https://orcid.org/0000-0002-9005-5026
Figures
Abstract
Aberrant reward processing and poor self-regulation have a crucial role in the development of several adverse outcomes in youth, including mental health disorders and risky behaviours. This scoping review aims to map and summarise the evidence for links between aspects and measures of reward processing and self-regulation among children and adolescents in the general population. Specifically, it examined the direct associations between self-regulation (emotional or cognitive regulation) and reward processing. Studies were included if participants were <18 years and representative of the general population. Quantitative measures were used for self-regulation, and gambling tasks were used for reward processing. Of the eighteen studies included only two were longitudinal. Overall, the direction of the significant relationships identified depended on the gambling task used and the self-regulation aspect explored. Emotional regulation was measured with self-report questionnaires only, and was the aspect with the most significant associations. Conversely, cognitive regulation was mainly assessed with cognitive assessments, and most associations with reward processing were non-significant, particularly when the cognitive regulation aspects included planning and organisational skills. Nonetheless, there was some evidence of associations with attention, cognitive control, and overall executive functioning. More longitudinal research is needed to draw accurate conclusions on the direction of the association between self-regulation and reward processing.
Citation: Bentivegna F, Papachristou E, Flouri E (2024) A scoping review on self-regulation and reward processing measured with gambling tasks: Evidence from the general youth population. PLoS ONE 19(4): e0301539. https://doi.org/10.1371/journal.pone.0301539
Editor: Akitoshi Ogawa, Juntendo University, JAPAN
Received: April 21, 2023; Accepted: March 17, 2024; Published: April 4, 2024
Copyright: © 2024 Bentivegna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Because this is a scoping review, all the data used for this review can be found in the studies that have been included in this review and that are listed in our manuscript. No other data has been used for the synthesis of the findings.
Funding: This study was funded by a PhD studentship to FB from the Medical Research Council (https://www.ukri.org/councils/mrc/) MR/N013867/1. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
It has been suggested that self-regulation, including regulation of both emotions and cognition, might be strongly linked to reward processing. Often described as a “hot” aspect of executive functioning [1], reward processing is related conceptually to decision-making and impulsivity. Decision-making is a strategic process of choice under risky conditions, and in decision neuroscience it is commonly measured using gambling tasks [2–4]. Because these tasks assess decision-making in conditions of uncertainty (which is not always the case for other reinforcement learning tasks), they are purported to mimic decision-making that would occur in “real-life”, where the potential benefits of a decision are balanced against its potential risks, i.e., reward processing. Importantly, they measure several aspects of decision-making and reward processing. One example is the Cambridge Gambling Task (CGT) [4], a gambling task from the Cambridge Neuropsychological Test Automated Battery (CANTAB), which produces six outcomes representing different aspects of decision-making: delay aversion, deliberation time, risk-taking, risk adjustment, quality of decision-making, and overall proportion bet. Given both their ability to assess reward processing under uncertainty and their focus on different aspects of decision-making, gambling tasks are effective tools that can be employed to identify processes taking place when making a decision and evaluating the risks and benefits of obtaining a reward. Of note, these tasks differ somewhat in what they specifically measure. For instance, compared to the CGT, the Balloon Analogue Risk Task (BART) [5] is considered a measure of risk-taking behaviour and as such it mainly focuses on aspects such as risk adjustment, risk tolerance, and overall willingness to take risks. On the other hand, the Iowa Gambling Task (IGT) [6] simulates uncertain gains and losses in real life situations and thus is a good measure of uncertain decision-making, as the participant is expected to progressively learn what situation is the most advantageous, and their outcome measures involve the net loss or gain deriving from their decisions throughout the task.
Defining and measuring self-regulation also has its challenges. Self-regulation in psychology is an umbrella term used to describe a set of abilities that allow individuals to manage and control their thoughts, emotions and behaviours in order to achieve a goal or adapt to a situation. Importantly, these abilities may vary widely depending on the researcher’s sub-field or the developmental phase of their sample. For instance, self-regulation is often referred to as temperament or ‘effortful control’ for very young children [7]. Instead, economics refer to self-regulation as ‘self-control’ with a focus on the ability of managing one’s behaviour. Finally, in biology and neuroscience it is common to define self-regulation as the neural and physiological processes responsible for managing and adjusting cognitive and emotional responses. More generally, self-regulation is commonly categorised as behavioural, emotional, and cognitive regulation, and, for each, both “cold” and “hot” executive functions may be involved. In line with a review critically discussing the several related constructs that researchers working in the broad field of self-regulation have examined [8], we decided to follow in this scoping review the definition of self-regulation as behavioural, emotional, and cognitive regulation, while excluding related but different aspects of cognitive control and its strong correlates such as working memory.
Many models of self-regulation, across psychology sub-fields, have been developed to elucidate how different self-regulation aspects may be intertwined [9]. Central in most however is the notion of a trade-off between an emotion-based system that drives people toward or away from action and a cool, calculating component that manages these emotion-based impulses. Thus, self-regulation and reward seeking seem closely linked [10]. Importantly, there is also empirical evidence from experimental studies suggesting that self-regulation not only manages such impulses but also affects the reward system directly [11], as self-regulatory depletion can increase impulses [12]. Research on emotion regulation particularly suggests a link of emotion regulation with impulsivity [13], and with the behavioural inhibition/reward system [14]. Similar results have been found in research using gambling tasks [15–18]. However, these studies were conducted with adults as participants, or with clinical samples of children, or with mixed-age samples without differentiation by developmental stage. It is not clear what the evidence on this topic is specifically in childhood and adolescence, a crucial developmental phase characterised by heightened reward-seeking behaviour. Additionally, findings from general population samples are particularly relevant as they apply to the population as a whole, rather than to special groups of people. Moreover, there is still confusion regarding the direction of this relationship, that is whether self-regulation predicts reward processing or the other way round. Understanding how such a relationship functions in childhood and adolescence could be fundamental for the prevention of adverse outcomes such as mental health problems and risky behaviours, or academic failure, all of which implicate both reward processing and self-regulation [19–21].
This scoping review therefore aimed to synthesise the current evidence on the role of self-regulation in reward processing (and vice versa) as assessed by gambling tasks, in general population samples of children and adolescents. In particular, this review has three objectives: i) to explore the existing literature on the associations between self-regulation and reward processing in samples of children and adolescents, and the gaps in this literature; ii) to assess the direction of these associations (does reward processing predict self-regulation, or does self-regulation predict reward processing?); and iii) to narratively map and summarise the findings with the aim of identifying links between specific aspects of self-regulation and specific aspects of reward processing.
Materials and methods
This scoping review followed the PRISMA guidelines for the reporting of scoping reviews ([22]; see S1 Table) and the protocol can be found on Open Science Framework (https://doi.org/10.17605/OSF.IO/QENK6).
Search method
We also followed the Joanna Briggs Institute’s Manual for Evidence Synthesis (https://synthesismanual.jbi.global) [23], which recommends using a three-step search strategy: first we identified pertinent keywords and index terms by searching two databases, i.e. Medline (Ovid) and Scopus, and the first 25 results were analysed and discussed with the research team to further refine the search; then, an updated search was conducted in those databases but also in Embase and PsycINFO (Ovid); finally, a hand-search of the reference lists of the included papers was conducted, and the authors of papers where the full-text was not available were contacted. All the searches were conducted from inception to November 2022 (as an example we provide the search strategy of one database: see S2 Table in S1 File).
Zotero (https://www.zotero.org/) was used throughout the screening process to store the citations and screen titles, abstracts, and full-texts of the retrieved papers.
Selection criteria
Participants.
We included studies that recruited samples of children and/or adolescents whose age was 18 or younger, and who were representative of the general population. Moreover, studies were deemed eligible if they used quantitative methods to assess self-regulation, including questionnaires and experimental tasks. However, studies were excluded if the sample included adult participants (or if a youth sub-sample analysis could not be conducted), and if they used clinical samples (including high-risk samples) or healthy matched samples that were specifically selected because of their “typical child/adolescent” status.
Concept.
Studies were eligible if they explored the associations between reward processing (including related aspects such as decision-making and risk-taking) and self-regulation. We excluded studies that did not use gambling tasks to measure reward processing, including reinforcement learning tasks and monetary incentive delay tasks, as they measure reward processing, but not necessarily under conditions of uncertainty or as a result of the ponderation of risks and benefits where an outcome is more probable than another. As previously mentioned, reward processing and aspects of behavioural self-regulation such as impulsivity and inhibition share some similarities, therefore we decided to only include emotional and cognitive self-regulation. However, behavioural self-regulation such as inhibition was still considered if it was part of a broader battery of measures and if the total score of such a battery of measures was estimated. Similarly, working memory was only included if the same conditions were met. Moreover, we excluded constructs such as temperament and effortful control because they are conceptually different from self-regulation.
Context.
We did not exclude any studies based on their study design or the country where they took place, though we excluded studies not written in English, studies that had not been peer-reviewed, and systematic reviews and meta-analyses.
In this review, we were only interested in the direct effect of reward processing on self-regulation and vice versa, hence we did also not consider the results of analyses of interaction (moderator) effects. We considered results as statistically significant when p-value was lower than .05.
Data extraction and synthesis
The data extraction template was tested and updated following discussion with the research team. We extracted data about the study and participants’ characteristics, including country of origin, study design, sample size, and participants’ age and sex; the main variables of interest and how they were measured, as well as the relevant confounders/covariates; and the main findings, including the significance, direction (positive, negative), and size of effects.
Given the scope of this review, we provide a narrative description of the findings. We begin by summarising the study and participants’ characteristics, followed by a description of the gambling tasks used. Next, we summarise the findings which we group for convenience by type of measure used for self-regulation (i.e. questionnaire or cognitive assessment), and we describe the measures. Finally, we investigate the relationship between reward processing and self-regulation by analysing separately the longitudinal and cross-sectional associations, and summarise findings across self-regulation aspects.
Results
Characteristics of included studies
Fig 1 illustrates the PRISMA flowchart summarising the search strategy [24]. First, we excluded 344 out of 427 studies following the screening of titles and abstracts; then, with the addition of 92 studies from the hand search of reference lists, the full-texts of 175 studies were screened; of those, 18 studies were eventually included in the review (see Table 1 for the detailed characteristics of the studies). The majority of studies included in this review were cross-sectional (n = 16), and only two examined longitudinal associations (time intervals between baseline assessment and last follow-up ranged between four and eight years). Some of the cross-sectional studies employed an experimental design. Most studies took place in the US (n = 6) and in Canada (n = 5), with the remaining in the UK (n = 2), Germany (n = 2), Argentina (n = 1), and China (n = 1). One study used international samples. More than half of the studies focused on childhood (0–12 years) (n = 11), while the remaining studies explored associations in both children and adolescents (n = 7); no study focused on adolescence (13–18 years) only. All studies recruited mixed-sex samples, with a slightly higher percentage of female participants overall. The sample sizes ranged from 33 to 11,303 participants, with a total number of 28,172 individuals.
* If a study was not available, its authors were contacted and the full-text was screened alongside the other included studies.
Gambling task types
Gambling tasks were generally administered as computerised assessments, unless the participants were very young, in which case they were administered as card games (S3A Table in S1 File). Specifically, the two types of gambling tasks administered as card games were the Children’s Gambling Task (n = 2) [1] and Preschool Gambling Task (PGT; n = 1) [25], which are age-appropriate versions of the Iowa Gambling Task (IGT) [6]. The latter is also the measure that was most frequently used (n = 5), followed by the Hungry Donkey Task (HDT; n = 3) [26], the CGT (n = 2), the Balloon Analogue Risk Task (BART; n = 2) [5] and the Bubblegum Analogue Risk Task for children (BART-C; n = 2) [27, 28], and the IGT for children (n = 1) [29], which are other age-appropriate versions of the BART and the IGT, respectively. Detailed descriptions for each gambling task and their different versions are reported in S3B Table in S1 File.
Self-regulation aspects and measures
In contrast to questionnaire report measures which clearly focused on specific aspects of self-regulation, such as emotional dysregulation and independence (cognitive) self-regulation, the outcome measures assessed by some of the cognitive assessments of the included studies were not always easily distinguishable (see Table 2). Therefore, we created different categories depending on the aspects of self-regulation indexed by each assessment. For instance, the Cognitive Flexibility Task [30] measures attention shifting, while the Contingency Naming Test [31] assessed both attentional control and cognitive flexibility. For this reason, we grouped all the assessments that measured attention, shifting and cognitive flexibility as one category. We followed a similar rationale for goal setting and planning ability, which are assessed by the Stockings of Cambridge (a variant of the Tower of London task from CANTAB, originally used by [32]), and other tasks measuring planning skills. Next, we grouped together those tasks that measured cognitive control, which in this review was defined as a combination of conceptually different constructs such as inhibition and attention (example of such tasks are the Stroop Task [33] and the Conners’ continuous performance test (CPT-II)) [34]. Finally, two studies measured attention in combination with inhibition and working memory, therefore, we grouped these studies under the ‘overall executive functioning’ category. For more details about the different measures see S4 Table in S1 File.
Self-report measures of self-regulation.
Only two studies investigated the relationship between reward processing and emotion regulation, and both studies used self-report questionnaires measuring emotional dysregulation, i.e. the Emotion Dysregulation Scale for children [35], which was modelled after the version for adults [36] and for older children [37], and the Child Social Behavior Questionnaire [38], developed by Hogan et al. (1992) [39]. The latter was also used to investigate cognitive regulation in the same study, specifically aimed at measuring independence self-regulation.
Cognitive assessments of self-regulation.
The majority of the cognitive assessments were administered using a computer, while the remaining measures were designed as experimental games using either cards or paper tasks, with some of these tasks being unvalidated and study-specific.
Most measures were used to assess attention, set-shifting and/or cognitive flexibility. The Dimensional Change Card Sort [40] from the NIH Toolbox was the most frequently reported measure (n = 3), followed by the Cognitive Flexibility Task [30] (n = 2), the Flanker Test of Focused Attention (n = 2) originally developed by [41], and the Trail Making Test [42] (n = 2). Other measures were used only once and included the Attentional Network task [43], the Wisconsin Card Sorting Test [44], the Shifting task adapted from the Preschool Executive Function Battery [45], the Contingency Naming Test [31], and the Battersea Multitask Paradigm [46]. The latter is also used to assess planning. Two additional measures were employed by two studies to assess planning and organisational skills: the Tower of London [47, 48] and the Stockings of Cambridge [49]. Cognitive control was assessed using the Stroop Color and Word Task [33] and other similar versions of the task, such as the Fruit Stroop Task [30, 50] and the Counting Stroop [51], by a third of the included studies. Instead, another measure of cognitive control, i.e. the CPT-II [34], was only employed by one study. Finally, overall executive functioning, which was treated as a single measure including attention, inhibition and working memory, was assessed as a combined single factor solution including scores for the Flanker Task, the List Sorting Working Memory Test (both available on the NIH Toolbox: https://www.healthmeasures.net/explore-measurement-systems/nih-toolbox/intro-to-nih-toolbox/cognition), and the Go/No-go Test; and as one single assessment called the Hearts and Flowers task [52]. However, given that Smith et al. (2012) used the part B of the Trail Making Test as a measure of set shifting, working memory, and inhibition combined [53] (compared to another study that described it as a measure of set-shifting only [29]; see S4 Table in S1 File), this measure was also deemed to be assessing overall executive functioning.
Association of self-regulation with reward-processing
The description of the associations between specific aspects of self-regulation and specific domains of reward processing (per gambling task used) is shown in Table 3. Overall, half of the papers found no associations between self-regulation and reward processing. Of these, one study had a longitudinal design [54], while nine papers, which also included experimental studies, reported cross-sectional results only [29, 47, 49, 53, 55–59]. The remaining papers found mixed results (n = 5) or significant results (n = 3).
It should be noted that we mostly reported only correlations for more than half of the included studies (n = 13). While these studies did also conduct regression analyses, these were used to test moderation effects, or to investigate behavioural aspects of self-regulation, hence falling outside the scope of this review. We discuss this further in the discussion section. The remaining papers (n = 5) included a combination of correlation analyses and ANOVA and regression analyses with different levels of adjustment for confounding.
Longitudinal results.
As previously mentioned, only two studies looked at longitudinal associations (see Table 4). The direction of this relationship was explored in the opposite manner in the two studies, with one study exploring the predictive effect of self-regulation on later reward processing [38], and the other study investigating whether, instead, reward processing is associated with later self-regulation [54]. Specifically, the first study focused on the associations between emotion and cognitive regulation at ages 3, 5 and 7 with different CGT domains at age 11, and controlled for gender, verbal ability, ethnicity, pubertal status, family poverty, and maternal mental health. The second examined the associations between affective decision-making measured with the HDT at age 8 and set-shifting and attention at age 12, adjusting for gender and verbal ability, but found no associations. Instead, mostly significant associations were found in the first study, particularly for emotion regulation. Specifically, latent growth curve models, which were used to estimate trajectories of self-regulation and focused on the intercept and slope of emotional dysregulation, showed significant associations for all the CGT variables at age 11 (all CGT variables except deliberation time: ranges b = -0.32 to 0.13, SE = 0.16 to 0.17, 95%CI -0.64 to 0.20, p < .01 to .05; deliberation time: b = 100.19, SE 48.68, 95%CI 4.72 to 195.65, p < .05), with the only exception being the non-significant association between the slope of emotional dysregulation and deliberation time. As for cognitive regulation, the latent growth curve models showed clear negative associations with risk-taking and delay aversion, but only the intercept of independence and self-regulation was associated negatively with deliberation time (b = -1146.93, SE = 252.24, 95%CI -1640.65 to -651.42, p < .01), and positively with quality of decision-making and risk adjustment (all CGT variables except deliberation time: ranges b = -0.20 to 0.36, SE = 0.00 to 0.17, 95%CI -0.28 to 0.70, p < .01 to .05).
Cross-sectional results.
Table 5 shows details of the cross-sectional associations. With the exception of one study that investigated emotion regulation (and found associations between emotion dysregulation and risk-taking measured by the BART, controlling for age and sex (b = 0.30, t = 2.43, p < .05)) [35], the remaining cross-sectional studies (n = 15) focused on the cognitive aspects of self-regulation and measured them using a range of cognitive assessments and tasks. Of these studies, less than half reported significant results (see also Table 3). In terms of self-regulation aspects, most studies focused on attention set-shifting and cognitive flexibility (n = 10), followed by cognitive control (n = 7), planning and organisational skills (n = 3) and overall executive functioning (n = 2).
Despite the absence of significant longitudinal associations and the majority of cross-sectional associations being non-significant [55–58], there was still some evidence of significance in the association between reward processing and attention, set-shifting and cognitive flexibility from four studies. One study [25] found positive correlations between shifting and specific aspects of decision-making measured by the PGT, i.e. the ‘learning index’ (resulting from the computation of the slope of the PGT across the five blocks of trials) showing whether and how much the children learnt to choose from advantageous decks (ranges r = 0.30 to 0.34, p < .01 to .05). Evidence was also found for the association between shifting and choosing advantageously on the PGT blocks when choices were driven by explicit knowledge (Block 4–5: t = 2.130 to 2.334, p < .05), which was the result of a multilevel analysis used to assess the effect of two different conditions (trial-focus vs. integrated-focus PGT variants). Groppe and Elsner (2015–2017) found similar results in two studies using the same data at baseline and one year later, and found positive correlations between attention shifting and decision-making measured by the HDT (r = 0.09 to 0.13, p < .01) [60, 61]. The final study used the BART-C to create three risk-taking clusters (risk-avoidant, reckless, adaptive risk-takers), and found that reckless participants had less attention accuracy than both risk-avoidant and adaptive risk-takers, while the risk-avoidant ones were slower compared to the adaptive risk-takers (p < .001 to < .05; effect sizes are not reported) [28].
All three studies exploring the associations between planning and/or organisational skills and reward processing failed to find significant associations. Specifically, no correlations were found between risk adjustment and delay aversion as measured by the CGT and planning ability [47], nor was one found between decision-making measured by the Children’s Gambling Task and planning ability after controlling for age, IQ, family income, and family education [49]. Moreover, no significant group differences (after controlling for age) were reported for children with high vs low sensitivity to punishment in relation to planning skills [29].
Most associations between cognitive control and reward processing were also non-significant. The two significant associations found by two different studies showed similar results. The first study found a negative correlation between cognitive inhibition and decision-making measured by the HDT (-0.08, p < .01) [61] while the second found a negative association between cognitive inhibition and decision-making measured by the IGT; however, the latter association was adjusted for age (-0.26, p < .01 two-tailed) [62].
As for overall executive functioning, two studies did not find evidence for a correlation between this and sensitivity to reward and loss [59] and decision-making [53] measured by the IGT, whereas another study found negative associations with adaptive risk-taking measured by the BART-C after controlling for grade (b = -0.081 to -0.059, p < .001 to < .01) [27].
Discussion
The aim of this scoping review was to narratively map and summarise the existing evidence on the associations between self-regulation and reward processing, measured with gambling tasks, in children and adolescents from the general population. The main gambling tasks used were the IGT, the HDT, the CGT, and the BART, or an adaptation of those measures. The vast majority of the studies used cognitive assessments to assess self-regulation, with attention, set-shifting and cognitive flexibility being the most commonly analysed aspects. A multitude of different tests was used to assess individual and combined self-regulation aspects, and the DCCS and the Stroop test were used more frequently than other assessments. By contrast, only two studies examined self-regulation using questionnaires, which were specific for emotional dysregulation and independence (cognitive) self-regulation.
Overall, most significant associations were detected in studies where self-regulation was assessed using questionnaires, and particularly in the case of emotion regulation. Specifically, positive associations were found for BART risk-taking and emotional dysregulation, and both positive and negative associations were found for early emotional dysregulation and early independence self-regulation and later CGT depending on the specific CGT aspect analysed. These results are not surprising in view of the link between emotional dysregulation and risk-taking behaviours [63]. Nonetheless, there is also some evidence for no associations between emotional dysregulation assessed with the Difficulties in Emotion Regulation Scale and the CGT [13]; however, this study was conducted in young adults, hence it might be possible that the association disappears over time.
As for self-regulation measured using cognitive assessments, the vast majority of the associations were non-significant. Interestingly, not a single significant association was found between reward processing and planning and organisational skills. One explanation for this could be that reward processing as ‘hot’ decision-making is more easily associated with ‘hot’ self-regulation, whereas cognitive regulation can be conceptualised as ‘cold’ self-regulation. This would also explain the high number of non-significant associations with cognitive assessments of self-regulation. As for the significant associations, this review found that overall executive functioning was negatively associated with adaptive risk-taking measured with the BART, and cognitive control was negatively associated with decision-making measured using the IGT and one of its child-friendly versions (HDT). Instead, the direction of the results for attention, set-shifting and cognitive flexibility was mixed. Specifically, significant positive associations were found for decision-making measured using the PGT and the HDT (both derived from the IGT), whereas one study [28] found negative associations for both risk-taking and risk-avoidance measured using the BART-C. However, it should be noted that the significance or not of the association, as well as its direction, was also dependent on the specific measures used to assess self-regulation. For instance, in the DCCS higher scores indicate better performance, whereas in the Stroop Task good performance is reflected by lower scores. Moreover, other factors might have an impact, such as the age of the sample and the adjustment or not for potentially relevant factors. For this reason, it was not possible to identify specific patterns of associations between self-regulation and reward processing.
Specifically, this review highlighted a number of difficulties linked to this specific topic. First, given that the majority of the findings coming from correlations, most findings were not particularly informative in terms of temporality and potential confounding. Moreover, only two studies focused on emotional regulation, hence caution should be exercised when drawing conclusions about links between reward processing and emotional regulation. Third, as discussed, self-regulation has been variously described, and this is reflected in the lack of clarity regarding what self-regulation measures actually assesses. For instance, the Stroop task measures cognitive inhibition or control, but it has also been used to assess attention and cognitive flexibility [64]. It is also noteworthy that most significant associations were detected in studies measuring self-regulation by questionnaires, rather than by cognitive assessments. One reason for this could be that the questionnaires used were able to index broad rather than narrow skills.
There were some limitations in this scoping review. The number of retrieved studies was relatively small. This is because we had to exclude a considerable number of studies that explored behavioural aspects of self-regulation. As mentioned before, such behavioural aspects overlap in content with reward processing, hence we deemed that the inclusion of these aspects would undermine the validity of our conclusions. Related to this, we acknowledge that some of the gambling tasks used in this review have their own limitations in terms of their validity and reliability (for instance, see Schmitz et al., 2020 [65]). Additionally, due to the vast majority of the non-experimental studies being cross-sectional, it was not possible to determine the temporality of the association between self-regulation and reward processing. Given that of the two studies exploring these associations longitudinally and in opposing directions, only the one analysing the association between early self-regulation and later reward processing found significant associations [38], it could be suggested that self-regulation is a predictor of subsequent reward processing; still, one limitation of this study is that bidirectionality was not examined. Therefore, more longitudinal research is needed to help disentangle the direction of this relationship. This review was also partially limited by the non-availability of precise estimates for some of the reported results; however, this was the case for a very small number of studies. Moreover, the quality of the studies was not assessed because this is outside the scope of scoping reviews. Another limitation concerns the setting of studies, with the majority taking place in Western countries; hence, findings might not be generalisable to non-Western contexts. Finally, despite having explained the rationale behind our focus on gambling tasks to measure reward processing, we appreciate that only some aspects of reward processing can be captured by such tasks, and future reviews may want to explore the link between self-regulation and reward processing measured differently. For instance, measures of reward processing such as the Marshmallow Task [66] and the Game of Dice Task [67] where the risk is known could be very useful to include (for an overview of these measures see Romer and Khurana, 2022 [68]). Similarly, constructs such as intertemporal choice or delay discounting may offer new insights into the strategies used to make a decision and assess risk. Moreover, while tasks to assess reinforcement learning were excluded, they can be helpful tools to assess how risky decision-making can be optimised over time [69, 70].
In conclusion, this scoping review summarised the available literature on the link between self-regulation and reward processing in children and adolescents from the general population. We identified the main aspects of self-regulation as well as their measures, which were numerous and not always clearly definable. The high number of non-significant associations, particularly for cognitive assessments, suggests that some aspects of self-regulation might be better explored using self-report questionnaires, at least in the context of reward processing measured using gambling tasks. In particular, emotional dysregulation is the one aspect that appears to have a clear significant relationship with reward processing; however, more longitudinal research is required to understand the direction of this association. Moreover, our review calls for more consensus regarding the definition of self-regulation and its different aspects across psychology subfields. Given the role of both poor self-regulation and aberrant reward processing for a number of adverse outcomes, it is crucial to understand how self-regulation and reward processing in youth coexist, likely co-develop and may mutually influence each other.
Supporting information
S1 Table. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.
https://doi.org/10.1371/journal.pone.0301539.s001
(DOCX)
References
- 1. Kerr A, Zelazo PD. Development of “hot” executive function: The children’s gambling task. Brain Cogn. 2004;55: 148–157. pmid:15134849
- 2. Buelow MT, Suhr JA. Construct validity of the Iowa gambling task. Neuropsychol Rev. 2009;19: 102–114. pmid:19194801
- 3. Kvam PD, Romeu RJ, Turner BM, Vassileva J, Busemeyer JR. Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks. Psychol Methods. 2020. pmid:32134313
- 4. Rogers RD, Owen AM, Middleton HC, Williams EJ, Pickard JD, Sahakian BJ, et al. Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex. J Neurosci. 1999;19: 9029–9038. pmid:10516320
- 5. Lejuez CW, Richards JB, Read JP, Kahler CW, Ramsey SE, Stuart GL, et al. Evaluation of a behavioral measure of risk taking: The balloon analogue risk task (BART). J Exp Psychol Appl. 2002;8: 75–84. pmid:12075692
- 6. Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50: 7–15. pmid:8039375
- 7. Rothbart MK, Sheese BE, Posner MI. Executive Attention and Effortful Control: Linking Temperament, Brain Networks, and Genes. Child Dev Perspect. 2007;1: 2–7.
- 8. Nigg JT. Annual Research Review: On the relations among self-regulation, self-control, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology. J Child Psychol Psychiatry. 2017;58: 361–383. pmid:28035675
- 9. Inzlicht M, Werner KM, Briskin JL, Roberts BW. Integrating models of self-regulation. Annu Rev Psychol. 2021;72: 319–345. pmid:33017559
- 10. Steinberg L, Albert D, Cauffman E, Banich M, Graham S, Woolard J. Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: Evidence for a dual systems model. Dev Psychol. 2008;44: 1764–1778. pmid:18999337
- 11. Baumeister S, Wolf I, Hohmann S, Holz N, Boecker-Schlier R, Banaschewski T, et al. The impact of successful learning of self-regulation on reward processing in children with ADHD using fMRI. ADHD Atten Deficit Hyperact Disord. 2019;11: 31–45. pmid:30225805
- 12. Wagner DD, Altman M, Boswell RG, Kelley WM, Heatherton TF. Self-regulatory depletion enhances neural responses to rewards and impairs top-down control. Psychol Sci. 2013;24: 2262–2271. pmid:24026225
- 13. Schreiber LR, Grant JE, Odlaug BL. Emotion regulation and impulsivity in young adults. J Psychiatr Res. 2012;46: 651–658. pmid:22385661
- 14. Markarian SA, Pickett SM, Deveson DF, Kanona BB. A model of BIS/BAS sensitivity, emotion regulation difficulties, and depression, anxiety, and stress symptoms in relation to sleep quality. Psychiatry Res. 2013;210: 281–286. pmid:23845417
- 15. Blair MA, Moyett A, Bato AA, DeRosse P, Karlsgodt KH. The Role of Executive Function in Adolescent Adaptive Risk-Taking on the Balloon Analogue Risk Task. Dev Neuropsychol. 2018;43: 566–580. pmid:30160534
- 16. Duell N, Steinberg L, Chein J, Al-Hassan SM, Bacchini D, Lei C, et al. Interaction of reward seeking and self-regulation in the prediction of risk taking: A cross-national test of the dual systems model. Dev Psychol. 2016;52: 1593–1605. pmid:27598251
- 17. Hanson KL, Thayer RE, Tapert SF. Adolescent marijuana users have elevated risk-taking on the balloon analog risk task. J Psychopharmacol Oxf Engl. 2014;28: 1080–7. pmid:25237125
- 18. Heilman RM, Crişan LG, Houser D, Miclea M, Miu AC. Emotion Regulation and Decision Making Under Risk and Uncertainty. Emotion. 2010;10: 257–265. pmid:20364902
- 19. Garland EL, Bryan CJ, Nakamura Y, Froeliger B, Howard MO. Deficits in autonomic indices of emotion regulation and reward processing associated with prescription opioid use and misuse. Psychopharmacology (Berl). 2017;234: 621–629. pmid:27933366
- 20. Ning HK, Downing K. The reciprocal relationship between motivation and self-regulation: A longitudinal study on academic performance. Learn Individ Differ. 2010;20: 682–686.
- 21. Sawyer AC, Miller-Lewis LR, Searle AK, Sawyer MG, Lynch JW. Is greater improvement in early self-regulation associated with fewer behavioral problems later in childhood? Dev Psychol. 2015;51: 1740. pmid:26501724
- 22. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169: 467–473. pmid:30178033
- 23. Peters M, Godfrey C, McInerney P, Munn Z, Tricco A, Khalil H. Chapter 11: Scoping reviews (2020 version). JBI Man Evid Synth JBI 2020. 2020.
- 24. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 2021;88: 105906. pmid:33789826
- 25. Garon N., Hecker O., Kwan A., Crocker T.A., English S.D. Integrated versus trial specific focus improves decision-making in older preschoolers. Child Neuropsychol. 2022. pmid:35430949
- 26. Crone EA, van der Molen MW. Developmental changes in real life decision making: Performance on a gambling task previously shown to depend on the ventromedial prefrontal cortex. Dev Neuropsychol. 2004;25: 251–279. pmid:15147999
- 27. Bell MD, Imal AE, Pittman B, Jin G, Wexler BE. The development of adaptive risk taking and the role of executive functions in a large sample of school-age boys and girls. Trends Neurosci Educ. 2019;17: 100120. pmid:31685124
- 28. Imal A.E., O’leary S., Wexler B.E. Risk-taking patterns of children, associated cognitive weaknesses, and prevention of negative outcomes. Psychiatr Res Clin Pract. 2020;2: 34–40. pmid:36101890
- 29. Gonzalez-Gadea ML, Scheres A, Tobon CA, Damm J, Baez S, Huepe D, et al. Stop Saying That It Is Wrong! Psychophysiological, Cognitive, and Metacognitive Markers of Children’s Sensitivity to Punishment. PloS One. 2015;10: e0133683. pmid:26218584
- 30. Roebers CM, Röthlisberger M, Cimeli P, Michel E, Neuenschwander R. School enrolment and executive functioning: A longitudinal perspective on developmental changes, the influence of learning context, and the prediction of pre-academic skills. Eur J Dev Psychol. 2011;8: 526–540.
- 31. Taylor HG, Mills EL, Ciampi A, du Berger R, Watters GV, Gold R, et al. The sequelae of Haemophilus influenzae meningitis in school-age children. N Engl J Med. 1990;323: 1657–1663. pmid:2233963
- 32. Hughes C, Russell J, Robbins TW. Evidence for executive dysfunction in autism. Neuropsychologia. 1994;32: 477–492. pmid:8047253
- 33. Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18: 643.
- 34. Conners K. Technical guide and software manual. North Tonawanda NY Multi Health Syst Conners’ Contin Perform Test CPT-II. 2002; 43–52.
- 35. Morrongiello BA, Kane A, McArthur BA, Bell M. Physical risk taking in elementary-school children: Measurement and emotion regulation issues. Personal Individ Differ. 2012;52: 492–496.
- 36. Gratz KL, Roemer L. Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the difficulties in emotion regulation scale. J Psychopathol Behav Assess. 2004;26: 41–54.
- 37. Neumann A, van Lier PA, Gratz KL, Koot HM. Multidimensional assessment of emotion regulation difficulties in adolescents using the difficulties in emotion regulation scale. Assessment. 2010;17: 138–149. pmid:19915198
- 38. Francesconi M, Flouri E, Harrison A. Decision-making difficulties mediate the association between poor emotion regulation and eating disorder symptoms in adolescence. Psychol Med. 2022; 1–10. pmid:35227340
- 39. Hogan AE, Scott KG, Bauer CR. The Adaptive Social Behavior Inventory (ASBI): A new assessment of social competence in high-risk three-year-olds. J Psychoeduc Assess. 1992;10: 230–239.
- 40. Zelazo PD. The Dimensional Change Card Sort (DCCS): A method of assessing executive function in children. Nat Protoc. 2006;1: 297–301. pmid:17406248
- 41. Eriksen BA, Eriksen CW. Effects of noise letters upon the identification of a target letter in a nonsearch task. Percept Psychophys. 1974;16: 143–149.
- 42. Spreen O, Gaddes W. Development norms for 15 neuropsychological tests age 6 to 15. Cortex J Devoted Study Nerv Syst Behav. 1969.
- 43. Rueda MR, Fan J, McCandliss BD, Halparin JD, Gruber DB, Lercari LP, et al. Development of attentional networks in childhood. Neuropsychologia. 2004;42: 1029–1040. pmid:15093142
- 44. Heaton R, Chelune G, Talley J, Kay G, Curtiss G. Wisconsin card sorting test manual: revised and expanded. Psychol Assess Resour. 1993; 5–57.
- 45. Garon N, Smith IM, Bryson SE. A novel executive function battery for preschoolers: Sensitivity to age differences. Child Neuropsychol. 2014;20: 713–736. pmid:24295496
- 46. Mackinlay R, Charman T, Karmiloff-Smith A. High functioning children with autism spectrum disorder: A novel test of multitasking. Brain Cogn. 2006;61: 14–24. pmid:16455173
- 47. Poland SE, Monks CP, Tsermentseli S. Cool and hot executive function as predictors of aggression in early childhood: Differentiating between the function and form of aggression. Br J Dev Psychol. 2016;34: 181–197. pmid:26615980
- 48. Shallice T. Specific impairments of planning. Philos Trans R Soc Lond B Biol Sci. 1982;298: 199–209. pmid:6125971
- 49. Poon K. Hot and cool executive functions in adolescence: Development and contributions to important developmental outcomes. Front Psychol. 2018;8. pmid:29367850
- 50. Archibald SJ, Kerns KA. Identification and description of new tests of executive functioning in children. Child Neuropsychol. 1999;5: 115–129.
- 51. Bush G, Whalen PJ, Rosen BR, Jenike MA, McInerney SC, Rauch SL. The counting Stroop: an interference task specialized for functional neuroimaging—validation study with functional MRI. Hum Brain Mapp. 1998;6: 270–282. pmid:9704265
- 52. Davidson MC, Amso D, Anderson LC, Diamond A. Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia. 2006;44: 2037–2078. pmid:16580701
- 53. Smith DG, Xiao L, Bechara A. Decision making in children and adolescents: impaired Iowa Gambling Task performance in early adolescence. Dev Psychol. 2012;48: 1180–7. pmid:22081879
- 54. Harms MB, Zayas V, Meltzoff AN, Carlson SM. Stability of executive function and predictions to adaptive behavior from middle childhood to pre-adolescence. Front Psychol. 2014;5. pmid:24795680
- 55. Byrne ME, Tanofsky-Kraff M, Lavender JM, Parker MN, Shank LM, Swanson TN, et al. Bridging executive function and disinhibited eating among youth: A network analysis. Int J Eat Disord. 2021;54: 721–732. pmid:33502799
- 56. Hongwanishkul D, Happaney KR, Lee WS, Zelazo PD. Assessment of hot and cool executive function in young children: Age-related changes and individual differences. Measurement of Executive Function in Early Childhood. Psychology Press; 2016. pp. 617–644.
- 57. Lamm C, Zelazo PD, Lewis MD. Neural correlates of cognitive control in childhood and adolescence: disentangling the contributions of age and executive function. Neuropsychologia. 2006;44: 2139–48. pmid:16310813
- 58. Romer D, Betancourt L, Giannetta JM, Brodsky NL, Farah M, Hurt H. Executive cognitive functions and impulsivity as correlates of risk taking and problem behavior in preadolescents. Neuropsychologia. 2009;47: 2916–2926. pmid:19560477
- 59. Ursache A, Raver CC. Iowa Gambling Task performance and executive function predict low-income urban preadolescents’ risky behaviors. Personal Individ Differ. 2015;79: 1–6. pmid:26412918
- 60. Groppe K, Elsner B. The influence of hot and cool executive function on the development of eating styles related to overweight in children. Appetite. 2015;87: 127–136. pmid:25528693
- 61. Groppe K, Elsner B. Executive function and weight status in children: A one-year longitudinal perspective. Child Neuropsychol. 2017;23: 129–147. pmid:26416504
- 62. Prencipe A, Kesek A, Cohen J, Lamm C, Lewis MD, Zelazo PD. Development of hot and cool executive function during the transition to adolescence. J Exp Child Psychol. 2011;108: 621–37. pmid:21044790
- 63. Weiss NH, Sullivan TP, Tull MT. Explicating the role of emotion dysregulation in risky behaviors: A review and synthesis of the literature with directions for future research and clinical practice. Curr Opin Psychol. 2015;3: 22–29. pmid:25705711
- 64. Scarpina F, Tagini S. The Stroop Color and Word Test. Front Psychol. 2017;8. pmid:28446889
- 65. Schmitz F, Kunina-Habenicht O, Hildebrandt A, Oberauer K, Wilhelm O. Psychometrics of the Iowa and Berlin Gambling Tasks: Unresolved Issues With Reliability and Validity for Risk Taking. Assessment. 2020;27: 232–245. pmid:29310459
- 66. Mischel W, Shoda Y, Rodriguez ML. Delay of gratification in children. Science. 1989;244: 933–938. pmid:2658056
- 67. Brand M, Fujiwara E, Borsutzky S, Kalbe E, Kessler J, Markowitsch HJ. Decision-making deficits of korsakoff patients in a new gambling task with explicit rules: associations with executive functions. Neuropsychology. 2005;19: 267. pmid:15910113
- 68. Romer D, Khurana A. Measurement of risk taking from developmental, economic, and neuroscience perspectives. 2022.
- 69. Tomov MS, Schulz E, Gershman SJ. Multi-task reinforcement learning in humans. Nat Hum Behav. 2021;5: 764–773. pmid:33510391
- 70. Vandendriessche H, Demmou A, Bavard S, Yadak J, Lemogne C, Mauras T, et al. Contextual influence of reinforcement learning performance of depression: evidence for a negativity bias? Psychol Med. 2023;53: 4696–4706. pmid:35726513