2021; Major et al., 2018; Oravecz et al., 2020; Prinzing et al., 2023; Zhou et al., 2022). However, the daily dynamics between feeling and expressing love and their implications for well-being are largely unexplored.

Consistent with research on love and loving-kindness (e.g. Condon & Makransky, 2020; Kahana et al., 2021), we distinguish between two “dimensions” of LEL: a felt aspect (love from another), and an extending aspect (love towards another). We label the felt dimension of love as “feeling loved” or “felt love,” and the extending dimension of love as “expressing love” or “expressed love.” Research has explored giving and receiving communal support and responsiveness within the context of existing relationships (e.g. Clark & Monin, 2006; Lemay et al., 2007). Lemay et al. (2007) described the link between an individual’s perception of support from their partner and their own extended responsiveness. In this context, the authors explored the mechanism through which extended and perceived support are related, with individuals projecting their own support onto their perceptions of their partner (Lemay et al., 2007). While this study is contextualized to support within a romantic partnership, and noted to be limited to a dyadic interaction (Lemay et al., 2007), this mechanism can be extended to the context of LEL, suggesting a possible linkage between expressing love and feeling loved. Consistent with research on the cultivation of another form of love, compassion (e.g. Roeser et al., 2018), it is possible that felt and expressed love dynamically influence each other, creating a positive feedback loop where an increase in expressed love yields an increase in felt love over time. Our study aims to capture the dynamics of these two aspects of LEL as they unfold over time.

Our conceptual model for love dynamics is similar to that of affect dynamics (Kuppens et al., 2010). We assumed that the two dimensions of love – felt and expressed – can be described in terms of their baseline levels, intra-individual (stochastic) variability, and inertia (e.g., time to return to baseline following changes in the system or auto-correlation), and most importantly, their possible asymmetric influence on each other (cross-influences) over time. Specifically, we used a continuous-time stochastic process model to capture the most important dynamical features and interactions of feeling and expressing love over time.

In this study, we focused on examining love dynamics in terms of inertias and cross-influences. Inertia describes how a process persists over time, as opposed to being regulated back to its baseline (Krone et al., 2018), and its level describes the persistence of momentary states over time. Higher inertia means that the level of loving feelings change more slowly, while lower inertia allows for quick regulation back to baseline. Each person can be described with their own inertia estimates of felt and expressed love, and these two can differ from each other. In addition, cross-influences (i.e., cross-effects) capture the reciprocal relationship between felt and expressed love (and vice versa) over time. The magnitudes of these cross-influences express how likely it is that changes in felt love/expressed love are followed by similar changes in the expressed love/felt love over time. These cross-influences are also individual-specific and can be asymmetrical. Therefore, we captured how likely it was that changes in the intensity of feeling loved were followed by changes in the intensity of expressing love, as well as the opposite, for every individual. We also derived group-level estimates for these inertias and the cross-influences via multilevel modeling.

To explore the links between sources of individual differences in love dynamics and psychological well-being, we selected relevant trait-level measures, such as general happiness, emotional well-being, and flourishing. While these three scales all explore dimensions of mental health and happiness, they each provide a unique aspect which may be informative in the context of LEL. General happiness provides comparative information (Lyubomirsky & Lepper, 1999), while emotional well-being captures mental health more wholly (Ware & Sherbourne, 1992). The flourishing scale includes additional dimensions regarding relationships, meaning, and general attitude toward the future (Diener et al., 2010). In fact, Major et al. (2018) found a link between flourishing mental health and positivity resonance. In relation to love dynamics in everyday life, each scale provides a component which may uniquely influence felt and expressed love. Gender was also included in the analysis as prior studies suggested possible gender differences (Prinzing et al., 2022).

Recruitment and Participants

We recruited 52 adults for a four-week long study from a northeastern college town. Of the participants, approximately 80% indicated White or Caucasian, 10% indicated Asian or Pacific Islander, 6% indicated Hispanic or Latino, and 4% indicated Black or African American. For gender, 67% indicated female and 33% indicated male. The participants ranged from 19 to 65 years of age (M = 30.17, SD = 10.14). No participants were excluded from the analysis. All participants provided written informed consent. The study was approved by Pennsylvania State University’s Institutional Review Board (STUDY1017) and was performed in accordance with relevant guidelines and regulations. The study design and analyses were not preregistered.

Procedure

Data analyzed here was part of a larger experience sampling study that explored people’s psychological well-being. We used a subset of the variables from this parent study needed to answer our research questions. Full details on the parent study with all measures are available via https://osf.io/7hvce/; relevant details are provided below.

Repeated assessments of love experiences were collected via the participants own smartphones in daily life context. Participants responded to surveys at random times (maximum of 6 times a day) during their regular waking hours. Compliance was high, with an average number of 157 (SD = 15) responses per participant throughout. In terms of statistical power, quantifying individual-specific dynamics needed approximately 60 observations per person (see, e.g., Oravecz et al., 2011) for precise estimates. For testing for correlations between trait-level measures (e.g., emotional well-being scores) and individual-specific love dynamics, a minimum of 46 people for detecting medium sized (r = .4) effects is needed which is exceeded with a sample size of N = 52. The trait-level variables were measured at the beginning of the study during an in-person session.

Measures

Felt love was measured repeatedly with the item “How much do you feel loved right now?” and expressed love was measured by the item “Since the last survey, I have been expressing love.” Individuals responded on a sliding scale from 0 to 100. It should be noted that responses to each item were not necessarily in relation to the same loving connection. Also, previous work by Heshmati et al. (2019) and Dickens et al. (2021) has shown that the layperson’s understanding of loving scenarios in everyday life do exist outside of the context of romantic relationships. Based on these findings in the context of this study, it is reasonable to assert that the responses provided by the participants are largely in alignment with the cultural consensus of love occurring in everyday life scenarios.

Gender was coded as a binary variable (Male = 0, Female = 1). General happiness was measured using the Subjective Happiness Scale (Lyubomirsky & Lepper, 1999), consisting of four statements with response values ranging from 1 to 7; the mean score was used for the analysis. The Flourishing Scale (Diener et al., 2010) was used to measure flourishing on a scale of 1 to 7 for eight items; the sum score was used for the analysis. Emotional well-being was measured using the Short-Form Health Survey (Ware & Sherbourne, 1992) and consisted of four questions with values ranging 1 to 6; the mean value was used for the analysis. Descriptive statistics, including the mean and standard deviations of the trait level measures, are shown in Table 1.

Table 1

*Descriptive statistics of the variables in the study.*
Variable	N	%	Mean	SD
Gender: Male	17	33
Gender: Female	35	67
General Happiness			5.22	1.43
Flourishing			45.44	8.85
Emotional Well-being			4.72	0.85
Felt Love			68.44	15.23
Expressed Love			64.62	17.63
Note. Number of participants and corresponding percentages are shown for the Gender variable. For the continuously scaled variables, the Mean column shows the average across individuals, the Standard Deviation (SD) column displays the corresponding between-person variability for each variable. For the EMA measures of felt and expressed love, the within-person mean was calculated for each person – the mean and standard deviation of these are displayed.

Data Analysis

We captured the time dynamics of love with a continuous-time stochastic model in which we allowed individuals to differ in their dynamical characteristics. While previous studies captured positivity resonance in discrete time (Zhou et al., 2022), a continuous-time stochastic model allowed us to capture dynamical features as an evolving system in high-intensity, irregularly-spaced ecological momentary assessment (EMA) data (Driver et al., 2017). Several studies have used a similar continuous-time stochastic process model to account for dynamical changes in core affect (Kuppens et al., 2010; Oravecz et al., 2009) and well-being (Marciano et al., 2022) in daily life.

We measured participant p’s felt and expressed love levels at time t via self-reports, denoted by the bivariate vector y_p(t). We assumed that these measures were perturbed with measurement noise, and that love dynamics evolved on a latent level, and specified our measurement model as y_p(t) = η_p(t) + τ_p + ϵ_p(t), with the 2-by-1 vector η_p(t) indicating latent levels of felt and expressed love, a 2-by-1 vector τ_p capturing the manifest intercept, and the 2-by-1 vector ϵ_p(t) quantifying the manifest residuals (see more details in Driver & Voelkle, 2018). Changes over time in the latent process, dη_p(t), were modeled through a bivariate stochastic differential equation model, specified as:

d η _p(t) = (A_pη_p(t) + b_p)dt + Σ_pdW(t),

where A_p is the person-specific 2-by-2 drift matrix, b_p is the person-specific continuous-time intercept vector, Σ_p is the person-specific 2-by-2 diffusion coefficient matrix, and W is a Brownian motion process. This model captures the continuous-time dynamics through the drift matrix, A_p, which is composed of the auto-correlations (inertia) on its diagonal and cross effects for felt and expressed love on its off-diagonal. In other words, it was this drift matrix that captured how felt and expressed love were linked to their respective past values as well as the cross-influences over time. All dynamic features (i.e., inertia and cross effects) were allowed to differ across individuals – that is each individual’s felt and expressed love dynamics were modeled as their own evolving system. To transform the diagonals of the drift matrix into inertia, a matrix exponential was applied. We fit this continuous-time stochastic model in a multilevel Bayesian framework to provide an intuitive understanding of how the love dynamics evolve both at the individual and group level.

For data analysis, we used the R (R Core Team, 2021) package ctsem (Driver & Voelkle, 2018). We ran two chains with 3,000 iterations, with default, weakly informative priors distributions. Additionally, most parameters estimated varied by person and initial time data points were not included as the expressed love question refers to the previous survey which would not be applicable for the initial data point. Data points which occurred less than one minute after a previous data point were excluded.

Individual-specific estimates of these love dynamics were tested for associations of the following trait level variables: general happiness, gender, emotional well-being, and flourishing, to explain the link between relatively stable person characteristics and love dynamics. We used JASP (JASP Team, 2023) to calculate correlations and corresponding Bayes factors. We chose the Bayes factor as it allows for quantification of evidence in the data in a statistically principled manner for any effect size (without requiring the binary decision of significant vs. non-significant). Data and analysis scripts used for this study are available on the Open Science Framework website at the following link: https://osf.io/gfsxw/.

The group-level estimates of key dynamical parameters (i.e., elements of the drift matrix) and their 95% credible intervals are shown in Table 2. The credible interval is interpreted as the range of numbers containing the true parameter with 95% probability (given the data and our model specifications; Hespanhol et al., 2019). Based on these estimates, we illustrated love dynamics over time (in hours) in Fig. 1 using the R (R Core Team, 2021) packages ctsem (Driver & Voelkle, 2018) and ggplot (Wickham, 2016). The curves are based on the mean estimates in Table 2, shaded areas display the 95% credible intervals. Figure 1 shows group-level love dynamics over time in terms of inertias and cross-influences of felt and expressed love.

Table 2

*Group-level love dynamics estimates.*
Time dynamics features	Mean	95% CI
felt love inertia	-0.065	[-0.130, -0.014]
expressed love inertia	-0.900	[-1.530, -0.512]
expressed to felt love cross-influence	0.820	[ 0.358, 1.578]
felt to expressed love cross-influence	-0.038	[-0.088, -0.005]
Note. Group-level mean estimates of the time dynamics features of love are shown in the second column with their corresponding 95% credible intervals in the third column.

The 95% credible intervals of the group-level drift matrix elements quantifying felt and expressed love inertias (b = -0.065, CI = [-0.130, -0.014], and b = -0.900, CI = [-1.530, -0.512]) did not contain 0, indicating these were credibly different from 0. In Fig. 1, the felt and expressed love inertias over time are shown in green and red, respectively. By looking at the time axis, we can see for how long the impact of the loving feelings persisted. We can also see a difference between the felt and expressed love dynamics: on average, felt love decayed at a slower rate than the expressed love, suggesting a stronger lingering effect of felt love than expressing love. The decay of inertia over time was consistent with literature on emotion processes (Schneider et al., 2020), providing further support for studying love within the context of an emotion.

Figure 1 also illustrates the cross-influences from felt to expressed love and vice versa, corresponding to the estimates of the last two lines in Table 2 (b = 0.820, CI = [0.358, 1.578] and b = -0.038, CI = [-0.088, -0.005]). Neither of the credible intervals contained 0, indicating that extending and felt love dynamics were credibly linked to each other across time. The cross-influence for expressed love to felt love (shown in orange) remained positive throughout the interval shown on the graph. Conceptually, this means that more intensely participants expressed love, the stronger they were feeling loved, with this dynamic peaking around three hours. Felt love to expressed love dynamics showed a different pattern. We found that increases in feeling loved were followed by decreases in expressing love – as shown by the blue curve in Fig. 1 – descending below 0. However, this effect was very small, with limits of the 95% credible interval approaching 0 after a couple of hours. This finding suggested that when people felt loved, it was unlikely that they would be expressing love – however, as stated, these dynamics were not very pronounced (i.e., negligible effect size).

To capture individual differences in felt and expressed love dynamics, dynamical features were allowed to differ across individuals, and we studied their links with trait-level variables. Results are shown in Table 3. We included Pearson’s correlation values (first line in each cell) with the evidence that the correlation is different from 0, summarized by the Bayes factor (B, second line in squared brackets). The higher B, the more evidence there was that a correlation differed from 0. Generally, a value between 1 and 3 indicates anecdotal evidence of a non-zero correlation, a value between 3 and 10 indicates substantial evidence for it, while anything above 10 indicates strong evidence (Wetzels & Wagenmakers, 2012).

Table 3

*Correlations and corresponding Bayes Factors for the love dynamics individual-level correlation estimates with the trait level variables.*
Time dynamics features	Gender	General Happiness	Flourishing	Emotional Well-being
felt love inertia	-0.060 [0.189]	0.262 [0.956]	0.411* [14.778]	0.192 [0.426]
expressed love inertia	-0.176 [0.369]	-0.051 [0.184]	-0.060 [0.189]	-0.110 [0.231]
expressed to felt love effect	0.313 [2.084]	0.303 [1.769]	0.194 [0.435]	0.203 [0.477]
felt to expressed love effect	0.148 [0.294]	0.329 [2.714]	0.262 [0.960]	0.187 [0.407]
Note. Asterisk indicates a Bayes Factor larger than 10.

There was strong evidence for positive correlation between flourishing and felt love inertia (r = 0.411, \(\:{B}_{10}\) = 14.778). Higher levels of flourishing were linked to slower changes in feeling loved (higher inertia), suggesting people with higher levels of flourishing would have longer-lasting loving feelings. Aside from this correlation, we found no additional evidence for linkages between the inertias and our selected trait level measures. With respect to the cross-influences, there was some weak evidence for an association between general happiness and the felt love to expressed love dynamic (r = 0.329, \(\:{B}_{10}\)= 2.714), suggesting people who tended to be happier also expressed more love when their own feelings of love increased.

We conceptualized feeling and expressing love in daily life as a dynamical system unfolding over time and estimated its properties with data from an EMA study. The cross-influences of expressed and felt love were credibly linked, indicating that increases in expressed love levels were followed by increases in felt love levels over time, peaking at three hours after the expression of love and decaying after this point. This suggests that expressing love is a potential route to increasing felt love. Interestingly, the cross-influence estimates for felt and expressed love was in the opposite direction, meaning that as felt love was increasing, expressed love was decreasing. However, this effect was very small. Although the context of love for this study was broader, these findings are similar to the Lemay et al. (2007) findings which suggested that an individual projects their own extended support on their perception of their perceived support from their partner. In the context of this study which measures love as it occurs in daily life, across any and all relationship contexts, this could be compared with the cross-influence of expressed love to felt love and understood to be a similar mechanism.

Felt love inertia was consistently higher than expressed love inertia over time. This suggests that felt love tends to endure longer compared to expressed love, as expressed love is likely to revert to a baseline level more rapidly than felt love. Taken this together with the finding on the cross-influence, it is possible that individuals who express love, and feel a sense of being loved as a result, will experience the benefit of feeling loved for longer. This is important because we also found strong evidence for the link between flourishing and felt love inertia. This correlation indicated individuals with more flourishing experienced more resistance to decay in their loving feelings.

These findings cast LEL as a dynamic flow of felt and expressed love which unfolds over time. Most crucially, the expressed to felt love cross-influence provided evidence of love dynamics influencing one another. This cross-influence indicated expressed love could be a potential route to increase felt love. We propose that LEL could be conceptualized as a skill – LEL can be noticed and expressed more through practice and awareness in daily life (Hofmann et al., 2011). Additionally, while felt love is more resistant to decay over time, the cross-influence from felt love to expressed love was found to be a smaller, negligable effect, when compared to the cross-influence from expressed love to felt love. These results may indicate that the lasting effect of felt love could be interfering with expressing love, explaining why felt love was not highly linked to expressed love over time. Conceptually, these results align with the idea of self-cherishing possibly impeding an individual’s expressed love. If an individual is self-cherishing and preoccupied with their own felt love, this could stall additional acts of expressed love (Hopkins, 2001; Miller, 2016).

Future directions include interventions aimed at increasing an individual’s expressed love or flourishing as these mechanisms could also increase felt love for the individual (Dahl et al., 2020). They also may test if strategies for the maintenance of loving feelings (i.e., by encouraging the expression of loving behaviors) would lead to more flourishing (e.g. Dahl et al., 2020; Roeser et al., 2018).

Constraints on Generality

Constraints include the predominantly White or Caucasian sample from northeastern US. Future research avenues include additional exploration for other racial and ethnic groups and regions.

This research contributes to the body of literature which studies love as an emotional process with dynamic changes over time. The decay that is observed across time for both felt and expressed love inertia further supports the contextualization of love as an emotion. Additionally, we found a link between expressed and felt love levels over time. Considering these felt and expressed love dynamics complemented with techniques for cultivating LEL through reflection and practice (Condon & Makransky, 2020) can facilitate the development of psychological well-being interventions. Importantly, this research unlocks the opportunity for interventions to target increasing expressed love to increase an individual’s feelings of love over time.

Support for this work was provided by grant #48192 from the John Templeton Foundation and the Edna Bennett Pierce Endowed Chair in Caring and Compassion at Penn State. Data, and code are shared publicly on an Open Science Framework (OSF) page. OSF Repository: https://osf.io/gfsxw/.

Data Availibility Statement

The data that support the findings of this study are openly available in OSF at https://osf.io/gfsxw.

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No competing interests reported.

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How much we express love predicts how much we feel loved in daily life

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Abstract

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Recruitment and Participants

Procedure

Measures

Data Analysis

Results

Discussion

Constraints on Generality

Conclusion

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References

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