For many of us, our first experience with “gambling” was the lucky dip at the local school fete. We handed over our pocket money and hoped the plain packet we selected would contain something worth our 50 cents.
Now the lucky dip has been reinvented and become ubiquitous in the form of the blind bag toy.
Blind bags (also referred to as surprise packs or surprise toys) involve small and collectible toys hidden inside opaque packaging.
There are now hundreds of different blind bag lines, from old favourites such as My Little Pony, Transformers and Teenage Mutant Ninja Turtles to new fads like Tokidoki, Zomlings and the hugely popular L.O.L. Surprise! dolls.
Though no statistics for global blind bag toy sales are available, data from the United States give an indication of their phenomenal growth in recent years.
Between 2017 and 2018, market analyst NPD estimates the blind bag market in the US grew by about 60% even while total toy sales fell 2%.
Not surprisingly the US Toy Association declared “The Big Reveal” its top trend for 2018, with “the act of removing a surprise toy from a blind bag” being “just as exciting as the toy itself”.
There’s no reason to think 2019 will be any different in the US or around the world. Indeed, if toy aisles in stores are anything to go by, buyers’ appetite for blind bags appears to be ballooning.
The factors driving this sales growth include social media – in which “unboxing” has become an entertainment in itself – and the growing adult market for toys.
But the three most potent elements are the combination of price, the appeal of collecting and the psychological lure of surprise. It’s these three things that make blind bag toys so ethically problematic.
Collect them all!
Compared with other options, blind bag and surprise toys seem cheap. In Australia, prices range from about A$1.60 to A$15. This makes them attractive to children and adults alike, particularly as stocking stuffers at Christmas.
But the problem is that one is never enough.
Almost all blind bag toys are marketed as collections – with “collecting them all” emphasised in packaging and advertising.
There may be dozens to collect. Ooshie maker Head Start International, for example, has released four series of Marvel Ooshies with a total of 164 toys to collect.
So despite not being very costly to buy once, the costs add up.
As we’ve noted before in discussing the Ooshies phenomenon, collecting is attractive to children. While an estimated 30% of adults collect something, more than 90% of children do so.
Collecting appeals to children’s natural curiosity and is also a way of understanding the world through gathering and categorising.
The blind bag business model weaponises this collecting impulse through the gamble of the lucky dip.
It combines the pleasure of reward with the element of surprise, which is both compelling and addictive.
It taps into the same psychological mechanism that results in gambling addiction – namely intermittent reinforcement.
Intermittent reinforcement
The seductive, manipulative power of intermittent reinforcement (also known as variable ratio reinforcement) was famously shown in experiments with pigeons in the 1950s led by the noted American psychologist B.F. Skinner.
His team trained pigeons to peck a small lever that led to a food reward. When rewarded every time, the pigeons pecked the lever only when they were hungry. But when rewarded intermittently, they became compulsive peckers.
B.F. Skinner explains variable ratio reinforcement. Credit: jenningh.
The effects of intermittent reinforcement have also been demonstrated in clinical tests with children.
This helps explain the phenomenal growth and profitability of blind bags for toy companies. With the outcome being uncertain and the reward intermittent, the dopamine rush of opening each new bag is maintained.
Loot boxes
A virtual form of blind bag is proving even more profitable for video game companies. They’re known as loot boxes, which players can buy with real money in the hope of scoring “equipment” or “skins” to enhance their game avatar.
More often than not they don’t contain anything the player wants. Which is why players keep buying.
A study this year estimated loot boxes could be worth US$30 billion a year to the video game industry.
Concern about loot boxes effectively exposing children to gambling has led to calls in many countries for greater regulation.
Belgium has banned them. In Britain, the government is considering making companies that make money using loot boxes hold a gambling licence.
In Australia, the federal government says more research is needed before it will regulate.
How different is a blind bag toy?
Maybe you are always assured a toy, but the relative scarcity of different toys in a series means some are worth much more than others, and the desire for those will drive further purchases.
It’s a business model that still fundamentally works on the same principles that make poker machines, scratchies and other forms of gambling so addictive.
So while blind bag toys can be fun, the use of “blind” packaging and intermittent reinforcement to drive sales must be considered ethically problematic, especially given they are targeted at children.
As in all things, awareness and moderation are key.
Gambling is a commonplace activity across the globe. While many people engage in gambling recreationally without marked negative personal consequences, some individuals develop maladaptive symptoms of disordered gambling, which may ultimately manifest as full Gambling Disorder (also known as Pathological Gambling), characterized by functional impairment. Gambling Disorder is associated with untoward longer-term outcomes, including reduced quality of life, and elevated risk of divorce, financial issues (bankruptcy/insolvency), and incarceration [1].
Gambling Disorder was previously included in the Diagnostic and Statistical Manual Version IV (DSM-IV) as an impulse control disorder not elsewhere classified, but was moved in DSM-5 to the category of Substance Related and Addictive Disorders [2].
It shares parallels with impulse disorders and substance disorders from several vantage points, including in terms of phenomenology, comorbid overlap, and neurobiological models [3–5].
The concept of impulsivity is central to understanding Gambling Disorder and related addictions [6], and was highlighted as an important overarching construct in a recent Delphi analysis [4]. Impulsivity refers to behaviors or acts that are unduly hasty, risky, and inappropriate, leading to negative outcomes [7].
Recent models of impulsivity highlight its complex, multifactorial nature, and the need to consider not only its behavioral manifestations but also underlying brain-based and psychological mechanisms [8, 9].
As noted, Gambling Disorder was previously listed alongside impulse control disorders; furthermore, elevated impulsivity at the level of personality traits and occurrence of impulse control disorders is found in Gambling Disorder and family members, suggesting that some elements, at least of impulsivity, may be familial, and may be regarded as vulnerability markers [10, 11].
From a cognitive perspective, impulsivity can be fractionated into different domains, including impulsive choice (preference for smaller more immediate rewards rather than larger delayed rewards), impulsive motor responses (failure to suppress inappropriate motor responses), impulsive decision-making (risky/suboptimal choices under situations of ambiguity), reflection impulsivity (tendency to make premature responses to solutions under conditions of high response uncertainty), and impulsive cognitive bias (failure to suppress inappropriate attentional bias) [12–15].
Although these cognitive domains appear to be in many cases partly dissociable from each other, both behaviorally [12], and in terms of neurochemical substrates across species [7, 16], they tend to co-occur at the latent phenotype level of conceptualization [17].
Meta-analytic studies have identified impulsivity in Gambling Disorder in some of these cognitive domains viewed individually [18, 19]. However, analysis across the full range of domains is lacking, and so it is not well-established whether disordered gambling is associated with particular circumscribed deficits or more generalized inhibitory dyscontrol.
It is also not yet established from meta-analysis whether impulsive cognitive dysfunction extends to people with some degree of disordered gambling falling short of the full diagnosis (termed “at-risk” or “problem” gambling). Furthermore, effects of moderators such as study quality on impulsive cognition have not been rigorously examined.
The aim of this paper was to conduct a comprehensive meta-analysis of the range of cognitive domains relevant to impulsivity in Gambling Disorder, including examination of key moderators. Domains of interest were: attentional inhibition, motor inhibition, discounting, decision-making, and reflection impulsivity.
Furthermore, we evaluated datasets not only for Gambling Disorder but also for problem gambling (defined as datasets for which the case group included disordered gamblers not meeting the diagnostic threshold). We hypothesized that Gambling Disorder would be associated with elevated impulsiveness across all domains; but that decision-making impairment would also extend to problem gambling, consistent with impaired decision-making being a candidate “early” vulnerability marker as suggested by some prior case–control research [20].
Discussion
This study undertook a comprehensive meta-analysis of cognitive findings germane to impulsivity in Gambling Disorder, and in problem gambling (individuals fulfilling some but not necessarily all diagnostic criteria for Gambling Disorder), versus controls.
The main finding was that Gambling Disorder was associated, in meta-analysis, with elevated impulsivity on motor inhibition, attentional inhibition, discounting, and decision-making tasks. These results were generally of medium effect size, except for Go/No-Go task motor inhibition, which was of small effect size.
This analysis provides the first meta-analytic support for the existence of impulsivity in Gambling Disorder across cognitive domains, in keeping with neurobiological models implicating impulsivity and dysregulation of related frontostriatal brain pathways in the pathophysiology of disordered gambling [5, 26–28].
Thus, in fully established Gambling Disorder, impulsivity is evident across the full swathe of relevant cognitive tasks. These data also demonstrate elevated decision-making impulsivity (medium effect size) even in those with Problem Gambling, highlighting also the relative lack of studies on impulsivity in this context, and the need for further research.
This is important because psychological models emphasize a likely role for impulsivity, as measured by behavioral measures, in the development—i.e., in the chain of pathogenesis—of Gambling Disorder [29, 30].
The concept of impulsivity also has broader relevance to other candidate behaviorally addictive disorders that are not currently listed in the DSM [4, 26, 31].
The finding of significant impairments across different impulsivity domains in Gambling Disorder has several potential interpretations. One interpretation is that distinct cognitive domains are independently impaired in Gambling Disorder, with each impairment having a different biological substrate in terms of fronto-striatal circuitry.
Another interpretation, which we feel more likely per the law of parsimony, is that these findings reflect the existence of impulsivity at the latent phenotypic level for Gambling Disorder. Put differently, we hypothesize that there is a generalized tendency toward hasty, inappropriate, and premature actions, which predisposes toward Gambling Disorder and different manifestations of impulsivity across cognitive tasks.
This may account for the common clinical observation that impulsive problems tend to co-occur within the same individual; and for multiple measures of impulsivity (behavior, cognition, and personality) exhibiting correlations at the population level [17].
In prior research, we found that 33 impulsive and compulsive problem behaviors were optimally explained statistically within a bifactor model of latent phenotypes: i.e., by a general factor (termed “disinhibition”) contributing to the full range of problem behaviors; and then two separable impulsive and compulsive factors more directly linked to the expression of particular problems [32].
Indeed such latent phenotypes have been associated with changes in functional connectivity between the basal ganglia and cortices, including in people with Gambling Disorder [33]. These prior latent phenotype studies did not examine cognition. The current meta-analysis suggests that it would be valuable to extend a bifactor model to impulsivity-related cognitive domains in Gambling Disorder, to test our above hypothesis. Identification and affirmation of such latent phenotypes may be valuable both in order to better understand common neurobiological mechanisms across addictive disorders, and also with a view to identifying early treatment targets.
The overall quality scores of the included studies was 71.9%, and we found no evidence, in moderation analyses, that worse study quality was associated with more pronounced cognitive deficits in any domain. The most common methodological issue (evident in 85% of the data studies) was failure to screen for impulse control and related disorders (including ADHD) using adequate instruments. Such conditions are often associated with impulsive cognitive problems in themselves and so may thus contribute to the neuropsychological profiles observed herein.
Turning to the other moderation variables, presence or absence of comorbidities in cases did not significantly affect the cognitive findings. We did not identify significant moderating effects of study age category, except for evidence that discounting deficits were more pronounced for adult studies than for the available youth study.
It may be that case–control differences for this domain are harder to detect in younger samples due to increased noise arising from heterogeneous stages of brain development, as compared with the mature adult brain. The only significant moderating effect of gender was that studies including mixed (male and female) participants had larger Stop-signal inhibition deficits than studies including only males. We did not find gender-related differences in Stop-signal inhibition in a large pooled analysis previously, hence this result may be spurious [34].
Geographical location moderated the cognitive findings in several ways: Go/No-Go motor inhibition task deficits were larger in European studies compared with Asian studies; and discounting task deficits were larger for USA studies than for European studies, which in turn were larger than for Asian studies.
The underlying reasons for these cross-cultural effects are unclear. Overall, this may suggest less marked case–control differences in cognition for data studies arising from Asia. Notable cross-cultural differences in rates of comorbidity, and the nature of gambling activities engaged in, have been reported in other settings for Gambling Disorder [35]. Direct head-to-head comparisons of impulsivity data from different geographical sites would be warranted in light of these moderation effects, in future work.
Though this is the first comprehensive meta-analysis in Gambling Disorder covering the broad range of cognitive domains related to impulsivity, several limitations should be noted. We focused on recognized well-validated cognitive measures suitable for meta-analysis, for which there were at least four datasets available for meta-analysis.
As such, this is not an exhaustive analysis of all conceivable tasks or domains. We were not able to examine all types of decision-making tasks, due to lack of data, and our a priori specified choice of methodology for classifying such tasks. However, it is important to note that data studies have reported decision-making deficits across other tasks in Gambling Disorder; viz with the Cambridge Gambling Task, Balloon Analogue Risk Task, and the Game of Dice Task [36–38].
Selection of task measures of interest was based on expert consensus within the study team. Each meta-analysis included one measure of interest from a given domain per study, in order to avoid interdependence across variables within a given meta-analysis. Nonetheless, the separate meta-analyses within this paper cannot be regarded as being independent from each other, since in some cases they included data from several cognitive domains from a given data study.
This approach is widely accepted for meta-analysis [39, 40], but does highlight the need for future data papers to examine the latent structure of impulsivity in Gambling Disorder (i.e., the possibility of latent phenotypes). We did not examine task measures related to emotional processing or bias for gambling stimuli; these tasks tend to be tailored for particular studies or participant populations and therefore are problematic for valid meta-analysis. For quality scores, we included whether comorbidities were appropriately identified; rather than whether individual data papers conducted statistical analyses to control for such potential confounds.
This was for pragmatic purposes, since whether papers identified comorbidities is relatively easy and objective to assess; whereas evaluating whether control for identified comorbidities was appropriate is difficult to assess. For example, one could covary for ADHD symptoms in terms of a cognitive measure, but this relies on various statistical assumptions that may or may not be met, which we could not assess (including whether such analyses were sufficiently powered).
For convenience, Gambling Disorder was defined using accepted cutoffs, but not all data papers used structured clinical interviews to make this diagnosis. The problem gambling datasets included, in some cases, potentially mixed samples (i.e., where some individuals of the group may have met the criteria for Gambling Disorder). Nonetheless the finding of decision-making impairment in this group in meta-analysis is consistent with a previous study using a different task, which found deficits in at-risk gamblers none of whom met the diagnostic threshold [20].
The moderation analyses were not always conducted due to lack of datasets in given categories; and it should also be considered that the number of datasets in the explored moderator categories was small in many cases, potentially limiting power to detect effects of moderators. In particular, more research is needed to examine how the cognitive profile of Gambling Disorder overlaps with, or differentiates from, other disorders associated with impulsivity, including personality disorders [41], but also impulse control and developmental disorders.
In summary, this meta-analysis found evidence for deficits in Gambling Disorder across all evaluable domains of impulsivity that were considered. Thus, in considering cognitive findings in this disorder, it is necessary to consider both impulsive and compulsive features [42]. In fully established Gambling Disorder, there is generalized impulsivity across the full range of domains.
By contrast, decision-making impairment was also found in problem gambling, but there were insufficient data studies to address other cognitive domains. Thus, in keeping with neurobiological models and consensus views on Gambling Disorder, impulsivity is core to understanding Gambling Disorder.
The extent to which these findings relate to vulnerability versus chronicity merits further study [4], as does the issue of the existence of latent phenotypes. The finding of decision-making deficits in at-risk gamblers here using the Iowa Gambling Task, and in a prior data study using a different task [20], indicates this is a particularly promising domain for identifying vulnerability markers in this setting.
Source:
The Conversation
Media Contacts:
Martin Grimmer & Louise Grimmer – The Conversation
