Scientists Try to Understand Addiction
Drug addiction is a serious problem that can harm people’s health and well-being. It used to be seen as a moral failure [2] but is now understood as a medical condition with biological causes.
People with drug addiction often engage in harmful behaviors, such as using drugs even when it causes problems in their lives or preferring drugs over other enjoyable activities.
Scientists have proposed different theories to explain why these behaviors happen, including problems with learning and decision-making and the brain’s reward system is more sensitive to drugs than other things.
The Rise of Computational Psychiatry in Addiction Studies
Scientists are using mathematical models to better understand psychiatric symptoms, and this has led to a new field called computational psychiatry. There are two types of methods used in this field: data-driven and theory-driven.
Theory-driven models are used more often in drug addiction research, as they can help break down behavior into smaller components to better understand the cognitive factors involved.
Different types of models are used depending on the addiction theory being studied.
A Recent Study on Computational Psychiatry
A recent review [1] looks at how computational methods can help us understand addictive behavior in humans.
Specifically, the review focuses on how certain computational models explain different symptoms of addiction, such as impaired control over drug use and the strong desire to use drugs.
The review summarizes these models and evaluates their effectiveness in advancing our understanding of drug addiction.
Other symptoms of addiction, like tolerance and withdrawal, are not included in the review because no computational models are available for these aspects of addiction.
A Break Down of the Study
In the study, scientists are using task experiments and computer models to understand better why people with addiction have trouble controlling their drug use.
They use a task where participants must make choices and learn from the outcome to create computer models that mimic how people make decisions.
They are looking at two types of decision-making: one based on goals and one based on habits, to see which is more commonly used by people with addiction.
Understanding How People Value Drugs
Scientists are using experiments and computer models to understand why people with addiction strongly desire to use drugs.
They use brain scans to examine how the brain reacts to drug cues and create computer models to understand why these cues make people want drugs.
They also use a task where people must choose how much they would pay for drugs to understand better how people value drugs and how this might be related to addiction.
Reinforcement Learning
Normally, when people do something that leads to a good outcome, they do it again.
This is called reinforcement learning. However, in drug addiction, people keep using drugs even when it causes problems. Researchers think this happens because the way reinforcement learning works is disrupted.
They use mathematical models to understand how this works and break down the different parts of the learning process. By doing this, they can identify which part is not working right in people with addiction.
They look at different parts, like how people learn from feedback, how much they stick to what they learned before, and how they respond to alternative rewards.
How Addiction Affects Rewards and Punishment Centers of the Brain
Research has shown that how people learn from rewards and punishments in their environment is related to certain brain parts, such as the dopamine system and frontostriatal networks.
Drugs can affect these parts of the brain and cause changes that affect the way people learn.
Recent studies have used computer models to look at how these changes work and found that people with addiction have less sensitivity to rewards and less communication between different parts of the brain.
To better understand these brain changes, scientists have developed computational models that break down how the brain learns from rewards and punishments.
These models have helped identify specific deficits in addicted patients, such as a reduced ability to learn from negative feedback, a greater tendency to repeat past choices, and a reduced ability to consider alternative rewards.
However, these models cannot be easily applied across different studies, as the parameters used to describe these deficits can vary depending on the specific tasks used to test them.
Therefore, it is essential to interpret the findings within the context of the specific behavioral paradigms used in each study.
How Drug Addiction Affects Goal-Directed and Habitual Behavior Control
Drug addiction is a disorder where drug use continues despite negative consequences. Two different systems in the brain regulate our actions: the goal-directed system and the habit system.
The goal-directed system helps us make adaptive choices based on the outcomes of our actions. In contrast, the habit system enables us to perform learned actions automatically without thinking about them.
In addition, drug use becomes more habitual over time and less sensitive to immediate consequences, leading to compulsive drug-seeking. The brain changes associated with drug use shift control from the goal-directed system to the habit system, which becomes dysregulated.
This shift is exacerbated by prefrontal cortical dysfunction, leading to further loss of control over drug use.
Habit formation is not pathological but can become maladaptive in addiction when the goal-directed system does not control automatic drug-seeking behaviors.
The Habit Model of Addiction Supported by Empirical Evidence
Habits in experimental psychology are defined as actions that are not sensitive to consequences and continue even when rewards are no longer needed. Experimentalists test these conditions with outcome devaluation and contingency degradation paradigms.
A predominance of the habit system in drug addiction may reflect either an enhanced habit system, an impaired goal-directed system, or a dysregulation between the two. Computational models of model-based and model-free reinforcement learning have been developed to dissociate the two systems.
Model-based learning evaluates actions against an internal model to identify the best action. In contrast, model-free learning maximizes future rewards by repeating actions that had been rewarded in the past.
The model-based and model-free systems are supported by distinct neural systems, which differ from those that subserve goal-directed actions and habits.
The transition from model-based to model-free control over behavior depends on the relative uncertainty of each system. The model-free system takes over control when there is low uncertainty.
Studies have shown that exposure to drugs and alcohol enhances habit formation for both drug and non-drug-related behaviors in rodents. Control over cocaine-seeking habits depends on dorsal striatal mechanisms.
Alcohol and cocaine-addicted patients are generally biased towards habitual responses in behavioral tasks. As measured with self-report instruments, psychostimulant drug users also report increased habitual tendencies in their daily lives.
Though crucial for this theory and dissociable in animal models, the distinction between drug-seeking and drug-taking may not readily be translatable to behavioral paradigms in humans.
Nevertheless, there has been a growing interest in applying computational methods to model these instrumental mechanisms in humans to test the extent to which addicted patients rely on habitual mechanisms of behavior in a non-drug-related context.
It is conceivable that drug addiction is linked with an increased reliance on model-free learning and a reduced tendency to engage in model-based learning.
The Habit Model of Addiction Supported by Empirical Evidence
A new computational model of habits and its potential implications in addiction research
Researchers have proposed a new computational model to describe habits as a product of pure repetition, called ‘value-free’ habits.
The model assumes two systems in the brain: the goal-directed controller and the habitual controller.
The goal-directed system tracks state transitions and reward values, while the habitual system updates habit strength based on repetition frequency.
An arbiter then determines the final behavior based on the strength of each system. Although this model has not been empirically tested, it seems consistent with existing knowledge about habits.
It could help explain data on contingency degradation, which the model-based / model-free account cannot explain. Future studies can compare this model to the existing model-free algorithm to determine which algorithm better fits behavioral data.
Understanding Environmental Based Drug Use Urges
People with drug addiction often report a strong urge to use drugs when they are exposed to environmental cues that are associated with drug use.
This can happen even after long periods of not using the drug, which makes them vulnerable to relapse.
Two main theories explain this urge: one suggests that it is because of the rewarding effects of the drug. At the same time, the other proposes that the brain’s motivational system becomes hyper-reactive to drug-related stimuli after long-term drug use.
This second theory, called incentive sensitization, suggests that the brain becomes hypersensitive to drug cues, leading to a compulsive urge to use drugs.
Studies using cue-reactivity paradigms have shown that drug-related cues elicit greater brain activity in drug-addicted patients than neutral cues, supporting the idea of incentive sensitization.
However, this idea has not been directly tested in humans.
Incentive Sensitization and the Zhang Model
The incentive sensitization theory suggests that drugs like cocaine, nicotine, and heroin can cause our brain’s reward system to become more sensitive to drug-related cues, which can lead to addiction.
The idea is that when our brain learns to associate certain stimuli with drugs, those cues become more attractive to us.
Some researchers have proposed that this process can be explained using a computer model called the temporal difference prediction error model.
However, this model doesn’t always account for how our physiological states, like hunger, affect our motivation.
The Zhang Model
Zhang and colleagues developed an alternative computer model called the Zhang model [3] to address this issue.
This model includes a factor that accounts for how our physiological states can modulate our incentive salience or how much we are attracted to certain stimuli.
They suggest that addictive drugs hijack this factor, making us “want” the drugs even when we don’t “like” them.
The Zhang model suggests that addictive drugs can change how our brains respond to things we want, making them more desirable even if they don’t have much value.
Studies have shown that certain brain cells in the ventral pallidum fire when we encounter something we want, and exposure to drugs like amphetamines can make this effect even stronger.
This sensitization effect can last long, even after the drug has worn off. Some researchers believe this change in the brain’s response to rewards can explain why addiction is hard to overcome.
The Zhang model shows that drug-related cues may increase a person’s desire for drugs, but it has not been tested in addicted patients.
It’s unclear whether the brain response to drug cues is due to prior learning or a stronger desire for drugs.
Most studies have been done on animals, so it’s unclear how this theory applies to humans.
Researchers have tried to link this theory to other aspects of addiction, such as learning.
Still, it’s unclear if these findings apply to human drug addiction. Studies are needed to understand the relationship between incentive salience and drug addiction..
Using Economic Demand Measures to Predict Addiction Severity
Theories of behavioral economics suggest that addicted patients have impaired decision-making and overvalue drugs, which leads to uncontrolled drug use.
This hypothesis is tested using the drug purchase task, which measures the economic demand for drugs.
Economic demand is the value of drugs when taking into account the cost of consumption.
Greater economic demand suggests that drugs are highly valued, and two key outputs of this model, demand intensity and elasticity, predict addiction severity.
High Demand Intensity
High demand intensity means more drugs are consumed when available, and low elasticity means drug consumption continues despite increasing costs.
Studies have shown that addiction severity is related to economic demand measures, which are ways of measuring how much people value drugs of abuse.
This means that people who are at risk for addiction may place more value on these drugs and may not change their drug use even if it becomes more expensive.
This has been found in people who use alcohol, tobacco, cocaine, and cannabis, as well as in rats.
Economic demand measures have also been used to identify patients who may respond better to treatment, such as those who value drugs of abuse less. However, the effects of these treatments may not last very long.
Economic Measures Can Predict Addiction Severity
Many studies show that economic measures of drug demand can predict addiction severity, but we don’t know exactly how chronic drug use causes people to value drugs too much.
Other theories might explain why people become addicted better than the economic model. We also don’t fully understand how long-term drug use affects how much people enjoy the drug.
People might overvalue drugs because of what they’ve learned from past experiences, but we must test this idea. Despite these limitations, the economic model is still useful for predicting addiction severity.
Advancements, Limitations, and Future Directions
In drug addiction research, computational models have helped us understand how the brain learns and makes addiction-related decisions.
These models give us detailed explanations of how these processes work behind the scenes.
However, most studies using these models haven’t looked directly at addiction-related behaviors because it’s hard to do so ethically. But overall, these models are a helpful tool that allows researchers to test their ideas about addiction.
Limitations
Drug addiction is a complex disorder that involves many different stages and factors. No single computational process can account for all aspects of the disorder.
Drug-addicted patients may cycle through different stages involving distinct theoretical processes, but these processes may also interact. Additionally, patients addicted to different types of drugs may be associated with different computational profiles.
Factors such as the duration and pattern of drug use and the age of drug use onset may also impact computational findings.
Therefore, computational models should be interpreted with the understanding that drug addiction is a multidimensional and complex disorder.
The Future of Computational Models in Addiction Research
Researchers have been using computational models to understand addiction better and potentially improve treatment in recent years.
These models can help detect individuals at risk of developing an addiction or identify those who may respond well to treatment.
However, while computational psychiatry has advanced our understanding of addiction, it has not yet led to any new psychological theories of addiction in humans.
Additionally, the translation from these models to clinical practice has been challenging due to the lack of standardization and low reliability of these measures. Some models may also lack biological or psychological relevance, which could limit their clinical usefulness.
Computational modeling is a useful tool in addiction research, but we must ensure these models are reliable and accurate.
Researchers are working on improving and testing these models in healthy individuals to ensure they are relevant to the studied behavioral process. We must also investigate how these models change over time in people with addiction to understand their relevance.
While there is some evidence that these models can predict treatment response, we need more studies to understand how they underpin the development and persistence of addictive behaviors.
Researchers need to critically examine the plausibility of these models before using them in research or clinical practice.
Sources
[1] Theory-driven computational models of drug addiction in humans: Fruitful or futile?
[2] A Moral Vision of Addiction: How People’s Values Determine Whether They Become and Remain Addicts
[3] The Zhang Model
