AUTOMATIC EMOTIONAL REGULATION IN
ADOLESCENTS, A MEG PILOT STUDY
C. O. SMULDERS, T. PARVIAINEN, 2018
1. INTRODUCTION
Being able to concentrate on the teacher while you just had a heated argument with your friend: one of the more difficult situations to navigate through for adolescents. Decreased inhibitory control ability as part of normal development in adolescent populations has been a known issue for as long as research has been done on teenagers (Hare et al., 2008; Hirvonen, Väänänen, Aunola, Ahonen, & Kiuru, 2017; Kadosh, Heathcote, & Lau, 2014). Much research has been done on the behavioral changes that occur during late
adolescence (Kadosh et al., 2014; van der Cruijsen, Peters, van der Aar, & Crone, 2018), ranging from inhibitory control to emotionality. To test these created theories, neuroscientific tools have been used to study multiple levels of biological structures, ranging from changes in the dopaminergic pathways (Wahlstrom, Collins, White, & Luciana, 2010) to the pruning of axonal pathways in the frontal lobe (Johnson et al., 2016). This body of research points towards a reduction of inhibitory control capability in adolescence, which is a key executive function (Diamond, 2013), as well as emotional regulation. Inhibitory control research has been a popular topic of interest in adolescent research due to the relative importance to this specific age group. Generally, in Western countries, education is typically mandatory until around the age of 18. This results in the final years of education coinciding with these behavioral changes (Hirvonen et al., 2017). This is relevant, as educational success is a key determinant of later life success. Reduced inhibitory control has been proven to reduce academic achievement (Allan, Hume, Allan, Farrington, & Lonigan, 2014).
Increased knowledge on the relation between behavioral changes in this age group and the academic responsibilities they have could lead to better educational tools and interventions. Inhibitory control in the context of academia can be described as the down-regulation of distracting stimuli when attention should be focused on an academic task. This can be a simple auditory or visual distractor (e.g. a window to the outside world), or a more complex emotional distractor (e.g. a friend tempting you into gossip in the back of class).
Inhibitory control in the context of an emotional distractor is often referred to as emotion
regulation (Cole, Martin, & Dennis, 2004; Gross & Gross, 2002; Urbain, Sato, Pang, & Taylor, 2017; Uusberg, Thiruchselvam, & Gross, 2014). The term emotion regulation - outside of the context of adolescent academic performance - has a long history of research and is often used in a variety of ways (Ertl, Hildebrandt, Ourina, Leicht, & Mulert, 2013; Phillips, Ladouceur, & Drevets, 2008a). As mentioned, emotion regulation is often used as a catch-all term referring to inhibitory control processes in the context of emotional stimuli. Recently however, active emotion regulation, the practice of actively up- or down-regulating your emotional response to a stimulus, has gained popularity as a clinical tool in treating depression and related pathologies (Koole, Webb, & Sheeran, 2015; Langeslag & Van Strien, 2013; Wessing, Rehbein, Postert, Fürniss, & Junghöfer, 2013). This method was used after findings showed above chance successful diagnosis of depression using an automatic emotion regulation paradigm (Berking, Wirtz, Svaldi, & Hofmann, 2014; Van Beveren et al., 2016). For the purpose of this study, however, the original use of the term emotion regulation
will be applied, thus leaving aside any clinical considerations.
Research repeatedly shows correlations between emotion regulation skills and academic
performance (van der Cruijsen et al., 2018; Wu et al., 2014). Generally, poor emotional regulation negatively correlates with academic results. This is often explained as the effect of poor inhibitory control in general, as inhibitory control is also strongly correlated with academic performance (Diamond, 2013; Hirvonen et al., 2017). A recent study on the effects of temperament style on academic performance, however, showed that individuals with poor inhibitory control as well as high negative affectivity (i.e. emotion regulation) were most at risk of poor academic performance, more so than those with just inhibitory control problems. In line with this work, research on the effects of emotional stimuli on inhibitory control shows decreased performance when emotional trials are compared to neutral trials (Balconi, Brambilla, & Falbo, 2009; Todd, Lewis, Meusel, & Zelazo, 2008). This has been shown in an academic context (Hare et al., 2008), as well as more general cognitive research (Bayle & Taylor, 2010; Chen et al., 2010; Lamm & Lewis, 2010; Yuan et al., 2011).
In current research, emotion regulation is generally split up into two possible modes of processing: active and implicit (Phillips, Ladouceur, & Drevets, 2008b). Active emotion regulation refers to the ability to actively change your emotional state in response to a given stimulus (e.g. trying to get energetic about doing a work-out while not innately experiencing such an emotion). While this often relates back to the clinical work done with emotion regulation, i.e. actively downregulating negative emotions in depressive patients, research has also shown the existence of active emotion regulation in an academic context (Wu et al., 2014). The more commonplace type of emotion regulation, and the one most relevant to everyday situations, is implicit emotion regulation, the unconscious ability to up- and downregulate effects of emotional stimuli.
Implicit emotion regulation has been studied in a host of everyday contextual situation such as reaction to emotional faces (Shafritz, Collins, & Blumberg, 2006; Todd, Lee, Evans, Lewis, & Taylor, 2012), emotional events and stories (Mocaiber et al., 2010), and the receiving of negative and positive grades (Wu et al., 2014). This set of research foci is non-exhaustive, and additional research is needed into the complex role emotion regulation plays in everyday life. Behaviorally however, it has been shown consistently that being exposed to emotional stimuli leads to decreased performance on standard inhibitory tasks such as the Stroop task and Go/No-go paradigms (Nixon, Liddle, Nixon, & Liotti, 2013). This is especially true for negative emotional cues, while positive emotional effects are often context dependent (Balconi et al., 2009; Rutherford & Lindell, 2011; Yuan et al., 2011). Cognitively, this is generally explained by
increased processing that accompanies emotional stimuli, where regions outside of visual areas (or other sensory areas) affect the amount of time it takes to process information (Pessoa, 2009). While this extra processing often leads to a decrease in the reaction time on Go trials (where no inhibitory process is required), it increases the neural inhibition response on No-go type trials
Figure 1: Model of brain regions involved in emotion regulation processes. Taken from Philips et al. 2008
(Lamm & Lewis, 2010; Lewis, Todd, & Honsberger, 2007; Shafritz et al., 2006).
(f)MRI research has found several prefrontal regions involved in emotion regulation, where orbitofrontal cortex and parahippocampal regions were specifically activated more in emotion inhibition trials compared to inhibition trials where neutral images were used (Pessoa, 2009; Phillips et al., 2008a). More complex models of activation in reaction to emotional stimuli processing exist (figure 1), like the popular model by Pessoa (2009), but are beyond the scope of this research to consider.
More temporaly focused research, using EEG and MEG, allow for a better deduction of the process of neural activity, where the later allows for a synchronization of information on temporal processing and spatial localization. This type of research has found delayed inhibitory responses for No-go trials paired with emotional stimuli, compared to No-go trials paired with neutral stimuli (Langeslag & Van Strien, 2013; Mocaiber et al., 2010; Wessing et al., 2013). MEG research has also found increased signal amplitude in various regions for emotional trials as compared to neutral trials (Bayle & Taylor, 2010; Giorgetta et al., 2013; Urbain et al., 2017; Wessing et al., 2013). Interestingly, this increased amplitude was enhanced when emotional stimuli were presented in a subject familiar context, i.e. images of friends or family. This could have strong ecological validity implications, as emotional distractors often come from people familiar to the individual. EEG research has also found several sporadic effects of emotional stimuli in the context of inhibition, like a higher response amplitude to visual cues at around 200ms (Deveney & Pizzagalli, 2008; Goodman, Rietschel, Lo, Costanzo, & Hatfield, 2013; Lewis et al., 2007; Zhang & Lu, 2012), and a stronger late positive potential generally associated with inhibitory responses (Langeslag & Van Strien, 2013; Mocaiber et al., 2010).
All of the previously mentioned research,
however, concerns either an adult population, or a
population much younger than what is
traditionally considered adolescence (i.e. 4-10). While research comparing adult emotion regulation to that of children exist, the age of ‘children’ is never older than 12 (Todd et al., 2012; Urbain et al., 2017). Often researchers explain their cut-off point in age by relating it to the changing nature and high variability of the
adolescent brain, as well as note the importance to bridge this gap in theoretical understanding (Kadosh et al., 2014; Urbain et al., 2017). This paired with the practical importance of understanding emotion regulation in the
adolescent population makes research in this field a worthwhile pursuit.
The present research thus aims to begin mapping how the adolescent brain processes emotional stimuli in the context of inhibition by focusing on detecting markers for emotion regulation. This will allow for a comparison between known markers for child processing of emotion
regulation, as well as a comparison with a control sample of adults. As the previously mentioned problems of doing this type of research (e.g. high variability and fast change in neural processing) in an adolescent population are well documented (Kadosh et al., 2014; Urbain et al., 2017), this research aims to create a multidisciplinary approach that uses state of the art psychometric tools to collect information in individual
differences as well as pubertal status. This should allow for a more in depth understanding of the neuroscientific results and create a theoretical bridge between the neuroscientific and social psychology literature on emotion regulation. This aims to answer the question whether specific neural markers exist for emotion regulation in adolescents, and whether they are distinguishable from adults. A secondary aim is to create a
paradigm that can be used for a larger study set to research emotion regulation in adolescents in combination with other psychometric tools, combining different fields of research.
2. MATERIALS AND METHODS
2.1. PARTICIPANTSAll participants were recruited through word of mouth, as well as posters placed on multiple locations throughout the University of Jyväskylä public spaces. 5 adolescents (3 female) spanning the age range 15-17 (mean: 16.2) were recruited for the study. 5 adult subjects (3 female, mean age: 28.2) were matched to act as a control group. All participants had normal vision and did not suffer from psychiatric or neurological disorders. The study was approved by the University Ethics Committee of the University of Jyvaskyla.
Participants, as well as their legal guardians in the adolescent population, were asked to fill in informed consent forms prior to experimental proceedings. Participants were fully debriefed after participation. Participants were rewarded a single voucher for a local movie theater for their participation.
2.2. EXPERIMENTAL MEG TASK AND PROCEDURE
All participants performed a modified Go/No-go task based on a previously used paradigm by Urbain et al. (2016). A modified version of the task was created using presentation© software (Version 20.1, Neurobehavioral Systems, Inc., Berkeley, CA, www.neurobs.com) and was presented using an older but compatible version of presentation (e.g. version 18.0, for a model of the experiment see figure 2). All modifications to the original task designed by Urbain et al. were done with the subjects’ age group in mind. Participants were presented with a screen where inter stimulus fixation crosses were interspersed with trials during which participants would see a picture of a face with a thick border frame that was colored blue or purple. Participants were asked to press a trigger-box button on all trials where the border color was purple, whereas they had to withhold their response on the blue border trials (image). Participants were explicitly told to
Figure 2: Model of experimental subsets. A) an example of the neutral images used in the study are visible in top two quadrants, whereas the angry pictures are seen in the bottom half. B) shows a single experimental trail, where images were shown without language cue, which were added for clarity. Each visual stimulus was followed by a fixation cross presented for 1000ms with a
focus their attentional resources on peripheral vision while maintaining eyesight on the center fixation point, as well as to focus on task accuracy and speed. Note the task irrelevance of the facial pictures. Facial pictures were divided into two emotional states, neutral and angry. Initial conceptions of the task contained a third
emotional state (fear), but this was taken out prior to data collection due to time constraints as well as considerations of ecological validity. This results in a two by two design, with task-color and emotion as independent factors.
All facial images were presented for 500ms, with a jittered ISI of 1000-1400ms, with a total trial time of 1500-1900ms. All participants were able to practice the task inside the MEG before active participation, to ensure they fully understood the task and experimental setting. All participants reported understanding the task after one set of practice trials. Participants were subsequently told the experiment was about to start.
Participants performed two blocks of 222 trials, with a pause in between that was ended at the request of the participants. Each block consisted of 104 trials with angry faces, and 118 trials of neutral faces, both divided equally between Go and No-go trials. This meant that each block contained exactly one full set of exhaustive task-color and emotion combinations without a combination appearing twice within the same block. This was done to avoid participant
familiarity to the images. All trials were presented in absolute randomized order within each block. Each block lasted approximately seven and a half minutes.
After the second block of trials, participants were asked to complete two blocks of resting state measurements, one with eyes open and one with eyes closed. Both resting state measurements were taken for a total of four minutes. This made the total scanning time approximately twenty-five minutes long.
All images were taken from the open sourced NIMH-chEFS data-set of child faces (Egger et al., 2011). This data-set consists of pictures of
adolescent children (age-range 10-17) in a host of emotional states. This data-set has been used in multiple publications and was chosen because of its experimental validity. An image set of children was chosen because of the expected improved effect strength of emotion regulation in line with findings by Urbain et al. (2016).
All images were presented approximately 120cm from the participants faces, projected on a white screen. The visual angle of the stimulus was arced at approximately 6 degrees of the straight visual field.
No participants fell under the 95% accuracy cut off decided before testing, and all failed trials were considered null and not taken into consideration in the analysis.
After MEG data collection participants were asked to fill in several questionnaires that measured a host of psychometric data on temperament, these unfortunately fall outside of the scope of the current paper. Possible uses of such psychometric data are elaborated on in the discussion section. 2.3. MEG DATA ACQUISITION
All data was collected on an Elekta Neuromag MEG system available at the Centre for Interdisciplinary Brain Research (CIBR) at the University of Jyväskylä. No MRI scans were collected due to time constraints as well as monetary constraints. MEG data was recorded continuously at 1000 Hz sampling rate, with a 0.33 to 200 Hz bandpass filter. To increase localization accuracy, three cardinal points were digitalized before measuring by use of the Elektra digitalization system, these points were
consistently taken at the nasion, and the right and left pre-auricular points. Head-movement
Positioning Indicator (HPI) coils were used to track movement during scanning procedure. These were set at continuous measurement (HPIc) throughout the experimental proceeding to correct for any movement.
2.4. HANDELING OF BEHAVIORAL DATA No in-depth analysis was done at this point in time on the behavioral results in the experiment. Error trials were removed from any MEG analysis, and the reaction time to Go trials was measured to be ~412ms after presentation of the stimulus on average. No further subcategorization based on reaction times was done to investigate differences between age groups, or differences between trials where different emotions were displayed. This was done since Go trials did not relate to emotion regulation, i.e. inhibition of a motor response, and were thus outside of the scope of this article. 2.5. MEG ANALYSES
Data analysis was performed using the inhouse Meggie software, created by CIBR at the University of Jyväskylä. Meggie is a GUI overlay build on python, using mne-python as source code for all data-transformation tools and analysis. Source reconstruction was unfortunately outside of the scope of the current pilot research, and further analysis will have to be conducted in order to trace the exact origin of the signal. All chosen analyses were based on analyses previously performed in the literature (Mocaiber et al., 2010; Urbain et al., 2017) coupled with a visual
inspection of the data.
Finally, all data analyses were performed with a separation of hemispheres, due to the well-known hemispherical dominance in children (Braniecki, Elliott, & Gaillard, 2010; Cutts, Maguire, Leenders, & Spyrou, 2004), as well as the fact that facial processing is known to be a lateralized process (Calvo & Beltrán, 2014; Meng, Cherian, Singal, & Sinha, 2012). This was chosen to give insight into the developmental process that occurs in emotion regulation processing from children to adults, where hemispherical dominance decreases. Data analyses were performed between two contrasts, Go vs No-go, and neutral No-go vs angry No-go. The former was performed to determine the successful manipulation attempted by the standard Go/No-go paradigm, whereas the latter
was performed specifically to determine markers for emotion regulation.
2.5.1. PRE-PROCESSING STEPS
MEG data was first MaxFiltered using the Elekta maxfiltering tool. Temporal Signal-Space Seperation (tSSS) was applied on the data using the HPIc data to correct for head-movement together with any extraneous noise components. MEG data was then band-pass filtered at 1-40 Hz, and an individual component analysis (ICA) was performed to determine cardiac and eye blink components. Each participant had a single component from the ICA removed for both heart rate and eyeblink.
Concurrently, the continuous data was epoched with respect to the trial trigger, with a 200ms window before trigger to show baseline and a 500ms window for termination of the stimulus to determine the after-effects typical in inhibitory control tasks (figure 3). This created a total epoch time of 1200ms. Averages were then created for each experimental condition, concatenating the information of all trials into a four 1200ms averaged epochs representative of the four experimental conditions: go/neutral, no-go/neutral, go/angry, no-go/angry.
2.5.2. AMPLITUDE AND LATENCY ANALYSIS
Averaged epochs were divided into left and right hemispherical subsets. Occipital gradiometers were then selected for the analysis, taken from the universal MEG 306 channel overlay (figure 4). Occipital channels were selected based on
previous findings in the literature (Bayle & Taylor, 2010; Urbain et al., 2017; Wessing et al., 2013). Gradiometers were changed into absolute values
Figure 3: Model of the epoch creation, red is the pre-epoch period before trial onset. Green is the period where the stimulus was present to the participants.
and averaged into a single value to control for possible current orientation differences between subjects. This resulted into a 1200ms epoched occipital response for all conditions for each participant for both hemispheres.
These epochs were then averaged over all participants to determine the time-frames of interest on which latency and amplitude analyses could be run (Graph 1 & 2). A single time-frame division was chosen for all conditions, as a result of largely overlapping time-frames. Small
adjustments to account for peak responses falling just outside of the chosen timeframes were applied before final analysis. The chosen
timeframes in order were: 0-150ms, 150-250ms, 250-450ms, 450-700ms, 700-1000ms (figure). Time-frames were determined through visual inspection of the data.
Peak amplitude and latencies were then determined for each participant for each time frame under all conditions. These peak amplitude and latencies were then entered into SPSS software (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.) to determine whether any effects had been found.
Ten ANOVA’s were performed using group (adult vs adolescent) as an in between factor, and hemisphere and contrast (i.e. task or emotion) as within subject factors. The ANOVA differed in time-frames, as well as focus on Go/No-go
differences and emotion regulation differences, so the final design was a two by five factorial. Both latency and amplitude were taken as independent factors in the analyses, each independently for left and right hemisphere respectively.
Figure 4: Elekta Neuromag MEG channel positions. The yellow and orange channels were taken into consideration for the
Graph 2: Averaged time-activation graph of all subjects for the task contrast. These were used to determine the time-frames used in the analysis.
Graph 1: Averaged activation graph of all subjects for the emotion contrast. These were used to determine the time-frames used in the analysis.
3. RESULTS
All results will be discussed in terms of time-frame, where each time-frame will discuss the result for both the Go/No-go contrast (henceforth task contrast) as well as the emotion regulation contrast (henceforth emotion contrast). While significant results will be discussed accordingly, this section will also mention near-significant results. While these results should not be taken into consideration for any theoretical
understanding, they can be taken as points of interest for future attempts at using this paradigm where a larger sample is possible. This is justified as the current research was a mere pilot to a larger study that will be conducted next year. All results where p < 0.1 were taken into
consideration. 0-150MS
No significant findings were found for the first time-frame which contained the initial neural response to a visual stimulus. This is true for both task contrast and emotion contrast (figure 5).
A near significant effect (p=.056) was found for the amplitude signal interaction of
group*hemisphere for the task contrast. This
5 5.5 6 6.5 7 7.5 Left Right Es tima te d M ar gin al M ea n Hemisphere
0-150ms amplitude peaks
Adult Go Adults No-go Adolescent Go Adolescent No-go
Figure 5: Results for the 0-150ms time-frame for amplitude in the task contrast
Graph 3: Example of single subject data and the chosen timeframe. Each peak was determined within the time-frames, which obtained both a peak amplitude value and a latency to that peak value.
would be in line with the literature, as adolescent participants showed more hemispherical
dominance compared to the adult controls. No near significant effects were found for the task contrast.
150-250MS
No significant findings were found for the second timeframe, which theoretically should contain the further processing of visual stimuli, and more specifically visual face processing. This is true for both task contrast and emotion contrast. A near-significant effect (p=.067) was found for the latency interaction effect of task * hemisphere * group. This effect seems to have been driven by a late left hemispherical latency in children compared to adults, which was especially true in the No-go task.
250-450MS
No significant effects were found for the third timeframe, which theoretically should contain the processing of action determination (i.e. motor response or inhibition response). This is true for both task contrast and emotion contrast.
A near-significant effect (p=.087) was found for the amplitude difference between Go and No-go trials. This conforms to the literature, where inhibition responses generally occur at the ~400ms latency. Furthermore, a near-significant effect (p=.082, figure 6) was found for angry and neutral differences in the emotion contrast. Finally, a near-significant effect (p=.064) was found for hemispherical differences in the
emotion contrast, where the right hemisphere was generally more dominant than the left
hemisphere. 450-700MS
A significant effect (p=.001) was found for differences between Go and No-go trials in the task contrast (figure 7). This was expected as this timeframe generally contains either the motor response or the active inhibition response. A stronger neural response was found for the Go trials, corresponding to the motor response. No significant findings were found for the emotion contrast.
A near-significant effect (p=.077) was found for the latency difference between Go and No-go trials in the task contrast. This is similarly associated
2.5 3 3.5 4 4.5 5 Left Right Es tima te d M ar gin al M ea n Hemisphere
250-450ms amplitude
peaks
Adult Go Adults No-go Adolescent Go Adolescent No-go
Figure 6: Results for the 250-450ms time-frame for amplitude in the task contrast
1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 Left Right Es tima te d M ar gin al M ea n Hemisphere
450-700ms amplitude
peaks
Adult Go Adults No-go Adolescent Go Adolescent No-go
Figure 7: Results for the 450-700ms time-frame for
with the different processes occurring at this time-frame between active motor responses and passive inhibitory responses. No near-significant effects were found for the emotion contrast. 700-1000MS
A significant effect (p=.003, figure 8) was found for the amplitude difference between go and no-go trials in the task contrast. The no-go trials had a significantly larger neural response in this timeframe, corresponding with a delayed inhibitory response. Furthermore, a significant effect (p=.048, figure 9) was found for the difference in amplitude between emotion regulation and normal inhibition trials in the emotion contrast. A stronger response was found for the normal inhibition trials.
A near-significant effect (p=.077) was found for the interaction of group*emotion in the emotion contrast. This corresponds very clearly to the found significant main effect of emotion being driven by the adult population (figure 9).
4. DISCUSSION
The aim of this study was to find a set of neurometric markers for emotion regulation in adolescents. A secondary aim was to create a paradigm that could be used for a larger study set to research emotion regulation in adolescents in combination with other psychometric tools, combining different fields of research.
The results do not strongly support the existence of neurometric markers for emotion regulation in the adolescent population. While a significant effect was found for emotional images in No-go trials compared to neutral No-go trial in the 700-1000ms time frame, distinguishing emotion regulation from normal inhibitory control - which would be in line with the literature (Langeslag & Van Strien, 2013; Mocaiber et al., 2010; Urbain et al., 2017) - results show that this effect was largely driven by the adult population in the study (figure 9). This does suggest, however, that there is a gradual change across adolescents where this effect develops. 1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3 3.5 Left Right Es tima te d M ar gin al M ea n Hemisphere
700-1000ms amplitude
peaks
Adult Go Adults No-go Adolescent Go Adolescent No-go
Figure 8: Results for the 700-1000ms time-frame for amplitude in the task contrast
Figure 9: Results for the 700-1000ms time-frame for amplitude in the emotion contrast
3 3.2 3.4 3.6 3.8 Left Right Es tima te d M ar gina l M ea n Hemisphere
700-1000ms amplitude
peaks
Adult Angry Adult Neutral Adolescent Angry Adolescent NeutralWhile the number of participants in the study was too low to find any effects with smaller effect sizes, results for the task contrast show the robustness of the findings, largely conforming to literature standards (Shafritz et al., 2006; Urbain et al., 2017; Yuan et al., 2011).
Previous findings in the literature, like the larger early visual response (M100) for emotional image No-go trials compared to neutral image No-go trials found in Urbain et al. (2016), were not found in the current study. While it is highly likely this may be due to the low participants number, it could also be attributed to the method of analysis. No source reconstruction analysis was performed on the data due to time constraints. This type of analysis could lead to slightly different results and would give further insight into the origin of the signal.
A further insight that might be derived from the current study, though no conclusive statement should be made due to a lack of significant results, is the strong hemispherical dominance in
adolescents, which seems to mitigate the effect of emotion regulation differences. Hemispherical dominance in children is a well attested
phenomenon (Braniecki et al., 2010; Cutts et al., 2004), but the current results show the possibility of a relation between hemispherical dominance and differences in processing of emotion regulation.
In addition, the results of the current study show the importance of further research into emotion regulation in adolescents, as the found effects on emotion regulation were largely driven by the adult controls, whom showed clear differences in processing to the adolescent sample. This highlights the relative gap in theoretical
understanding of this phenomenon. The value of further insights into this domain is high, as inhibitory control issues in adolescence are well documented (Diamond, 2013; Hirvonen et al., 2017; Urbain et al., 2017).
Further value could be derived from pairing a larger sample size with psychometric testing in the current study. Since it seems that emotion
regulation processing changes during adolescence, it would be interesting to measure relative
maturity in the adolescent population and plot this against the employed modes of processing. The current study did measure pubertal status in the post-experiment questionnaires, however, no clear results emerged from this set of data. A visual inspection of the differences within the adolescent sample, however, showed small differences in modes of processing, where older adolescents (age 17) had a stronger amplitude difference between emotion regulation trials and inhibitory control trials compared to younger adolescents (age 15). This was somewhat mitigated in a single adolescent of age 17, who scored relatively low on pubertal status. This thus indicating the pubertal status, rather than age per se, could be the defining feature that pairs with changes in emotion regulation processing. No statistical testing was performed on these results, as they involved differences between single subject. To give more insight into possible explanations for the difference in emotion regulation processing between adults and children, a larger sample would need to be tested and paired with psychometric tests.
Another interesting psychometric addition to the current study would be to measure personality traits and pair them with the neurometric data. Research into this combination could give insight into possible effects of emotion regulation on academic performance. Research into
temperament style and academic performance has shown clear effects of inhibitory control and negative emotion control, which could point towards emotion regulation processing problems as a key neurological conduit. Research into this relation could lead to improved intervention for at risk adolescents, as well as improved knowledge into the complex relational function emotion regulation plays. The current study attempted to start this process by measuring basic
temperament style from a host of questionnaires after the experimental proceedings.
Unfortunately, no clear differences between subjects were found, and all subjects fell within the same temperament group: resilient (Hirvonen
et al., 2017). This did not allow for any analysis on the effects of temperament style. This type of research suffers strongly from small samples and would need a diverse and large subject pool. Finally, the current study might suffer from participant selection bias in the adult population. While the adolescent population were selected from individuals that signed up to participate in the study, and were unknown to the
experimenters, the adult population was both familiar to the experimenters, and almost exclusively selected from the local fencing club. This could bias the results, and the adult controls in a larger study should be taken as a random sample of the population to be able to generalize any found results.
5. CONCLUSION
No strong evidence was found for markers of emotion regulation in adolescents. A difference was found in processing of emotion regulation trials between the adult control group and the adolescent sample, pointing towards a distinctive change that occurs at around the age-group of interest. While the study suffered from a small sample size, the results are conforming to the literature, and a strong inhibition component was present in the data.
This research highlights the need to increase our understanding of emotion regulation, a specific form of inhibitory control, in adolescent
populations, as academic achievement is known to be linked to successful inhibitory control and affective control. Increased understanding of the neurological processes that occur in emotion regulation, especially the changes that occur in adolescence, would lead to better understanding of the relationship between academic results and inhibitory control.
This knowledge should be paired with a relational model to personality differences, as well as pubertal status, in order to better comprehend the changes that occur over the course of this period, as well as the individual differences and their effects on academic performance.
Adolescents around the age of sixteen have two main foci, academic responsibilities and social needs. These two interests do not have to be at odds with one another yet do sometimes battle for attentional resources. In these moments,
successful emotion regulation is paramount to successful fulfilment of the responsibilities that adolescents have. It is therefore important that we better understand what constitutes successful emotion regulation, to better create situations for adolescents to thrive and achieve academic success, as well as control and correct for individuals who still have not developed mature emotion regulation processing.
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