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Chapter 5: Gambling Hijacks an Ancestral Motivational System Shaped by Natural Selection
Patrick Anselme, Faculty of Psychology, Department of Biopsychology, University of Bochum, Universitätsstraße 150, D-44801 Bochum, Germany; e-mail: Patrick.Anselme@rub.de; phone: +49 (234) 32 21628; fax: +49 (234) 32 14377.
Why Do We Gamble?
People gamble on many occasions, at casinos but also simply buying lottery tickets and scratch cards. In the United Kingdom, for example, around 70% of people gambled in the past 12 months according to a 1999 (72%) and a 2007 (68%) survey (Wardle et al., 2007). Gambling activity is not new; prehistoric dices consisting of objects such as pebbles and bones as old as 8,000 years have been identified (Schwartz, 2006). The first dice throwers were shamans who used astragali for divination (the practice of telling the future), not for gambling in its modern form, but this was already a way of wagering on uncertain outcomes. The line between divination and gambling is blurred in our ancestors, but it is a fact that gambling-like activity has always been a crucial part of the human experience (Schwartz, 2006).
Why do people gamble in our modern societies? Griffiths (1990) reported that a large majority (90%) of individuals start to gamble for fun, and that this factor remains the most common reason invoked (84%) to continue this activity thereafter (see also Nower & Blaszczynski, 2010). A smaller number of individuals start to gamble to win money (70%), and this reason is then less frequently given to justify continuation of the activity (48%). Why is it so attractive and fun engaging in an activity that, for sure, will cause greater monetary losses than gains? And why do many people repeat this activity again and again to such a point that gambling behavior may turn pathological and destroy the gambler’s family, social, and professional lives? Here, I defend the hypothesis that gambling is attractive and fun, irrespective of its possible deleterious modern consequences, because natural selection favored organisms for which reward uncertainty enhances reward-seeking motivation. (The word “organism” refers to our human ancestors but also to our nonhuman ancestors and to many other animal species that are not directly related to humankind.) I am not trying to say that individuals seek and like uncertainty in itself. Instead, the relatively random distribution of resources (especially food) in the environment is a challenge for survival. Food is globally predictable but locally unpredictable; whether an attempt to get food will be rewarded or not cannot be determined in advance. Individuals with a higher motivation to seek food items in this gambling-like situation—time and energy must be spent without any guarantee of positive outcomes—increase their chance of finding them in sufficient amounts and hence their chance of survival and of reproduction (Anselme, 2013; Anselme & Robinson, 2013; Anselme et al., 2017).
In this perspective, gambling is attractive and fun because we tend to find desirable and pleasant the activities we are adapted for. For example, we want and like to consume sweet and salty nutrients due to their favorable metabolic implications, but we are less hungry for bitter and sour foods—two tastes often associated with toxins and fermentation in nature (Panksepp, 1998). Similarly, people prefer to live in savanna-like environments relative to other alternatives such as deserts and different forest types (Falk & Balling, 2010; Orians & Heerwagen, 1992), suggesting that we want and like the environments in which our hunter-gatherer ancestors lived and evolved during the Pleistocene—a period of approximately 2.5 MY (Pinker, 1997). In other words, gambling would be attractive and fun because this activity hijacks an ancestral motivational system shaped by natural selection to optimize food seeking in uncertain environments.
In this chapter, I aim to go further in the analysis of this motivational system. Specifically, I target the implications of previous theoretical developments (see Anselme 2015, 2016) for the understanding of the motivational origins of gambling behavior. First, it is shown that reward uncertainty enhances foraging behavior in animals (and humans) exposed to natural and artificial settings, indicating that this represents quite a general process. Second, it is argued that a good candidate to explain the behavioral invigoration observed under uncertainty is to say that animals “hope” for rewards when they are not guaranteed. Incentive hope—or the motivational excitement for possible good news when bad news are likely—is believed to be a product of natural selection, because it may act against the risk of starvation (Anselme et al., 2017). It is explained how incentive hope can be distinguished from conscious hope, the former denoting the core psychological underpinning of the latter, and also partly from incentive salience (Berridge & Robinson, 1998). Third, I suggest that our modern societies provide well-designed environments, such as casinos, in which incentive hope is hijacked and may lead to maladaptive behaviors in the most vulnerable individuals.
Reward Uncertainty Boosts Foraging Activity
Researchers working with animals have long noted that the uncertainty of obtaining a reward invigorates seeking behavior. This phenomenon is well documented in two distinct research fields: the ecology of energy management and Pavlovian autoshaping. Here, I briefly review the uncertainty situations under which behavioral invigoration can be observed and emphasize the adaptiveness of this process.
Energy Management in Unfavorable Environments
In the wild, animals have to face multiple sources of risk, especially predation and starvation. The risk of starvation is particularly important for small-sized individuals, such as found in many passerine bird species (e.g., robins, starlings, blackbirds), because their unfavorable surface/volume ratio causes a rapid loss of their internal heat. In summer, when food is abundant, small birds spend 40–60% of the day time seeking food and tend to reject opportunities to eat in order to reduce the risk of predation—the leaner an individual, the faster and the more agile it is to escape from predatory attacks. But in winter, when food is scarcer, they may spend up to 85–95% of the day time seeking food, and they eat as many food items as they can find out in order to reduce the risk of starvation, despite a higher risk of predation. Cold is only partly responsible for this increase in foraging time, because individuals kept under low temperatures in the lab do not necessarily increase food consumption (e.g., Pravosudov & Grubb, 1998). In fact, small birds eat and/or hoard more food items when the resources are unpredictable—that is, potentially unavailable on some days (Bauer et al., 2011; Dolnik, 1967; Haftorn, 1976; King & Farner, 1965; Pravosudov, 2003; Pravosudov & Grubb, 1997; van Balen, 1980). Invigoration of seeking behavior in winter has the adaptive consequence that, if food remains in sufficient amounts in the environment, this process may increase the fat reserves of birds—and therefore their chance to survive the unlucky days (for computer simulations, see Anselme et al., 2017). It is important to note that humans exposed to limited, irregular resources also eat more and become attracted by high-calorie food items (Cheon & Hong, 2017; Laran & Salerno, 2013; Nettle et al., 2017; Swaffield & Roberts, 2015). The fact that people—for whom starvation risk is low within modern Occidental societies—exhibit the same kind of behavioral responses to food unpredictability as wild birds do is an indication that invigoration of seeking behavior is genetically determined here, shaped by natural selection as an anti-starvation strategy.
Finally, it must be emphasized that winter is only one cause of food unpredictability in the wild, other factors contribute to render access to food difficult. Some individuals have an unpredictable access to food because they are poor foragers (Cresswell, 2003), or because dominant individuals prevent them from reaching the richest-food sites (Krams, 2000), or because of intraspecific competition. Indeed, a food site contains a limited number of edible items. The presence of competitors on this site reduces the opportunities to eat for a given individual. As in the case of poor foragers and of subordinate individuals, competitors tend to invigorate foraging rate and foraging effort in general (Chakravarti & Cotton, 2014; Fernandez-Juricic et al., 2004; Keeling & Hurnik, 1996; Plowright & Redmond, 1996; Xin et al., 2017).
Sign-Tracking Behavior under Partial Reinforcement
The brief presentation of a conditioned stimulus (CS, such as a lever for rats), followed by automatic delivery of some food pellets, may lead animals—after repeated trials—to approach and interact with the CS (sign-tracking) or to inspect the food dish during the CS presentation (goal-tracking). For detailed information on the behavioral and neurobiological analyses of sign- and goal-tracking behaviors, see Kuhn et al. (this volume), Meyer & Tripi (this volume), and Robinson et al. (this volume). In this experimental procedure (Pavlovian autoshaping), sign-tracking appears irrational because this behavior occurs without necessity—the animal is rewarded on each trial, irrespective of its interaction with the CS (Tomie et al., 2016).
Interestingly, when only half of the trials are randomly rewarded (the CS is ambiguous, predicting food or no food), sign-tracking behavior increases in comparison with a situation of continuous reinforcement (where the CS is nonambiguous, predicting food consistently; Anselme et al., 2013; Boakes, 1977; Collins & Pearce, 1985; Gottlieb, 2004). Accordingly, goal-tracking behavior is decreased, and there is evidence that food uncertainty converts potential goal-trackers into sign-trackers (Robinson et al., 2015). The invigoration of the sign-tracking responses appears even more irrational here in that reward rate is reduced (partial reinforcement) compared to what it could be (continuous reinforcement). But is this irrationality? After all, we saw previously that foraging effort is increased when resources are scarce, and that this adaptive strategy consists of an insurance against starvation. To understand the stimulating effect of partial reinforcement on sign-tracking behavior, we have to ask a simple question: why did Pavlov’s dog salivate to the sound of a bell? The answer is: because salivation is useful to the digestion of the upcoming food. Animals learn to respond to a CS only if the conditioned response helps them cope with—is relevant to—the unconditioned stimulus or UCS (Domjan, 2016, p. 100). For example, Garcia and Koelling (1966) found that the number of lick responses to an audiovisual CS decreased when the UCS was shock but not when the UCS was sickness. In contrast, the lick responses to a taste CS decreased when the UCS was sickness but not when the UCS was shock. The rationale behind these results is that, in nature, the auditory or visual detection of a stimulus can potentially predict injury (e.g., predatory attack) but not illness, while the taste of an ingested food can potentially predict illness (e.g., spoiled nutrient) but not injury. The evolutionary established relationships between CSs and UCSs could also explain the higher response rates to an unreliable than to a reliable CS in Pavlovian autoshaping: animals would respond more to unreliability because, in nature, the distribution of food items is basically random and many CSs are imperfect predictors of food (a seed husk may be empty, a mulberry may have no fruits, etc.). Animals are simply prepared to work harder when part of the attempts to get food is non-rewarded. In other words, avidly responding to the CS presentations in uncertainty autoshaping is similar to avidly checking the available CSs in a poor-food environment; this schedule-induced behavior is the signature of a neurobehavioral adaptation—aimed to minimize the risk of starvation in a natural setting.
Incentive Hope: A Common Response to Unpredictable, Significant Events
I have suggested that animals increase their responses to signals of uncertain food because this contributes to reduce the risk of starvation, but how this adaptive behavior is produced remains unclear. What is the psychological process responsible for behavioral invigoration here? Elsewhere, I showed that learning and frustration cannot satisfactorily account for invigoration (Anselme 2015, 2016)—the arguments will not be repeated due to space limitation. Instead, as previously, I focus on the importance of incentive salience (or incentive motivation or “wanting”) in the process-controlling invigoration, while showing that this phenomenon is only part of the full explanation. It is suggested that behavioral invigoration under uncertainty comes from the motivational excitement animals develop for possible good news when bad news are likely—a psychological state referred to as incentive hope (Anselme 2015, 2016). How incentive hope can motivate gambling will be considered.
Incentive Salience and Beyond
The incentive salience hypothesis (Berridge & Robinson, 1998) relies on a body of evidence showing that (a) motivated behavior is strongly correlated with the release of dopamine in the ventral striatum, and (b) motivated behavior is produced unconsciously rather than by means of conscious decisions. The hypothesis suggests that mesolimbic dopamine transforms the neutral perception/representation of a stimulus in an appetizing reward (Berridge, 2007). For example, dopamine controls the fact that a high-calorie dessert is attractive when hungry and aversive when full, and cognition is ineffective in modulating this effect. The neurophysiological underpinnings of this phenomenon is adaptive, informing the individual of what should or should not be done (e.g., eat or don’t eat) without having to think about it. Thus, incentive salience easily explains why a CS predictive of reward is avidly approached and physically contacted in hyperdopaminergic mutant mice (Peciña et al., 2003), while food itself is ignored in dopamine-deficient mice (Cannon & Bseikri, 2004).
At first sight, incentive salience could also easily explain why reward uncertainty invigorates behavioral responses; uncertainty could be viewed as a factor contributing to “wanting.” Indeed, reward uncertainty enhances dopamine production in the ventral tegmental area, which directly projects to the ventral striatum (D’Souza & Duvauchelle, 2008; Fiorillo et al., 2003; Hart et al., 2015), and reward uncertainty interacts in a complementary fashion with dopaminergic drugs (Robinson et al., 2015; Singer et al., 2012; Zack et al., 2014). Also, there is evidence that pathological gamblers show higher striatal dopamine levels than healthy controls (Joutsa et al., 2012), especially when they are experiencing uncertainty (Linnet et al., 2012). For sure, incentive salience is involved in the processing of reward uncertainty, but there are two good reasons to believe that it is only part of the whole story:
- The incentive salience hypothesis does not make any prediction relative to the behavioral effects of reward uncertainty. The attribution of incentive salience to a CS is independent of the predictive value of the CS—that is, of the reliability of the CS-UCS association (Flagel et al., 2007; Robinson & Flagel, 2009). A 100% reward-predictive CS is attractive, but the hypothesis does not tell us whether a 50% reward-predictive CS should be more or less attractive. Intuitively, we may have the feeling that the hypothesis would predict a decrease (rather than an increase) in response rates under a 50% (relative to a 100%) probability of reward, because a 0% reward-predictive (random) CS is unattractive (e.g., Rescorla, 1968). But the incentive salience hypothesis provides no theoretical justification for this interpretation.
- If incentive salience fully controlled behavioral invigoration under uncertainty, animals exposed to a free choice should prefer an ambiguous CS (50% predictive of food) to a nonambiguous CS (100% predictive of food). But such a preference is not observed, except perhaps under the influence of some dopaminergic agonists (Tremblay et al., 2017). In probabilistic choices, animals avoid CS ambiguity when possible (McDevitt et al., 2016). They may prefer one option that provides food or no food over another option that provides food with more certainty but only if the former is associated with nonambiguous CSs (e.g., if the white key turns red—> 80% chance of reward, if the white key turns green—> 0% chance of reward) and the latter with ambiguous CSs (e.g., if the white key turns blue or yellow—> 100% chance of reward). In other words, animals exposed to free choices track the reliability of CSs not uncertainty in itself (Chow et al., 2017; Smith & Zentall, 2016).
In summary, explaining how reward uncertainty invigorates response rates—whether in the wild or in serial autoshaping—amounts to understanding how uncertainty alters the animal’s motivation to respond. The role of dopamine in this phenomenon suggests that incentive salience is required, but more is needed to fully capture how reward motivation interacts with uncertainty itself.
Motivational Excitement for Possible Good News
How can animals become motivated to seek unpredictable reward sources? In fact, the more unpredictable a food source, the more motivated an animal should be to optimize its chance of survival (Anselme, 2013). As discussed earlier, this is what they do; animals behave as if they were excited by possible good news—that is, finding what they seek—when the environmental conditions indicate that bad news—not finding what they seek—are likely. This is the incentive hope hypothesis (Anselme, 2015, 2016). The word “hope” refers to the fact that an individual “wants” a reward that is not guaranteed; and “incentive” refers to the fact that this psychological state is subcognitive—its occurrence does not require any form of knowledge or consciousness. Importantly, incentive hope is viewed as the unconscious core psychological underpinning of conscious hopes but does not require the perception of an introspective self—there is an “I” who hope for future reward. Conscious hopes involve the ability to have a concept of self as a stable entity that existed in the past (yesterday) and will continue to exist in the future (tomorrow). However, current findings suggest that this ability is specific to humans (Tulving, 2005). Incentive hope simply means that animals behave as if they consciously hoped for reward. Similarly, incentive salience—“wanting” that food—is likely to be the unconscious core psychological underpinning of conscious desires (Anselme and Robinson, 2016) but does not imply that there is an “I” who want that food. Thus, the concept of incentive hope encompasses that of “wanting,” since incentive hope is assumed to be a dopamine-dependent process. But it is not reducible to that of incentive salience, and incentive hope may recruit brain regions not specifically involved in the control of incentive salience such as the dorsomedial striatum (Torres et al., 2016).
Now, we have a theoretical framework compatible with the incentive salience hypothesis that can overcome the two limits described in the previous section. First, our view predicts that reward uncertainty invigorates response rates to an ambiguous CS because of the animal’s motivational excitement. Second, and this is important, incentive hope implies that an ambiguous CS invigorates responding only if uncertainty is unavoidable (like in serial autoshaping or in the wild), not that an ambiguous CS should be preferred to a nonambiguous CS. Behavioral invigoration does not denote preference but survival requirement. The incentive hope hypothesis predicts that ambiguity cannot be preferred to nonambiguity because developing hope for something you can obtain for sure is superfluous. It could be argued that a number of experiments show a preference for reward uncertainty in choice procedures (e.g., Belke & Spetch, 1994; Dunn & Spetch, 1990; Gipson et al., 2009; Mazur 1991; Spetch et al., 1990; Stagner & Zentall, 2010; Vasconcelos et al., 2015). But, as noted earlier, uncertainty is not sought for itself in these experiments; the animals are just tracking the nonambiguous CSs associated with it. For example, Chow et al. (2017) tested the preference of rats for a discriminative option consisting of two nonambiguous CSs with 50:50 odds (one predicted 0% and the other 100% chance of reward) and a non-discriminative option consisting of one ambiguous CS that predicted reward or non-reward with 50:50 odds. Thus, the overall probability of reinforcement was 50% with each option. They observed an increasing preference for the discriminative alternative with training, indicating that animals do not prefer ambiguity. These authors argued that this result contradicts a major prediction of the incentive hope hypothesis, which suggests that uncertainty-induced hope for rewards adds some motivational value to normal “wanting” for those rewards (Anselme, 2015, 2016; Anselme et al., 2013). For them, incentive hope should be minimal in the discriminative option because reward probabilities are known for each CS (0% and 100%), while it should be maximal in the non-discriminative option due to the predictive inaccuracy of the CS (50%). However, there is misinterpretation about the definition of the incentive hope hypothesis here; the hypothesis posits that unavoidable reward uncertainty associated with CS ambiguity enhances conditioned responding (e.g., serial autoshaping), not that animals prefer this configuration. In their procedure, the rats were undoubtedly attracted by the nonambiguous 100% chance of reward associated with one of the CSs in the terminal link of the discriminative option. Probabilistic choice behavior is a consequence of incentive salience without incentive hope.
In summary, we showed that animals increase their response rates to stimuli that ambiguously and unavoidably predict the presence or the absence of rewards—especially food—in natural and artificial settings. This phenomenon appears to be an adaptive strategy shaped by natural selection to minimize the risk of starvation. We suggested that the core psychological state underpinning behavioral invigoration in this context is incentive hope. If correct, this means that incentive hope is likely to play a determining role in human gambling. For example, the idea that hopes for future success develop among gamblers experiencing near misses—that is, failures that are close to being successful—was briefly proposed by Parke and Griffiths (2004). Indeed, near misses are perceived as encouraging signs that confirm the effectiveness of the gambler’s strategy, leading to continued gambling. In addition, the motivational effects of near misses are evidenced by the higher dopamine levels correlated with their occurrence (Chase and Clark, 2010; Clark et al., 2009; Kassinove and Schare, 2001). More generally, let’s see how gambling environments may contribute to recruit incentive hope.
Casinos Act as Supernormal Configurations of Stimuli
In this section, it is argued that gambling hijacks an ancestral motivational system (incentive hope) shaped by natural selection to promote survival in the wild—just as serial autoshaping does. The idea that some behaviors exploit cognitive/perceptual systems designed for independent evolutionary purposes is not new: reading, writing, enjoying music, and even believing in God have no adaptive function in themselves, they are all by-products of the toolkits of our complex adapted brain (Pinker, 1997). For the same reason, we have no adaptation to specifically enjoy eating hamburgers, but hamburgers contain a number of ingredients (fat, salt, proteins) for which high-selective pressures existed among our hunter-gatherer ancestors—these ingredients were both rare and necessary for survival. The individuals who could find them out had a better chance of survival than those who could not, and had consequently a better chance of transmitting their good genes to the next generation, and so forth. Today, hamburgers act as supernormal configurations of stimuli because they bring all these ingredients together, stimulating incentive salience (“wanting”) more than many other foods. Similarly, I think that gambling hijacks incentive hope, because casinos act as supernormal configurations of stimuli:
- Casinos are confined environments in which any outcome is uncertain. In a sense, monetary rewards in casinos are comparable to the scarce food items that an animal seeks midwinter: reward uncertainty is unavoidable and successful foraging behavior is not guaranteed, irrespective of the cognitive strategy that is used. One major difference, however, is that the person may leave the casino any time. The opportunity to express an activity we are adapted for (seeking under uncertainty) while having the option to quit before things are going bad might be part of the pleasure to gamble.
- Casinos contain multiple potent, ambiguous CSs. Sounds, lights, the tokens used for slot machines, and money itself, are very powerful CSs that contribute to motivate people to play games and persist in this activity. Griffiths (1990) reported that, among other factors, the flashing lights and/or the music and noise are attractive qualities for 60% of the players. More specifically, Mentzoni et al. (2014) found that low-tempo music increases gambling persistence (more bets placed), while high-tempo music increases gambling impulsivity (faster reaction time per placed bet). Tokens, sometimes in metal like real money, may become strong CSs for people going to a casino repeatedly. All these CSs are ambiguous: a token is placed in a slot machine and many lights/sounds arise before the outcome—win or loss—is known.
- Casinos simulate intraspecific competition for resources. When the amount of a resource is limited, the presence of competitors necessarily reduces the availability of that resource for each individual. Competition-induced scarcity increases foraging effort, as shown earlier in animals. The same principle might apply to casinos, which are full of people trying to get (foraging on) the same rare resource—money (Kohn, 1992). In a sense, the competition also exists between the gambler and the casino owner.
- Availability of such environments. Countries that offer many gambling opportunities have higher prevalence rates of pathological gambling (e.g., Ladouceur et al., 1999; Johansson et al., 2009). It is interesting to note that not all forms of gambling are good predictors of pathological behavior, and that the best six predictors—pull-tabs, casino, bingo, cards, lottery, and sport betting—have little in common (Welte et al., 2004). However, the two riskiest types—pull-tabs and casino—have high-event frequencies, compared to the least risky types—lottery and sport betting (Welte et al., 2004). Pathological gambling obeys the same logic as other addictions, such as overeating in junk-food societies or hypersexuality induced by free pornography on the Internet.
Together, these situational factors may generate a context that favors the expression of human foraging activity, just as similar features/qualities enhance foraging effort in nonhuman animals. But if gambling recruits an ancestral motivational system established for other evolutionary purposes, why does only a small portion of the population (1–2%) develop pathological gambling (Shaffer et al., 1999; Wardle et al., 2007)? The presence of many casinos in our modern Western societies should attract most people and cause severe addiction to gambling. In fact, the presence of favorable environments is certainly insufficient in itself to induce addictive behaviors—similarly, animals forage more intensively in winter than in summer, but they do not become addicted to food seeking. Important factors to consider here are related to the personality traits (Forbush et al., 2008) and lifestyle of gamblers (Ledgerwood & Petry, 2006). Individual vulnerability contributes to explain why problem gambling remains relatively marginal relative to the significant proportion of individuals who gamble in the general population (e.g., Wardle et al., 2007).
Why Are We Not All Pathological Gamblers?
What is an addiction? Take the example of drug addiction (for details, see Morrow, this volume; Robinson et al., this volume; Tomie et al., this volume). Repeated use of a drug abuse (cocaine, heroin, alcohol, etc.) has deleterious effects on health and other dimensions of the drug user’s life. Drug addicts may be fully aware of that, expressing their firm intention to stop their consumption and partaking sometimes in treatment programs. However, a drug rehab removes the withdrawal symptoms associated with the early phase of abstinence but has no action on the long-lasting sensitization of dopamine neurons caused by repeated drug administration (Robinson & Berridge, 1993). Even long after the withdrawal symptoms have disappeared, a significant number of individuals relapse to drug taking (Hunt et al., 1971), and the probability to relapse is higher for those who developed neuronal sensitization (Bartlett et al., 1997). This means that mesolimbic dopamine plays a determining role in addictive behavior, independently of the individual’s cognitive intention to remain abstinent. In fact, any kind of addiction—including problematic gambling activity—is a consequence of this same process. And any kind of addiction is susceptible to develop only in the most vulnerable individuals. Many persons cannot stop smoking, while others quit easily. The most probable reason for this is that the former have an addiction to nicotine and the latter do not. The same is true of gambling. For example, Parkinson’s disease patients are often treated with dopaminergic drugs—such as pramipexole and ropinirole—to alleviate their symptoms (Dodd et al., 2005; Voon et al., 2011). However, only a minority of them (13.6%) develop addictions, which may include compulsive shopping and pathological gambling (Weintraub et al., 2010; see also Crockford et al., 2008).
There are no strict causes of problem gambling activity, but a series of risk factors have been identified (for extensive reviews, see Goudriaan et al., 2004; van den Bos et al., 2013; van Holst et al., 2010). For example, impulsivity—the tendency to prefer small immediate rewards over larger delayed rewards—is a major factor to consider. The inability to wait for gratifications leads problem gamblers to try to obtain the jackpot at a casino rather than saving a little amount of money every week. Given that young individuals have lower levels of self-control than older ones, they are more susceptible to gambling addiction (Johansson et al., 2009), a feature also observed between young and older rats with respect to drug self-administration (Adriani et al., 2002; Quoilin et al., 2010; Spear & Varlinskaya, 2010). Competitiveness is also correlated with pathological gambling, because competitive people are less likely to give up after a loss than noncompetitive people (Parke et al., 2004). Thus, competitive individuals are more prone to chasing behavior, which is also a risk factor in the development of problem gambling. Problem gambling is more frequent among people experiencing a lack of rewarding events in life: slot machine players typically gamble to escape stressful situations, while horse race and casino gamblers attempt to replace feelings of boredom with higher levels of arousal (van Holst et al., 2010). Finally, social and environmental stresses make individuals more vulnerable to the addictive properties of drugs of abuse and also more prone to attribute motivational salience to CSs (Beckmann & Bardo, 2012; Diaz et al., 2013; Lomanowska et al., 2011; Nader et al., 2012; Pattison et al., 2013). This may certainly contribute to enhance the attractive power of the CSs present in casinos or on the screen of a computer for people who gamble online at home. Other factors have been reported, such as the illusion of control and the presence of drug addictions (e.g., Johansson et al., 2009).
Thus, vulnerability to gambling addiction results from a combination of external and internal factors, explaining why many people do not have to struggle against gambling problems. But it must be realized that gambling addiction is only possible because we have a motivation to perform this kind of activity. Impulsivity, competitiveness, boredom, and stress can lead individuals to gamble only because they have an attraction for money (incentive salience) and are motivationally aroused by possible good news (incentive hope). Incentive salience is sufficient to explain drug addiction, because drugs directly act at a neurobiological level and have immediate reinforcing effects. Incentive salience is also crucial in gambling and represents the motivational basis for money attraction. But incentive hope is necessary to explain why unlikely monetary CSs are so avidly chased, just as why a rat respond more to an unreliable than to a reliable metal lever CS. Original predictions can be proposed on this basis. For example, the incentive hope hypothesis predicts that the hope for money is higher than a negative emotion such as frustration before a trial, especially in problem gamblers because their motivation to earn money is higher than in non-gamblers (Nower & Blaszczynski, 2010). The hypothesis also predicts that the perception of economic insecurity is correlated with this form of addiction. It is already known that problem gamblers are more frequent among people with low socioeconomic status (e.g., Johansson et al., 2009). But it is here argued that people without money problems who perceive themselves in a situation of potential economic insecurity (because of unstable working activities) might be a population at risk as well.
In conclusion, incentive hope is hypothesized to be at the very origin of gambling addiction, even though maladaptive behavior depends on a combination of many other factors that is found only in a small proportion of the general population. But it is paradoxically this motivational system that perhaps allowed all our ancestors to survive for hundreds of thousands of years.
- Adriani, W., Macri, S., Pacifici, R., & Laviola, G. (2002). Peculiar vulnerability to nicotine oral self-administration in mice during early adolescence. Neuropsychopharmacology, 27, 212–224.
- Anselme, P. (2013). Dopamine, motivation, and the evolutionary significance of gambling-like behaviour. Behavioural Brain Research, 256, 1–4.
- Anselme, P. (2015). Incentive salience attribution under reward uncertainty: A Pavlovian model. Behavioural Processes, 111, 6–18.
- Anselme, P. (2016). Motivational control of sign-tracking behaviour: A theoretical framework. Neuroscience and Biobehavioral Reviews, 65, 1–20.
- Anselme, P., Otto, T., & Güntürkün, O. (2017). How unpredictable access to food increases the body fat of small passerines: A mechanistic approach. Behavioural Processes, 144, 33–45.
- Anselme, P., Robinson, & M. J. F. (2013). What motivates gambling behavior: Insight into dopamine’s role. Frontiers in Behavioral Neuroscience, 7, 182.
- Anselme, P., Robinson, & M. J. F. (2016). “Wanting,” “liking,” and their relation to consciousness. Journal of Experimental Psychology: Animal Learning and Cognition, 42, 123–140.
- Anselme, P., Robinson, M. J. F., & Berridge, K. C. (2013). Reward uncertainty enhances incentive salience attribution as sign-tracking. Behavioural Brain Research, 238, 53–61.
- Bartlett, E., Hallin, A., Chapman, B., & Angrist, B. (1997). Selective sensitization to the psychosis-inducing effects of cocaine: A possible marker for addiction relapse vulnerability? Neuropsychopharmacology, 16, 77–82.
- Bauer, C. M., Glassman, L. W., Cyr, N. E., & Romero, L. M. (2011). Effects of predictable and unpredictable food restriction on the stress response in molting and non-molting European starlings (Sturnus vulgaris). Comparative and Biochemical Physiology A, 160, 390–399.
- Beckmann, J. S., & Bardo, M. T. (2012). Environmental enrichment reduces attribution of incentive salience to a food associated stimulus. Behavioural Brain Research, 226, 331–334.
- Belke, T. W., & Spetch, M. L. (1994). Choice between reliable and unreliable reinforcement alternatives revisited: Preference for unreliable reinforcement. Journal of the Experimental Analysis of Behavior, 62, 353–366.
- Berridge, K. C. (2007). The debate over dopamine’s role in reward: The case for incentive salience. Psychopharmacology, 191, 391–431.
- Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward: Hedonic impact, reward learning, or incentive salience? Brain Research Review, 28, 309–369.
- Boakes, R. A. (1977). Performance on learning to associate a stimulus with positive reinforcement. In H. Davis & H. M. B. Hurvitz (Eds.), Operant Pavlovian interactions (pp. 67–97). Hillsdale, NJ: Erlbaum Associates.
- Cannon, C. M., & Bseikri, M. R. (2004). Is dopamine required for natural reward? Physiology and Behavior, 81, 741–748.
- Chakravarti, L. J., & Cotton, P. A. (2014). The effects of a competitor on the foraging behaviour of the shore crab Carcinus maenas. PLoS ONE 9, e93546.
- Chase, H. W., & Clark, L. (2010). Gambling severity predicts midbrain response to near-miss outcomes. Journal of Neuroscience, 30, 6180–6187.
- Cheon, B. K., & Hong, Y.-Y. (2017). Mere experience of low subjective socioeconomic status stimulates appetite and food intake. Proceedings of the National Academy of Science, 114, 72–77.
- Chow, J. J., Smith, A. P., Wilson, A. G., Zentall, T. R., & Beckmann, J. S. (2017). Suboptimal choice in rats: Incentive salience attribution promotes maladative decision-making. Behavioural Brain Research, 320, 244–254.
- Clark, L., Lawrence, A. J., Astley-Jones, F., & Gray, N. (2009). Gambling near-misses enhance motivation to gamble and recruit win-related brain circuitry. Neuron 61, 481–490.
- Collins, L., Pearce, J. M. (1985). Predictive accuracy and the effects of partial reinforcement on serial autoshaping. Journal of Experimental Psychology: Animal Behavior Processes, 11, 548–564.
- Cresswell, W. (2003). Testing the mass-dependent predation hypothesis: In European blackbirds poor foragers have higher overwinter body reserves. Animal Behaviour, 65, 1035–1044.
- Crockford, D., Quickfall, J., Currie, S., Furtado, S., Suchowersky, O., el-Guebaly, N. (2008). Prevalence of problem and pathological gambling in Parkinson’s disease. Journal of Gambling Studies, 24, 411–422.
- Diaz, L. R., Siontas, D., Mendoza, J., & Arvanitogiannis, A. (2013). High levels of wheel running protect against behavioral sensitization to cocaine. Behavioural Brain Research, 237, 82–85.
- Dodd, M. L., Klos, K. J., Bower, J. H., Geda, Y. E., Josephs, K. A., & Ahlskog, J. E. (2005). Pathological gambling caused by drugs used to treat Parkinson disease. Archives of Neurology, 62, 1377–1381.
- Dolnik, W. R. (1967). Bioenergetische anpassungen der vogel an die uberwinterung in verschledenen Breiten. Der Falke, 14, 305–306.
- Domjan, M. (2016). The principles of learning and behavior. Belmont, CA: Cengage Learning.
- D’Souza, M. S., & Duvauchelle, C. L. (2008). Certain or uncertain cocaine expectations influence accumbens dopamine responses to self-administered cocaine and non-rewarded operant behavior. European Neuropsychopharmacology, 18, 628–638.
- Dunn, R., & Spetch, M. L. (1990). Choice with uncertain outcomes: Conditioned reinforcement effects. Journal of the Experimental Analysis of Behavior, 53, 201–218.
- Falk, J. H., & Balling, J. D. (2010). Evolutionary influence on human landscape preference. Environment and Behavior, 42, 479–493.
- Fernandez-Juricic, E., Siller, S., & Kacelnik, A. (2004). Flock density, social foraging, and scanning: An experiment with starlings. Behavioral Ecology, 15, 371–379.
- Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003). Discrete coding of reward probability and uncertainty by dopamine neurons. Science, 299, 1898–1902.
- Flagel, S. B., Watson, S. J., Robinson, T. E., & Akil, H. (2007). Individual differences in the propensity to approach signals vs goals promote different adaptations in the dopamine system of rats. Psychopharmacology, 191, 599–607.
- Forbush, K. T., Shaw, M., Graeber, M. A., Hovick, L., Meyer, V. J., Moser, D. J., Bayless, J., Watson, D., & Black, D. W. (2008). Neuropsychological characteristics and personality traits in pathological gambling. CNS Spectrums, 13, 306–315.
- Garcia, J., & Koelling, R. A. (1966). Relation of cue to consequence in avoidance learning. Psychonomic Science, 4, 123–124.
- Gipson, C. D., Alessandri, J. J. D., Miller, H. C., & Zentall, T. R. (2009). Preference for 50% reinforcement over 75% reinforcement by pigeons. Learning and Behavior, 37, 289–298.
- Gottlieb, D. A. (2004). Acquisition with partial and continuous reinforcement in pigeon autoshaping. Learning and Behavior, 32, 321–334.
- Goudriaan, A. E., Oosterlaan, J., de Beurs, E., & Van den Brink, W. (2004). Pathological gambling: A comprehensive review of biobehavioral findings. Neuroscience and Biobehavioral Reviews, 28, 123–141.
- Griffiths, M. D. (1990). The acquisition, development, and maintenance of fruit machine gambling in adolescents. Journal of Gambling Studies, 6, 193–204.
- Haftorn, S. (1976). Variation in body weight, wing length and tail length in the great tit Parus major. Norwegian Journal of Zoology, 4, 241–271.
- Hart, A. S., Clark, J. J., & Phillips, P. E. M. (2015). Dynamic shaping of dopamine signals during probabilistic Pavlovian conditioning. Neurobiology of Learning and Memory, 117, 84–92.
- Hunt, W. A., Barnett, L. W., & Branch, L. G. (1971). Relapse rates in addiction programs. Journal of Clinical Psychology, 27, 455–456.
- Johansson, A., Grant, J. E., Kim, S. W., Odlaug, B. L., & Götestam, K. G. (2009). Risk factors for problematic gambling: A critical literature review. Journal of Gambling Studies, 25, 67–92.
- Joutsa, J., Johansson, J., Niemelä, S., Ollikainen, A., Hirvonen, M. M., Piepponen, P., . . . & Kaasinen, V. (2012). Mesolimbic dopamine release is linked to symptom severity in pathological gambling. NeuroImage, 60, 1992–1999.
- Kassinove, J. I., & Schare, M. L. (2001). Effects of the “near miss” and the “big win” on persistence at slot machine gambling. Psychology of Addictive Behavior, 15, 155–158.
- Keeling, L. J., & Hurnik, J. F. (1996). Social facilitation acts more on the appetitive than the consummatory phase of feeding behaviour of domestic fowl. Animal Behavior, 52, 11–15.
- King, J. R., & Farner, D. S. (1965). Studies of fat deposition in migratory birds. Annals of the New York Academy of Science, 131, 422–440.
- Kohn A. (1992). No Contest: The Case against Competition. Boston, MA: Houghton-Mifflin.
- Krams, I. (2000). Length of feeding day and body weight of great tits in a single- and two-predator environment. Behavioral Ecology and Sociobiology, 48, 147–153.
- Ladouceur, R., Jacques, C., Ferland, F., & Girouz, I. (1999). Prevalence of problem gambling: A replication study 7 years later. Canadian Journal of Psychiatry, 44, 802–804.
- Laran, J., & Salerno, A. (2013). Life-history strategy, food choice, and caloric consumption. Psychological Science, 24, 167–173.
- Ledgerwood, D. M., & Petry, N. M. (2006). Psychological experience of gambling and subtypes of pathological gamblers. Psychiatry Research, 144, 17–27.
- Linnet, J., Mouridsen, K., Peterson, E., Møller, A., Doudet, D. J., & Gjedde, A. (2012). Striatal dopamine release codes uncertainty in pathological gambling. Psychiatry Research, 204, 55–60.
- Lomanowska, A. M., Lovic, V., Rankine, M. J., Mooney, S. J., Robinson, T. E., & Kraemer, G. W. (2011). Inadequate early social experience increases the incentive salience of reward-related cues in adulthood. Behavioural Brain Research, 220, 91–99.
- Mazur, J. E. (1991). Choice with probabilistic reinforcement: Effects of delay and conditioned reinforcers. Journal of the Experimental Analysis of Behavior, 55, 63–77.
- McDevitt, M. A., Dunn, R. M., Spetch, M. L., & Ludvig, E. A. (2016). When good news leads to bad choices. Journal of the Experimental Analysis of Behavior, 105, 23–40.
- Mentzoni, R. A., Laberg, J. C., Brunborg, G. S., Molde, H., & Pallesen, S. (2014). Type of musical soundtrack affects behavior in gambling. Journal of Behavioral Addictions, 3, 102–106.
- Nader, J., Chauvet, C., Rawas, R. E., Favot, L., Jaber, M., Thiriet, N., & Solinas, M. (2012). Loss of environmental enrichment increases vulnerability to cocaine addiction. Neuropsychopharmacology, 37, 1579–1587.
- Nettle, D., Andrews, C., & Bateson, M. (2017). Food insecurity as a driver of obesity in humans: The insurance hypothesis. Behavioural and Brain Sciences, 40, 1–53.
- Nower, L., & Blaszczynski, A. (2010). Gambling motivations, money-limiting strategies, and precommitment preferences of problem versus non-problem gamblers. Journal of Gambling Studies, 26, 361–372.
- Pattison, K. F., Laude, J. R., & Zentall, T. R. (2013). Environmental enrichment affects suboptimal, risky, gambling-like choice by pigeons. Animal Cognition, 16, 429–434.
- Orians, G. H., & Heerwagen, J. H. (1992). Evolved responses to landscapes. In J. H. Barkow, L. Cosmides, & J. Tooby (Eds.), The adapted mind: Evolutionary psychology and the generation of culture (pp. 555–579). New York, NY: Oxford University Press.
- Panksepp, J. (1998). Affective Neuroscience: The Foundations of Human and Animal Emotions. Oxford: Oxford University Press.
- Parke, J., & Griffiths, M. (2004). Gambling addiction and the evolution of the “near miss.” Addiction Research and Theory, 12, 407–411.
- Parke, A., Griffiths, M., & Irwing, P. (2004). Personality traits in pathological gambling: Sensation seeking, deferment of gratification and competitiveness as risk factors. Addiction Research and Theory, 12, 201–212.
- Peciña, S., Cagniard, B., Berridge, K. C., Aldridge, J. W., & Zhuang, X. (2003). Hyperdopaminergic mutant mice have higher “wanting” but not “liking” for sweet rewards. Behavioral Neuroscience, 23, 9395–9402.
- Pinker, S. (1997). How the mind works. New York, NY: Norton & Company.
- Plowright, C. M. S., & Redmond, D. (1996). The effect of competition on choice by pigeons: Foraging rate, resource availability and learning. Behavioural Processes, 38, 277–285.
- Pravosudov, V. V. (2003). Long-term moderate elevation of corticosterone facilitates avian food-caching behavior and enhances spatial memory. Proceedings of the Royal Society B, 270, 2599–2604.
- Pravosudov, V. V., & Grubb, T. C. (1997). Management of fat reserves and food caches in tufted titmice (Parus bicolor) in relation to unpredictable food supply. Behavioral Ecology, 8, 332–339.
- Pravosudov, V. V., & Grubb, T. C. (1998). Management of fat reserves in tufted titmice Baelophus bicolor in relation to risk of predation. Animal Behavior, 56, 49–54.
- Quoilin, C., Didone, V., Tirelli, E., & Quertemont, E. (2010). Ontogeny of the stimulant and sedative effects of ethanol in male and female Swiss mice: Gradual changes from weaning to adulthood. Psychopharmacology, 212, 501–512.
- Rescorla, R. A. (1968). Probability of shock in the presence and absence of CS in fear conditioning. Journal of Comparative and Physiological Psychology, 66, 1–5.
- Robinson, M. J. F., Anselme, P., Suchomel, K., & Berridge, K. C. (2015). Amphetamine-induced sensitization and reward uncertainty similarly enhance the incentive salience of conditioned cues. Behavioral Neuroscience, 129, 502–511.
- Robinson, T. E., & Berridge, K. C. (1993). The neural basis of drug craving: An incentive-sensitization theory of addiction. Brain Research Review, 18, 247–291.
- Robinson, T. E., & Flagel, S. B. (2009). Dissociating the predictive and incentive motivational properties of reward-related cues through the study of individual differences. Biological Psychology, 65, 869–873.
- Schwartz, D. G. (2006). Roll the Bones: The History of Gambling. Gotham: Verlag.
- Shaffer, H. J., Hall, M. N., & Vander Bilt, J. (1999). Estimating the prevalence of disordered gambling behavior in the United States and Canada: A research synthesis. American Journal of Public Health, 89, 1369–1376.
- Singer, B. F., Scott-Railton, J., & Vezina, P. (2012). Unpredictable saccharin reinforcement enhances locomotor responding to amphetamine. Behavioural Brain Research, 226, 340–344.
- Smith, A. P., & Zentall, T. R. (2016). Suboptimal choice in pigeons: Choice is primarily based on the value of the conditioned reinforcers rather than overall reinforcement rate. Journal of Experimental Psychology: Animal Learning and Cognition, 42, 212–220.
- Spear, L. P., & Varlinskaya, E. I. (2010). Sensitivity to ethanol and other hedonic stimuliin an animal model of adolescence, implications for prevention science? Developmental Psychobiology, 52, 236–243.
- Spetch, M. L., Belke, T. W., Barnet, R. C., Dunn, R., & Pierce, W. D. (1990). Suboptimal choice in a percentage-reinforcement procedure: Effects of signal condition and terminal-link length. Journal of the Experimental Analysis of Behavior, 53, 219–234.
- Stagner, J. P., & Zentall, T. R. (2010). Suboptimal choice behavior by pigeons. Psychonomic Bulletin and Review, 17, 412–416.
- Swaffield, J., & Roberts, S. C. (2015). Exposure to cues of harsh or safe environmental conditions alters food preference. Evolutionary Psychological Science, 1, 69–76.
- Tomie, A., Badawy, N., & Rutyna, J. (2016). Sign-tracking model of loss of self-control of drug-taking. In Recent Advances in Substance Abuse. AvidScience, 2–64.
- Torres, C., Glueck, A. C., Conrad, S. E., Moron, I., & Papini, M. R. (2016). Dorsomedial striatum lesions affect adjustment to reward uncertainty, but not to reward devaluation or omission. Neuroscience, 332, 13–25.
- Tremblay, M., Silveira, M. M., Kaur, S., Hosking, J. G., Adams, W. K., Baunez, C., & Winstanley, C. A. (2017). Chronic D2/3 agonist ropinirole treatment increases preference for uncertainty in rats regardless of baseline choice patterns. European Journal of Neuroscience, 45, 159–166.
- Tulving, E. (2005). Episodic memory and autonoesis: Uniquely human? In H. S. Terrace & J. Metcalfe (Eds.), The missing link in cognition: Origins of self-reflective consciousness (pp. 3–56). Oxford: Oxford University Press.
- van Balen, J. H. (1980). Population fluctuations of the great tit and feeding conditions in winter. Ardea, 68, 143–164.
- van den Bos, R., Davies, W., Dellu-Hagedorn, F., Goudriaan, A. E., Granon, S., Homberg, J., Rivalan, M., Swendsen, J., & Adriani, W. (2013). Cross-species approaches to pathological gambling: A review targeting sex differences, adolescent vulnerability and ecological validity of research tools. Neuroscience and Biobehavioral Reviews, 37, 2454–2471.
- van Holst, R. J., van den Brink, W., Veltman, D. J., & Goudriaan, A. E. (2010). Why gamblers fail to win: A review of cognitive and neuroimaging findings in pathological gambling. Neuroscience and Biobehavioral Reviews, 34, 87–107.
- Vasconcelos, M., Monteiro, T., & Kacelnik, A. (2015). Irrational choice and the value of information. Scientific Reports 5, 13874.
- Voon, V., Gao, J., Brezing, C., Symmonds, M., Ekanayake, V., Fernandez, H., Dolan, R. J., & Hallett, M. (2011). Dopamine agonists and risk: Impulse control disorders in Parkinson’s disease. Brain, 134, 1438–1446.
- Wardle, H., Sproston, K., Orford, J., Erens, B., Griffiths, M., Constantine, R., & Pigott, S. (2007). British gambling prevalence survey 2007. National Center for Social Research.
- Weintraub, D., Koester, J., Potenza, M. N., Siderowf, A. D., Stacy, M., & Voon, V., et al. (2010). Impulse control disorders in Parkinson disease: A cross-sectional study of 3090 patients. Archives in Neurology, 67, 589–595.
- Welte, J. W., Barnes, G. M., Wieczorek, W. F., Tidwell, M. C. O., & Parker, J. C. (2004). Risk factors for pathological gambling. Addictive Behaviors, 29, 323–335.
- Xin, Q., Ogura, Y., Uno, L., & Matsushima, Y. (2017). Selective contribution of the telencephalic arcopallium to the social facilitation of foraging efforts in the domestic chick. European Journal of Neuroscience, 45, 365–380.
- Zack, M., Featherstone, R. E., Mathewson, S., & Fletcher, P. J. (2014). Chronic exposure to a gambling-like schedule of reward predictive stimuli can promote sensitization to amphetamine in rats. Frontiers in Behavioral Neuroscience, 8, 36.