The Effects of a Brief Promoting Positive Emotions Intervention and Depression Symptoms on Reward Responsiveness in Young Adults
Emma Boldwyn Dr. Autumn Kujawa PSY-PC 4999 Honors Thesis
Spring 2023
Abstract
Research has shown that depression treatments focusing on increasing positive affect can be beneficial and promote reward responsiveness. The purpose of this study was to test how a single-session intervention to promote positive emotions and pre-existing depression symptoms impact neural measures of monetary and social reward responsiveness in young adults.
Participants (N=44) first completed a depression symptom questionnaire. Then, they were randomly assigned to a brief promoting positive emotions intervention group (BPPE) or a study skills control group. Following the intervention, participants participated in a series of computer tasks while electroencephalogram (EEG) was recorded to measure the reward positivity (RewP), an event-related potential conceptualized to measure reward responsiveness. It was hypothesized that depression would be associated with blunted RewP, and that participants in the BPPE group with higher depression symptoms would display greater RewP than those in the control condition with similar depression symptoms. It was also hypothesized that there would be an interaction effect between intervention and depression symptoms, Finally, it was hypothesized that participants who reported greater well-being would display greater monetary and social RewP regardless of group assignment. Results for all models were non-significant; however, effect size estimates provide preliminary evidence that for individuals with higher depression symptoms, BPPE may improve reward responsiveness in the social reward domain. Extension to larger samples is needed to further investigate how reward responsiveness varies in young adults and the effectiveness of single-session interventions focused on positive emotions.
Introduction
Major depressive disorder (MDD) is among the most prevalent psychological disorders in the United States, impacting roughly 1 in 10 adults (10.4%). Recent research has found rates of depression to be even higher in the young adult (age 18-25 years) population, with the National Institute of Mental Health (NIMH) estimating that between 11% to 17% of young adults have experienced a depressive episode in the past year (Hasin et al., 2018). Young adulthood is a transitional period filled with life-altering events such as beginning a career, moving away from home, and fostering new relationships. Many forms of psychopathology are known to develop during this time, including MDD, and earlier onset age is associated with poorer mental health and well-being outcomes later in life (Smith & Blackwood, 2004).
The presence of MDD and subthreshold depression symptoms in adolescence and young adulthood is associated with an increased risk for later MDD in adulthood, which further
necessitates improved understanding of the disorder in the younger population (Klein et al., 2009). Depression can have debilitating symptoms, including a marked loss of interest and pleasure, feelings of worthlessness, reduced ability to think or concentrate, and increased risk of suicide (American Psychiatric Association, 2013). A large body of previous research has
emphasized the importance of understanding the combined effects of multiple influences, such as biological and psychosocial factors, in the treatment of depression (Birmaher et al., 1996).
Increased understanding of the relation between individual neural differences and treatment responses could lead to improved outcomes for individuals suffering from the disorder. This project examines the use of electroencephalogram (EEG), specifically event-related potentials (ERPs), to measure neural responses to reward following a brief intervention focused on promoting positive emotions in college students.
Event-Related Potentials
ERPs are extracted from continuously recorded EEG and are small, brief voltage changes in the brain following a specific behavior or stimulus presentation (Sur & Sinha, 2009). ERPs record brain activity with millisecond temporal resolution preceding, during, and following interaction with task stimuli (Kropotov, 2010). Over the course of a task, the amplitude, latency, and topography of the ERPs are averaged across trials to obtain a general shape or pattern of the brain activity around the event of interest (Bradley & Keil, 2012). Compared to other neural measures, the strength of EEG is the ability to measure brain activity on the order of
milliseconds. Because of this, researchers can extract brain activity around a stimulus and make connections between specific patterns in ERPs and psychological disorders, like depression.
In an effort to establish a consistent framework for researching psychopathology
dimensionally, the National Institute of Mental Health developed the Research Domain Criteria (RDoC). One of the domains of the RDoC is Positive Valence Systems (PVS), which are
systems in the brain responsible for reactions to positive or motivating situations, including reward responsiveness (Morris & Cuthbert, 2022). Reward responsiveness is one’s ability to experience pleasure in the anticipation and presence of reward-related stimuli (Taubitz et al., 2015). Altered reward responsiveness has been reliably linked to mental health problems including anhedonia (lack of interest/pleasure), anxiety, depression, and other internalizing disorders (Henriques & Davidson, 2000; Klawohn et al. 2021). Reward responsiveness can be measured in anticipation to, immediately after, or following mental processing of the rewarding stimuli. An ERP measure of reward responsiveness immediately after receipt of a reward is the reward positivity (RewP), which is measured approximately 250-350 milliseconds after a reward stimulus. Reward stimuli can be monetary, including the gain or loss of money or valuable items.
The RewP has also been observed in social contexts, such as in response to interpersonal approval stimuli or peer ratings. Individuals with depressionexhibit a reduced response to rewards compared to individuals with no disorders, as demonstrated by differences in RewP (Bress et al., 2015). Additionally, research has found that children and adolescents with a blunted RewP and no previous depression symptoms are more likely to develop depression in the future (Burani et al., 2021). The RewP as a measurement tool is advantageous in that it is an efficient, cost-effective measure that is increasingly demonstrating the capacity to highlight individual differences in reward responsiveness in relation to depressive symptomatology. As use of this neural measure is becoming more common, it is imperative to understand how different types of rewards relate to depression.
Monetary Reward Responsiveness and Depression
Initial studies on the RewP and depression were primarily focused on feedback to
monetary gains and losses. One procedure that has been used to study this is called the Monetary Incentive Delay (MID) Task, in which the participant is asked to click the mouse as quickly as possible once a white box appears on the screen. They are told that if they click the mouse while the box is still onscreen, they will win a small sum of money. If they respond too slowly, they will lose a slightly smaller sum of money. While participants complete the task, continuous EEG is recorded, and the RewP to monetary gain and loss trials is then averaged across trials and a difference score (commonly denoted as monetary ΔRewP) is calculated isolating neural responses to rewards versus non-rewards. Research has found individuals with more blunted monetary ΔRewP have more numerous and severe depression symptoms (Henriques &
Davidson, 2000; Lutz & Widmer, 2014; Ait Oumeziane et al., 2019). While research using the monetary RewP has elucidated preliminary associations between depression and reduced reward
responsiveness, there is also growing interest in different types of rewards and how they may relate to depression differently across populations.
Social Reward Responsiveness and Depression
While research indicates that there could be key differences in how adolescents and adults respond to social feedback, little information is available pertaining to young adults (Sowell et al., 1999). In a meta-analysis of literature on neural response to social reward across ages, Foulkes & Blakemore (2016) found that compared to adults, adolescents place more value on socially rewarding stimuli. When it comes to young adults, however, it is unclear whether they respond more similarly to adolescents or older adults. In some studies, the term ‘adolescent’
includes ages up to 23, and in others, ‘adult’ encompasses all individuals over 16 (Galvan, 2010).
Many young adults are still acquiring social skills and learning about relationships, and for college students in particular, the majority of social interactions are with peers of similar ages (Steinberg et al., 2008). It is possible that young adults could value social reward as much as adolescents do, particularly because the college environment that many young adults live in necessitates having stable friendships and peer relationships (Steinberg, 2004).
Because far fewer studies have focused on social reward responsiveness compared to monetary reward, tasks for measuring social reward processing are less well established. In 2014, Kujawa and colleagues developed a procedure for measuring social ΔRewP called the ‘Island Getaway Task’. In this task, participants are told that they are going to be playing an online game based off the reality television show Survivor with peers who are playing in different locations.
The goal of the game is to avoid being voted out by other fake players and make it to the
‘deserted island’. During the game, participants view random players’ profiles and vote to keep them in the game or vote them out; afterwards, they learn how other players voted on them. It is
the participant’s response to this social feedback that is of particular interest neurologically. The pilot study of this task analyzed the difference score between positive and negative feedback neural responses occurring around 250-350 milliseconds after receiving negative feedback (commonly denoted as social ΔRewP). Results demonstrated that adolescents who had more depression symptoms were more sensitive to receiving negative compared to positive feedback from other players in the game, with a correspondingly more blunted social ΔRewP (Kujawa et al., 2014). The findings from this study reveal that there are significant neural differences in adolescents with versus without depression symptoms in terms of social ΔRewP.
In studies comparing ERP data between social and monetary reward tasks, the RewP is often correlated across tasks and patterns are often similar with other variables of interest (Distefano et al., 2018; Rademacher et al., 2010). A next step, then, is to consider factors that impact social versus monetary RewP to gain a better understanding of how both indices relate to reward responsiveness. As this research area expands, it is important to consider the implications it could have on depression treatment.
Reward Responsiveness and Response to Treatment
Psychotherapy is the most common non-pharmacological method to treat depression, and there are numerous theoretical approaches to conducting it (Mayo Clinic, 2018). One empirically supported approach is cognitive-behavior therapy (CBT), which focuses on teaching individuals to challenge negative thoughts and reroute negative behavior cycles (Charmaine et al., 2016).
CBT is widely considered to be one of the most effective long-term treatments of depression.
However, recent research on positive affect is increasingly supporting the importance of promoting resilience, mindfulness, and psychological well-being in therapeutic treatment.
(Werner-Seidler, 2013), processes that are not currently targeted in CBT. There are different
theories as to why promoting positive affect skills is associated with improved long-term
treatment outcomes. First, researchers have found that positive emotions helps downregulate the physiological and psychological effects of negative emotions (Tugade & Frederickson, 2004).
Additionally, intact PVS functions, such as reward anticipation and satiation, have been shown to assist individuals in engaging in behaviors that have potentially rewarding outcomes, such as gaining social or intellectual resources, that in turn promote resilience and overall well-being (Fredrickson, 2013).
Craske and colleagues (2019) created a positive affect-based depression treatment that taught participants skills such as recalling past positive experiences, attending to positive events, seeking out pleasant activities in the future, and visualizing future positive experiences.
Compared to a treatment that focused on reducing negative affect, subjects in the positive affect treatment exhibited lower symptoms of depression, anxiety, and stress at the end of treatment.
While results of this study show promise for the future of positive affect-based treatment, no research has been conducted on the neural responses to such an intervention. Because interventions that focus on positive emotionality also aim to increase reward sensitivity, it is imperative to understand brain responses to reward stimuli after positive affect-based treatment is conducted. Neural indices of reward have the potential to improve knowledge of how
depression impacts not only the brain, but how individuals perceive and respond to the world around them. In understanding neural outcomes of positive affect treatments, clinical treatment can be improved to allow individuals the opportunity to receive the most effective therapy.
Research Questions and Hypotheses
The first aim of this project was to investigate whether a brief promoting positive
emotions (BPPE) intervention impacts monetary and social reward responsiveness, as measured
by the RewP, in a young adult sample. An additional goal was to investigate the association between reward responsiveness, in monetary and social domains, and depression symptoms and overall well-being in a young adult sample. Well-being was included as a variable of interest because it serves as a more specific measure of general positive affect. Finally, the primary goal of this project was to examine the interaction effect between the intervention and depression symptoms, as well as overall well-being, on social and monetary reward responsiveness. That is, I aimed to investigate whether intervention effects on reward responsiveness varied as a function of either depression symptoms or overall well-being.
I hypothesized that, as in previous research, participants in the control study skills (SS) condition would demonstrate a negative association between depression and RewP observations, such that more depressive symptoms would be related to blunted reward responsiveness.
However, for participants who underwent the BPPE intervention, depression symptoms would be positively associated with the RewP. That is, for participants in the BPPE intervention, those with more initial depression symptoms would respond to the intervention such that they then would display greater monetary and social reward responsiveness. Along with this, I
hypothesized that the RewP would be larger in the BPPE condition than the control condition. I also hypothesized that participants with higher well-being scores would display greater monetary and social reward responsiveness, regardless of group assignment.
Methods Participants
Participants were undergraduate students aged 18-24 at Vanderbilt University recruited using an online platform (SONA) and compensated with research participation credit for academic classes, as well as small prizes (e.g., scrunchies, stickers, candy, etc.). Exclusion
criteria included not being fluent in English or having any visual or hearing impairments that would prohibit individuals from completing all study procedures. A total of 54 participants consented to the study and successfully completed all study tasks. Ten participants were
excluded from analyses due to having EEG data that was too noisy to process, leading to a total sample size of 44. The average age of participants was 19.2 years old (SD= 1.01), where 32.6%
of participants identified as male, 65.1% identified as female, and 2.3% identified as non-binary.
95.3% of participants identified as non-Hispanic or Latino. 60.5% of participants identified as Caucasian, 23.2% as Asian, 9.3% as African American, and 7% as multiracial.
Of the total sample, a portion of participants’ data was not available for analysis on both reward tasks due to EEG noise and technical issues. For example, some participants had usable data for the monetary reward task, but not the social reward task, and vice versa. Specifically, 40 total participants were included in analyses on monetary reward (23 in the BPPE group, 17 in SS). For social reward, 37 participants were included (21 in the BPPE group, 16 in SS).
Study Design
Participants were randomly assigned to either the experimental ‘brief promoting positive emotions’ (BPPE) intervention group or the control ‘study skills’ group. There were three independent variables: condition (intervention or control), general depression symptoms, and overall well-being. There were two dependent variables: monetaryΔRewP and social ΔRewP.
Procedure
Participants completed a variety of questionnaires on an iPad using REDCap. All
questionnaires were given in the same order. Then, participants were randomly assigned to either the BPPE group or study skills control group and then went through the relevant intervention with a member of the research team. Following the intervention, participants completed
computer tasks, including the Monetary Incentive Delay (MID) and Island Getaway (IG) tasks, while EEG data was recorded. The order of computer tasks was randomly counterbalanced. Once the computer tasks were finished, participants were debriefed, and researchers answered any questions participants had about the experiment. Based on performance in the MID task, participants were rewarded with an amount of “cash” that could be exchanged for prizes in the lab such as hair scrunchies, candy, and stickers.
Brief Promoting Positive Emotions Intervention
In the brief promoting positive emotions (BPPE) intervention group, participants
underwent a structured 45-minute intervention with a research assistant (administered under the supervision of a Ph.D. student with clinical training). The intervention was designed to be interactive and conversation-based, comprised of a brief psychoeducation of positive emotions and teaching skills to recall, savor, and plan for positive experiences. Throughout the discussion, the research assistant described a handful of skills, gave personal examples, and asked
participants to consider how the skills could be implemented in their own lives. Participants also received a packet to fill out during the intervention to help track examples of how they had used and could plan to use positive emotion skills in their daily lives. At the end of the intervention, they were encouraged to use the packet and the skills they learned for the remainder of the experiment, as well as in the future.
Study Skills Intervention
In the study skills control intervention, participants underwent a similar 45-minute discussion with a research assistant. However, rather than learning about positive emotions, participants in this group learned about effective study skills that they could apply to their classes. The research assistant taught them about skills like notetaking, reading strategies,
making a weekly study plan, and accessing campus academic resources. Participants also filled out a packet throughout the discussion about how to apply these study skills to their classes and were encouraged to apply these skills to their daily lives.
Measures Depression
Participants completed the Inventory of Depression and Anxiety Symptoms (IDAS), which was used to assess depression and anxiety symptoms. The IDAS includes 64 items that ask participants to measure how often they have felt or experienced certain feelings, sensations, problems, and experiences related to depression and anxiety (Watson et al., 2007). The scale of responses ranges from 1 to 5, with a 1 indicating complete disagreement with the statement and a 5 indicating total agreement with the statement. Two items (questions 24 and 35) were reverse coded to ensure participants were responding genuinely rather than clicking all 1’s or 5’s.
Of the eleven subscales of the IDAS, two were used for the purposes of this project:
general depression and well-being. The general depression subscale includes 13 items designed to assess symptoms associated with depression, including sadness, guilt, and worthlessness. A higher score on the subscale indicates greater depression symptoms. The well-being subscale includes 5 items designed to assess the extent to which an individual feels happy, content, and has a positive outlook on life. A higher score on the subscale indicates higher positive affect and well-being in one’s life. Because these subscales include overlapping items, analyses were conducted on them separately.
Monetary Reward Task
On the Monetary Incentive Delay (MID) Task, participants began a trial by seeing either a blue circle with a dollar sign, to indicate an incentive trial, or a white circle, to indicate a non-
incentive trial, for 500 milliseconds. A fixation cue was displayed during an anticipatory period of 2000 milliseconds, then participants were asked to respond as quickly as possible to a small (approximately 1 inch in height and width) white box in the center of the screen by clicking the left mouse button. Depending on whether the participant successfully clicked the mouse while the box was still onscreen, they were presented with one of two cues. Clicking the mouse while the white box is still on the screen resulted in a green up-arrow (↑) representing monetary gain of 40 cents, while clicking the mouse too slowly resulted in a red down-arrow (↓) representing monetary loss of 20 cents. The non-incentive trials always displayed a yellow (-) sign and did not result in monetary gain or loss. Throughout the trials, the computer adjusted task difficulty by flashing the white box for a shorter or longer amount of time, depending on how successfully the participant was clicking the screen. The default period of time the white box was on the screen was 200 milliseconds. If a participant successfully clicked the screen in time, the following presentation period was on screen for a period 10 milliseconds shorter. If a participant was unsuccessful in clicking the screen in time, the following presentation period was 10
milliseconds longer. In order to equalize the amount of positive and negative reward feedback, the computer was set to average participants around a 50% success rate. Participants completed a total of 70 trials during this task.
Social Reward Task
The Island Getaway Task asked participants to play an online social feedback game with 13 other players. Participants were asked to email researchers a photograph of themselves to be uploaded as part of their profile, which they filled out to include their name, school, hobbies, and interests. In every round, participants first viewed a white cross during a 2000 millisecond period of fixation. Next, they viewed another player’s profile for 2000 milliseconds, where they voted
to keep the player in or kick them out. They then viewed another white cross for fixation for 1000 milliseconds, followed by feedback presentation for 1500 milliseconds. During the feedback presentation, a green thumbs-up indicated the co-player voted to keep them in the game, while a red thumbs-down indicated the co-player voted to kick them out. Throughout the trials, feedback was randomized and averaged so participants received an equal number of co- player acceptances and rejections. After six rounds participants made it to the final ‘round’ and won the game. Upon completion of the game, participants were debriefed and informed that the co-players voting them out were computer-stimulated and randomized. Participants completed a total of 51 trials in this task.
EEG Data Collection and Processing
Continuous EEG data was collected using a 32-channel Brain Products actiCHamp System while participants completed the monetary and social reward tasks. Data was first filtered with cutoffs between 0.1 and 30 Hz and corrected for any ocular movement during the tasks, then it was segmented to 200 milliseconds before and 800 milliseconds after feedback. For single electrodes with exceptionally noisy data, interpolation was utilized via signals from surrounding electrodes. Semiautomatic artifact rejection removed artifacts on the basis of voltage steps greater than 50 µV, maximum voltage difference of 175 µV, a minimal allowed amplitude of -200 µV and maximal allowed amplitude of 200 µV, and lowest allowed activity of 0.50 µV within 100 millisecond intervals. Then, the data was inspected visually by a research assistant to ensure other artifacts were removed as needed. Monetary and social RewP indices were scored according to prior literature and visual inspection. That is, while the RewP to the MID task is typically scored from 250-350 milliseconds (Ait Oumeziane et al., 2019) and the RewP to the IG task is typically scored from 275-375 milliseconds (Kujawa et al., 2014), the current data
suggested earlier timeframes to capture the monetary and social RewP, respectively. See Figures 1 and 2. For the MID task, monetary ΔRewP was scored between 200-300 milliseconds at Cz by subtracting the average response on loss trials from win trials. For the IG task, social ΔRewP was scored between 250-350 milliseconds at Cz by subtracting the average response on rejection trials from acceptance trials.
Figure 1
RewP at Cz for the Monetary Incentive Delay Task
Note. The red line denotes monetary RewP during win trials. The blue line denotes monetary RewP during loss trials. The black line denotes monetary ΔRewP between win and loss. The red area on the scalp distribution reflects areas of larger difference in monetary ΔRewP.
Figure 2
RewP at Cz for the Island Getaway Task
Note. The red line denotes social RewP during acceptance trials. The blue line denotes social RewP during rejection trials. The black line denotes social ΔRewP between acceptance and rejection. The red area on the scalp distribution reflects areas of larger difference in monetary ΔRewP.
Data Analysis
There were three independent variables: condition (intervention or control), general depression symptoms, and overall well-being. There were two dependent variables: monetary ΔRewP and social ΔRewP.
In SPSS, descriptive statistics were run on demographic and IDAS self-report data.
Additionally, central tendency data and independent samples t-tests were run on monetary and social ΔRewP. Participants were grouped by condition, which was coded as a binary variable (0
= BPPE, 1 = control). The test variables were monetary and social ΔRewP.
In R Studio, multiple linear regression analyses were conducted to test the effects of condition, depression, and their interaction in explaining variance in monetary and social
ΔRewP. Additional multiple linear regression analyses were also conducted to test the effects of
condition, well-being, and their interaction in explaining variance in monetary and social
ΔRewP. R packages to run these analyses included the interactions package (version 1.1.0; Long, 2019), readxl package (version 1.4.0; Wickham & Bryan, 2022), jtools package (version 2.2.0;
Long, 2022), ggplot2 package (Wickham, 2016), readr package (version 2.1.2; Wickham, Hester,
& Bryan, 2022), and lm.beta package (version 1.7-1; Behrendt, 2023). Prior to all analyses, general depression and well-being were standardized. Condition was coded as binary variable (0
= BPPE; 1= control).
The first model regressed monetary ΔRewP onto condition, depression, and the interaction between condition and depression. The interaction term was included to examine whether the association between depression symptoms and monetary ΔRewP may be moderated by condition. The second model regressed monetary ΔRewP onto condition, well-being, and the interaction between condition and well-being. The third model regressed social ΔRewP onto condition, depression, and the interaction between condition and depression. The interaction term was included to examine whether the association between depression symptoms and monetary ΔRewP may be moderated by condition. The fourth model regressed social ΔRewP onto condition, well-being, and the interaction between condition and well-being.
Results Group Differences in RewP
As expected, the average monetary ΔRewP was higher in the BPPE group (M = 3.46, SD
= 4.32) than the SS group (M = 2.31, SD = 4.71). However, this was not a statistically significant difference, t(38) = 0.8, p = 0.428, d = 0.25. Contrary to hypotheses, the average social ΔRewP was lower in the BPPE group (M = 1.17, SD = 4.32) than the SS group (M = 2.48, SD = 5.67).
However, this was not a statistically significant difference, t(35) = -0.80, p = 0.432, d = 0.26.
Multiple Linear Regression Analyses Monetary Reward
A multiple linear regression model tested the associations between the monetary ΔRewP, as the dependent variable, and three independent variables: condition (BPPE versus study skills), general depression scores, and an interaction term between condition and general depression to investigate the potential moderating effect of condition on the effect between general depression and monetary ΔRewP. Overall, the model was not significant (F(3,36) = 1.02, p = .395) and explained no variance in monetary ΔRewP (adjusted R2 = 0). Results revealed that condition was not significantly associated with monetary ΔRewP (β = -0.12, p = 0.448). General depression was also not significantly associated with monetary ΔRewP (β = -0.36, p = 0.167), although the pattern was in the expected direction. The interaction term was not statistically significant, though it was trending toward statistical significance with a medium effect size (β = 0.39, p = 0.14). Figure 3 illustrates the relationship between general depression and predicted monetary ΔRewP for both groups. As seen in the figure below, there is a negative association between depression and monetary ΔRewP only in the BPPE group.
Figure 3
Interaction Effect Between General Depression and Condition on Monetary ΔRewP
An additional multiple linear regression model examined the associations between the monetary ΔRewP, as the dependent variable, and three independent variables: condition (BPPE versus SS), well-being scores, and an interaction term between condition and well-being to investigate the potential moderating effect of condition on the effect between well-being and monetary ΔRewP. Overall, the model was not significant (F(3,36) = 0.31, p = 0.821) and
explained a small amount of variance in monetary ΔRewP (adjusted R2 = -0.06). Results revealed that condition was not significantly associated with monetary ΔRewP (β = -0.13, p = 0.433).
Well-being was not significantly associated with monetary ΔRewP (β = 0.14, p = 0.586). The interaction term was not statistically significant (β = -0.09, p = 0.704).
Social Reward
A multiple linear regression model examined the associations between social ΔRewP, as the dependent variable, and three independent variables: condition (BPPE versus SS), general depression scores, and an interaction term between condition and general depression to
investigate the potential moderating effect of condition on the effect between general depression
and social ΔRewP. Overall, the model was not significant (F(3,33) = 1.18, p = 0.333) and explained only a small amount of variance in social ΔRewP (adjusted R2 = 0.01). Results
revealed that condition was not significantly associated with social ΔRewP (β = 0.13, p = 0.445).
General depression was not significantly associated with social ΔRewP, though it was trending toward statistical significance and positively associated with social ΔRewP, with a medium effect size (β = 0.41, p = 0.127). The interaction term was not statistically significant in predicting social ΔRewP, though it was also trending toward statistical significance and negatively associated with social ΔRewP, with a medium effect size (β = -0.43, p = 0.112).
Although the interaction effect was not statistically significant, a simple slopes analysis revealed that the largest association between general depression and social ΔRewP was found in the BPPE condition (t = 1.57, p = 0.13), suggesting that depression was positively associated with social ΔRewP only in the BPPE group. In line with hypotheses, for those in the BPPE group, increases in depression symptoms was associated with a larger social ΔRewP. Figure 4 below illustrates the relationship between general depression and predicted social ΔRewP for both groups.
Figure 4
Interaction Effect Between General Depression and Condition on Social ΔRewP
An additional multiple linear regression model tested the associations between social ΔRewP, as the dependent variable, and three independent variables: condition (BPPE versus SS), well-being scores, and an interaction term between condition and well-being to investigate the potential moderating effect of condition on the effect between well-being and social ΔRewP.
Overall, the model was not significant (F(3,33) = 0.34, p = 0.8) and explained a small amount of variance in social ΔRewP (adjusted R2 = -0.06). Results revealed that condition was not
significantly associated with social ΔRewP (β = 0.14, p = 0.432). Well-being was not
significantly associated with social ΔRewP (β = -0.16, p = 0.534). The interaction term was not statistically significant in predicting social ΔRewP (β = 0.11, p = 0.674).
Discussion
Inconsistent with hypotheses, there were no significant differences in monetary or social reward responsiveness between the intervention and control group. Additionally, contrary to hypotheses, scores of depression and well-being were not significantly associated with reward responsiveness in either the monetary or social domains. Though not statistically significant, a
positive association of medium effect size was found for general depression in relation to social reward for participants in the BPPE group, consistent with hypotheses. These preliminary findings may suggest that as depression symptoms increase, so does social reward
responsiveness, which is contradictory to previous findings , which have found more depression symptoms to be associated with a smaller ΔRewP (Henriques & Davidson, 2000; Lutz &
Widmer, 2014; Ait Oumeziane et al., 2019). This indicates that it is possible the BPPE intervention may have influenced social RewP for those with higher depression symptoms.
Unexpectedly, the interactions between condition and both depression and wellbeing were not significantly associated with reward responsiveness in either the monetary or social domain.
Though not statistically significant, a positive association of medium effect size was found for the interaction between general depression and condition associated monetary reward. This preliminary finding suggests that for those with fewer depression symptoms, the intervention may have provided a slight boost in reward responsiveness, but it may not have been as beneficial to those with greater symptoms. A positive association of medium effect size was found for the interaction between general depression and condition associated with social reward, though it was also not statistically significant. This preliminary finding suggests that it is possible that for those with greater depression symptoms, the intervention may have provided a slight boost in reward responsiveness.
Monetary and Social Reward
Overall, little evidence of group differences in the RewP across domains were found.
This is contrary to my hypothesis, as it was expected that regardless of pre-existing depression and well-being levels, the intervention would promote a more responsive RewP compared to the control condition. Further, while the interaction between general depression and condition was
not statistically significant in predicting social ΔRewP, it was trending towards significance. This suggests that it may be possible that for those in the intervention group with high pre-existing depression, the intervention provided a slight boost in reward responsiveness, as reflected by a larger social RewP. In comparison, those in the control group with more depression displayed a more blunted RewP, which is consistent with the previous depression literature (Kujawa et al., 2014; Distefano et al., 2018; Rademacher et al., 2010). This is consistent with previous
intervention studies to increase RewP (Kryza-Lacombe et al., 2021; Burkhouse et al., 2018), although many previous studies have utilized multi-session, CBT-oriented approaches. As this study is the first to investigate the impact of a single-session, positive-emotion focused
intervention, it is encouraging that patterns in the results are trending towards potentially having the same impact on reward responsiveness as past research.
Though these results were not statistically significant, it is worth considering how the intervention may have impacted monetary and social reward responsiveness differently. Because the intervention asks participants to relate positive emotions and experiences to their own lives, and all participants were college students, perhaps they inherently related their experiences more to socially oriented activities and memories. For college students in particular, the college environment inherently requires having stable relationships with peers (Steinberg, 2004); thus, it is possible that for young adults in college specifically, social reward responsiveness is more likely to be impacted by a discussion asking participants to recall, savor, and plan for positive experiences. Understanding the relationship between what reward individuals value more could potentially aid in measuring the effectiveness of interventions similar to BPPE. If an individual is depressed and values social reward, utilizing social reward responsiveness before and after an intervention can be a valuable tool in understanding how beneficial the intervention is.
Limitations & Future Directions
This study was the first to examine the impact of a brief promoting positive emotions intervention on neural measures of reward responsiveness. Because depression and reward responsiveness have been repeatedly associated in previous research, it was important to consider how a short, single-session discussion about positive emotions & skills could impact a neural marker of depression. Nonetheless, several limitations should be noted. First, it is
important to note that the sample of young adults in the study are not representative of the population of interest. Ideally, acquiring a sample size of young adults outside of an academic institution—including participants from a broader range of educational levels, socioeconomic statuses, and cultural backgrounds—would provide a more representative sample. Furthermore, this intervention was delivered by trained undergraduate research assistants (RAs), and it is possible that there were inconsistencies between RAs in terms of intervention delivery and dialogue, though study staff read off of scripts to minimize this impact. Similarly, because undergraduate RAs led participant sessions, it was common to encounter noisy EEG data or technical errors due partially to inexperience that may have impacted the reliability of the neural data. This was reflected in the large number of exclusions in the final sample size due to EEG data quality. Additionally, participants’ levels of engagement and participation in the
intervention may have impacted measures of its effectiveness, which may have limited reward responsiveness for participants in the BPPE condition. Finally, the sample size was not as large as would be preferable to achieve adequate power to observe a more accurate effect size.
Overall, this study provided the first glimpse into the impact of a single-session positive intervention on a neural indicator of depression and well-being. Although results were
statistically nonsignificant, effect sizes indicated that the intervention may lead to a small
increase in social reward responsiveness, particularly for individuals with pre-existing depression symptoms. It can be concluded that a brief positive emotion intervention may potentially be beneficial to increasing reward responsiveness, but more research must be done in order to obtain a more accurate understanding.
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