Cognitive control and emotion regulation in adolescence: Examining the impact of affective inhibitory control and heart-rate-variability on emotion regulation dynamics in daily life
Creators: name: Michels, Nathalie
name: Debra, Gillian
name: Giletta, Matteo
Publisher: name: OSF Registries
Year: 2024
Subjects: subject: Developmental Psychology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: OBJECTIVES AND HYPOTHESES:
Compared to child- and adulthood, adolescence is characterized by a heightened intensity of emotional and physiological responses to stress (e.g., Somerville et al., 2010; Spear, 2009), which may challenge the downregulation of negative affect (NA). Thus, understanding the link between emotion regulation (ER) processes and NA in adolescence is particularly relevant. Crucially, empirical and theoretical work suggest that individuals’ cognitive control abilities, especially in the context of emotionally salient situations (i.e., affective control, Schweizer et al., 2020) may contribute relevantly to ER processes. Yet, little is known about how affective inhibitory control influences adolescents’ ER dynamics, across various situations throughout daily life, where affective control is likely particularly important (e.g., Friedman &amp; Miyake, 2017).

This study main objective is to investigate whether trait (between-person) differences in the ability to inhibit responses towards affective stimuli (i.e., affective inhibitory control) moderate the relationship between adolescents’ momentary use of ER strategies (ERS; cognitive reappraisal, rumination) and momentary NA in daily life. In addition, building on the Neurovisceral Integration Model (NIM), the influence of individual differences in heart-rate-variability (HRV) will be investigated as a peripheral index of (affective) inhibitory control processes. Specifically, the NIM proposes that regulatory processes of the prefrontal cortex on subcortical areas (including the amygdala) result in tonic inhibition which is outputted to the heart’s sinoatrial node via the vagus nerve and stellate ganglia (Thayer et al., 2009). As a consequence, high resting vagally-mediated HRV (vmHRV) can reflect efficient functioning of these prefrontal-subcortical regulatory processes involved in (affective) inhibition control (e.g., Thayer &amp; Lane, 2009). Consistent with this idea, a meta-analytic review suggests that resting vmHRV correlates positively with inhibitory control (Magnon et al., 2022).

Data will be used from the ongoing longitudinal Outside-In study which included an ecological momentary assessment (EMA), and repeated assessments of cardiovascular activity and affective inhibitory control. An overview of the study hypotheses is provided below. 

Hypothesis 1: Cognitive reappraisal. Based on theoretical and empirical work (e.g., Joormann &amp; D’Avanzato, 2010; Pe et al., 2013), we expect the relationship between cognitive reappraisal and momentary NA to be stronger in adolescents’ demonstrating a higher ability to inhibit negative information as well as a higher resting vmHRV. 
Difficulties in inhibiting affective stimuli has been suggested to hinder the processing of novel relevant information required for successful reappraisal (Joormann &amp; D’Avanzato, 2010). For example, adults showing a higher ability to inhibit negative information in working memory (i.e., affective inhibitory control) tended to report a greater downregulation of NA after using cognitive reappraisal (Pe et al., 2013). Concerning vmHRV, based on the NIM, optimal functioning of prefrontal-subcortical inhibitory processes - which arguably could include inhibition of emotionally irrelevant information hindering reappraisal - could be reflected by higher levels of resting vmHRV (e.g., Thayer &amp; Lane, 2009). Accordingly, in adults, higher level of vmHRV is found to predict more frequent use of cognitively demanding ERS, including reappraisal (Geisler et al., 2010).

Hypothesis 2: Rumination. We expect the association between rumination and momentary NA to be weaker in adolescents demonstrating a higher affective inhibitory control and displaying a higher resting vmHRV. 
Difficulties with affective inhibitory control is proposed to prompt rumination, as negative thoughts are allowed to remain longer in working memory (Joormann &amp; Gotlib, 2008). Adults showing lower abilities to inhibit negative information were observed to experience more momentary NA following the use of rumination (Pe et al., 2013),. In adolescents, youth reporting higher levels of trait rumination emerged to have more difficulties in inhibiting negative stimuli as compared to adolescents reporting lower trait rumination (Hilt et al., 2014). Regarding vmHRV, based on the NIM, higher levels of vmHRV is expected to be associated with less intrusive ruminative thoughts. Some studies lend support to this notion, as lower levels of resting vmHRV in adults were linked with increased reported trait rumination (Williams et al., 2017; but see e.g., Li et al., 2023) as well as current state rumination (Kocsel et al., 2019).

Exploratory examinations:
Findings in adults show that the ability to inhibit negative stimuli and a higher resting vmHRV is associated with lower NA (e.g., Shimony et al., 2021; Sloan et al., 2017) and greater use of cognitive reappraisal (Geisler et al., 2010). Therefore, in addition to test the hypotheses described above, we will examine the extent to which one’s affective inhibitory control and resting vmHRV relate to momentary NA, the stability of NA (i.e., autoregressive estimate of NA), and adolescents’ habitual use of cognitive reappraisal and rumination. 

DATA COLLECTION:
Have any data been collected for this study already?
Yes, data have already been collected as part of an ongoing longitudinal project (“Outside-In”). Outside-In aimed at investigating the mechanisms underlying the influence of peer adversity (i.e., bullying victimization) on health outcomes. This multi-wave project includes behavioral and physiological measures as well as repeated EMAs, every six months starting from the fall of 2021. Relevant to the purpose of the current study, an affective go/no-go task was administered to measure affective inhibitory control. Furthermore, adolescents’ cardiovascular activity at rest was measured to obtain indicators of tonic vmHRV. The repeated EMA involved a subsample (n = 243) from the project and took place approximately 1-2 weeks following the last assessment of affective inhibition control and vmHRV. The main objective of the current study is to examine how ER dynamics captured by EMA are influenced by trait (between-person) differences in affective inhibitory control and vmHRV at rest. Therefore, we will use EMA data from the third wave (October- December 2022), and data from the first three waves (wave 1 October-December 2021, wave 2 April – June 2022, wave 3 October- December 2022).to extract between-person measures of affective inhibitory control and resting vmHRV by using 

Have any data been used in other studies?
Yes, EMA data tapping into momentary affect and ER (see the Measures section) from the third wave are also used in two other studies. First, the (currently ongoing) study of Braet et al. is examining whether certain ERS (distraction, rumination, and cognitive reappraisal) mediate the relationship between self-kindness and both NA and positive affect (https://osf.io/gktbp). Second, the (currently ongoing) study of Lo et al., is investigating if emotion differentiation (using items tapping into NA and positive affect) enhances emotion regulation variability (using each included ERS, see the Measures section) (https://osf.io/9vx7t). Importantly, EMA data have not been merged yet with behavioral and physiological data.

Experience sampling procedure
Several days prior to the start of the data collection, participants were instructed to install the m-Path application (https://m-path.io/landing/) on their phones after which they received in-person instructions about the study procedure (taking approximately 10 minutes), including a description of the timing of the EMA surveys, a brief explanation of the questions, and information on how to contact the investigators, in case support was needed.

Participants received five smartphone surveys per day during two subsequent weeks (including 10 week days and two weekends). Surveys were scheduled at fixed time points (time-based sampling scheme) resulting in a total of 70 surveys. Table 1 (in the supplementary materials) displays the time schedule of each survey. Note that the time schedule is tailored to each school and differed during the weekend. Surveys remained active for a maximum of 50 minutes, 90 minutes (fourth survey), or 120 minutes (fifth and last survey of the day), before they expired. Consistent with previous EMA guidelines (van Roekel et al., 2019), cumulative incentives based on adolescents’ survey completion were used to increase compliance. Adolescents received a 20-euro gift voucher (completion of at least 49 surveys) or a 10-euro gift voucher (completing between 35 and 48 surveys). Participants’ compliance rate was actively monitored and reminders were sent whenever necessary.

MEASURES:

trait measures:

Affective inhibitory control
At each of the three waves, adolescents completed an emotional go/no-go task to assess their ability to inhibit affective information. The task was run on a Lenovo laptop having a 15.6 inch screen and programmed using Psychopy software (Peirce et al., 2019) version 2020.1.3. The stimulus set consisted of pictures of 10 adolescent faces (five male and females) taken from the National Institute of Mental Health Child Emotional Faces Picture Set (Egger et al., 2011). Images displayed either an angry, happy or neutral facial expression (see Figure 2 in the supplementary materials). Participants were seated in front of the laptop in a well-lit room away from windows or other distracting elements. Participants were instructed to press the space bar or to withhold their response when perceiving either a neutral or an emotional facial expression, depending on the task block. Before starting the actual task, participants were instructed to respond as fast and accurately as possible and they had to complete a training phase to ensure that they correctly understood the task. During this training phase, participants had to respond on the emotional face and were shown five sad and four neutral faces, in a fixed order. We opted for sad faces during this phase to reduce familiarity with the main set of stimuli. Whenever participants failed to respond correctly in at least seven out of nine trails, participants received a brief repetition of the instructions by the investigator and had to restart the training phase until this criterion was met.

 During the go/no-go task, participants had to complete four task blocks ("go happy, no-go neutral”; “go neutral, no-go happy”; “go angry, no-go neutral”; “go neutral, no-go angry”) each consisting of 60 trials. The sequence of blocks was pseudorandomized in such way that blocks containing angry faces were always followed by happy expressions and vice versa. Within each block, 15 trials were no-go trials occurring in a fixed position within each block (Table 2). Stimuli were randomized such that two consecutive trails did not contain identical face and facial expression (the same face but a different emotion was allowed). Each trial started with the face stimuli shown for 500 ms before disappearing and being followed by a black fixation cross for 1,500 ms (see Figure 1). Responses could be administered during the whole trail, thus during the appearance of a face and during the fixation cross. Including instructions and training phase, the current task took approximately 12 minutes to complete. At each wave, participants’ commission error rate during the “go neutral, no-go angry” block was used as an indicator of affective inhibitory control. Specifically, at each wave, a percentage of the amount of incorrect responses to the 15 angry stimuli was computed and used in further analyses.

Heart rate variability
Adolescents’ resting electrocardiogram was continuously recorded using EcgMove4 activity sensors (https://www.movisens.com) with a sampling rate of 1024 Hz. The sensor was attached on the surface of the skin below the left pectoral muscle using two electrodes. Afterwards, participants watched a movie clip depicting natural landscapes (deemed as non-arousing) in absence of other persons, for five minutes. Participants were instructed to sit relaxed in an upright position and were discouraged to move while watching the movie. Heart rate and inter-beat interval (IBI) series of the middle 3 minutes from these five minute recording were extracted and processed using the Kubios HRV Premium software (version 4.0; Tarvainen et al., 2014). The IBI’s were detrended utilizing the smoothness priors method (λ = 500). Moreover, cubic spline interpolation (4Hz) was applied to replace artefacts. Following automatic artifact correction by Kubios (Lipponen &amp; Tarvainen, 2019), visual inspection was done to check and remove low-quality data. At each wave, the root mean square of the succesive differences (RMSSD) was used as an index of vmHRV. Compared to high frequency parameters, the RMSSD is observed to be less influenced by respiratory rate (Hill &amp; Siebenbrock, 2009). 

EMA measures

Momentary negative affect
Participants momentary NA was measured with six emotion words tapping into high and low arousing emotions (i.e., sad, angry, anxious, uncertain, annoyed, and stressed). Items were rated on a 7-point scale ranging from 1 (totally not) to 7 (totally). Following reliability analyses (see the analysis section), the removal of items showing a low factor loading compared to the other loadings will be considered. A scale score will be calculated using the mean across the corresponding items. Consistent with previous work in our group (Debra et al., submitted) we will exclude scale scores at any given occasion if over half of the items at that occasion would be missing. Higher scale scores reflect a higher level of NA

Emotion regulation strategies and mean strategy endorsement
Participants’ use of ER was assessed using a two-step approach (similar to e.g., De France &amp; Hollenstein, 2021). First, participants reported the intensity of their experienced negative emotions since the last survey (or after waking up). In case no negative emotion was experienced, participants were instructed to respond with the score of 1. Second, questions assessing ERS use were introduced in response to these negative emotions and were followed by seven ERS. Single items from the validated RESS-EMA scale (Medland et al., 2020) were used to assess cognitive reappraisal (“I changed the way I thought about the situation.”), rumination (“I could not stop thinking about it.”), distraction (“I did other things to distract myself.”), expressive suppression (“I tried to hide my emotions”), and emotional expression (“I expressed my feelings.”). Next, adopted from Brans et al. (2013), one item was used to measure social sharing (“I have talked about my feelings with others.”). Finally, two items from the Dutch version of the Selbsteinschätzung emotionaler Kompetenzen questionnaire (SEK-27; Berking &amp; Znoj, 2011) were used to assess self-kindness (“I tried to cheer myself up”; “I supported myself”). An average score of self-kindness items will be computed (Debra et al., submitted). A higher score indicates a more intense use of a given ERS.

While cognitive reappraisal and rumination are of main interest for this study, the remaining ERS will be used to compute a mean strategy endorsement by taking the average across all seven ERS in each survey. The mean strategy endorsement will be included to examine the influence of cognitive reappraisal and rumination on NA, above and beyond the influence of one’s average use of ERS (e.g., Brans et al., 2013; Debra et al., submitted). A higher mean strategy endorsement could potentially indicate a more intense use of multiple strategies at a given time point.

Control items
To detect careless responding, each EMA survey included one control item. Specifically, participants were shown a Dutch word containing two up to seven letters and had to report the amount of letters this word consisted of. Words were chosen randomly from a list containing 42 words (see Table 2 in supplementary materials). Furthermore, control items were placed randomly within each survey. Using the control items, accuracy scores were computed by dividing the amount of correct responses with the amount of surveys participants completed in total. Participants showing an accuracy score below 80% will be excluded from the analyses.

PARTICIPANTS:
A total of 243 adolescents enrolled in their second year of secondary school took part in the EMA during the third wave. Participants hailed from nine secondary schools in the Flemish region of Belgium were allowed to participate if they provided assent and had parental consent . Three participants completed less than three EMA surveys and will be excluded in further analyses. Moreover, five participants showed an accuracy score below 80% on the check items and will be removed. Finally, we will exclude one participant who was notably younger (11 years of age) compared to their peers, given the possible influence of pubertal development on the investigated processes (e.g., Juraska &amp; Willing, 2016). Therefore, the final sample will include 235 participants (Mage = 13.48 years, SDage = 0.39, 106 females). 

ANALYSIS:

Data preparation
Before running the main analytic models, a series of preliminary analyses will be performed. First, we will use the commission error rates and RMSSD scores at the three waves to extract persons’ trait level indicators. . Specifically, we will estimate two separate random intercept models using either RMSSD scores or commission error rates at the three waves as manifested variables and separate the between-person from within-person variance. The resulting random intercepts for RMSSD and commission error rates, reflecting between-person variation in mean levels, will be extracted and used in subsequent analyses. Second, we will examine within- and between-person reliabilities of NA by computing McDonalds’s omega coefficients using multilevel confirmatory factor analyses in Mplus (Geldhof et al., 2014; McNeish, 2018). If the within-person reliability would fall below .60, we might consider to exclude items that upon removal would result in increased within-person reliability (given our focus on within-person estimates). Third, intraclass correlations (ICC; i.e., the proportion of variance at the between-person level) will be calculated for momentary NA, reappraisal and rumination using multiple intercept-only models (including a random intercept per participant). Prior to extracting the between-person variance we will compute and report the ICC from commission error rate, and RMSSD, as well. Fourth, we will evaluate the assumption of stationarity of the EMA data (i.e., no trends over time in both mean, variance, and autocorrelation) through estimation of two-level fixed effect models in which the number of the survey (reflecting the time trend) is the only predictor. Should considerable time-trends be detected e.g., when the number of EMA trigger accounts above 5% of variance in NA (based on previous work from our lab), we will consider the recommendations of Asparouhov et al. (2018) to use residual dynamic structural equation modelling in which the time trend is modeled separately from the autoregressive effect. Finally, to deal with the unequally spaced measurement occasions, we will use the Mplus command “tinterval”. Since EMA surveys were scheduled with approximately three hours in between, we will initially set three-hour time windows. 

Dynamic structural equation models
To analyze our research questions we will estimate a series of multilevel autoregressive (lag-1) models using Dynamic structural equation models (DSEM, for an overview see Figure 3 in the supplementary materials). Each model will contain (a) the autoregressive estimate of NA and (b) the within- and between-person regression estimates of NA and cognitive reappraisal or rumination (in a separate model), and strategy mean endorsement. Next, to test our main research objective, we will include the random intercept indicator of commission error rate or RMSSD (in a separate model) as between-person predictors of the regression estimate between rumination or reappraisal (in a separate model), and NA. Similarly, also at the between-person level, we will regress current NA, the autoregressive estimate of NA, and cognitive reappraisal or rumination, on the current moderators (see the Mplus code example in the supplementary materials). Lastly, we will include the covariances between the with-person slopes and NA. 
In the above models, within-person regression estimates will be modelled as latent (random) slopes and allow to vary per person. Next, noninformative priors will be used since little is known concerning how affective inhibitory control or vmHRV influence the relationship between single ERS and NA in adolescents’ daily life. Model convergence will be evaluated using both the potential scale reduction (PSR) convergence criterium (a PSR below 1.02 will be considered as a reference cut-off consistent with Hoofs et al., 2018) and through visual inspection of the trace plots of each parameter. Should a model have poor converge, we may potentially consider to (a) change the regression estimate of NA and the strategy mean endorsement from latent random to fixed and (b) remove the covariances between each within-person variable and estimate. Similar to previous work from our group, models will be run with a fixed amount of 500,000 iterations to ensure stable estimations.

Inference criteria
In addition to p-values, we will primarily focus on the point estimates of the parameters and the corresponding 95% credibility intervals (CI) of the posterior distribution as these tend to be more informative. Specifically, inspection of the CI allows to observe how precise the parameter estimations are and whether negative or positive values (i.e., a negative or positive trend) for these estimations are more likely. 

Sensitivity analysis
For each final DSEM model, we will check the robustness of our findings in two main ways. First, we will remove participants with no within-person variance in NA and rumination or cognitive reappraisal in a given model. Second, we will omit data from all EMA triggers in which participants answered the control item incorrectly. 

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