Goal-directed and habitual decision making under stress in gambling disorder: An fMRI study.

The development of addictive behaviors has been suggested to be related to a transition from goal-directed to habitual decision making. Stress is a factor known to prompt habitual behavior and to increase the risk for addiction and relapse. In the current study, we therefore used functional MRI to investigate the balance between goal-directed 'model-based' and habitual 'model-free' control systems and whether acute stress would differentially shift this balance in gambling disorder (GD) patients compared to healthy controls (HCs). Using a within-subject design, 22 patients with GD and 20 HCs underwent stress induction or a control condition before performing a multistep decision-making task during fMRI. Salivary cortisol levels showed that the stress induction was successful. Contrary to our hypothesis, GD patients did not show impaired goal-directed 'model-based' decision making, which remained similar to HCs after stress induction. Bayes factors provided three times more evidence against a difference between the groups or a group-by-stress interaction on the balance between model-based and model-free decision making. Similarly, no differences were found between groups and conditions on the neural estimates of model-based or model-free decision making. These results challenge the notion that GD is related to an increased reliance on habitual (or decreased goal-directed) control, even during stress.

A model for learning based on the joint estimation of stochasticity and volatility.

Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage.

Linear reinforcement learning in planning, grid fields, and cognitive control.

It is thought that the brain's judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we introduce a model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of specific, quantifiable biases. It replaces the classic nonlinear, model-based optimization with a linear approximation that softly maximizes around (and is weakly biased toward) a default policy. This solution demonstrates connections between seemingly disparate phenomena across behavioral neuroscience, notably flexible replanning with biases and cognitive control. It also provides insight into how the brain can represent maps of long-distance contingencies stably and componentially, as in entorhinal response fields, and exploit them to guide choice even under changing goals.

A simple model for learning in volatile environments.

Sound principles of statistical inference dictate that uncertainty shapes learning. In this work, we revisit the question of learning in volatile environments, in which both the first and second-order statistics of observations dynamically evolve over time. We propose a new model, the volatile Kalman filter (VKF), which is based on a tractable state-space model of uncertainty and extends the Kalman filter algorithm to volatile environments. The proposed model is algorithmically simple and encompasses the Kalman filter as a special case. Specifically, in addition to the error-correcting rule of Kalman filter for learning observations, the VKF learns volatility according to a second error-correcting rule. These dual updates echo and contextualize classical psychological models of learning, in particular hybrid accounts of Pearce-Hall and Rescorla-Wagner. At the computational level, compared with existing models, the VKF gives up some flexibility in the generative model to enable a more faithful approximation to exact inference. When fit to empirical data, the VKF is better behaved than alternatives and better captures human choice data in two independent datasets of probabilistic learning tasks. The proposed model provides a coherent account of learning in stable or volatile environments and has implications for decision neuroscience research.

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.

Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of our theory enables researchers to make inference on group-level parameters by performing HBI t-test.

Emotionally Aversive Cues Suppress Neural Systems Underlying Optimal Learning in Socially Anxious Individuals.

Learning and decision-making are modulated by socio-emotional processing and such modulation is implicated in clinically relevant personality traits of social anxiety. The present study elucidates the computational and neural mechanisms by which emotionally aversive cues disrupt learning in socially anxious human individuals. Healthy volunteers with low or high trait social anxiety performed a reversal learning task requiring learning actions in response to angry or happy face cues. Choice data were best captured by a computational model in which learning rate was adjusted according to the history of surprises. High trait socially anxious individuals used a less-dynamic strategy for adjusting their learning rate in trials started with angry face cues and unlike the low social anxiety group, their dorsal anterior cingulate cortex (dACC) activity did not covary with the learning rate. Our results demonstrate that trait social anxiety is accompanied by disruption of optimal learning and dACC activity in threatening situations.SIGNIFICANCE STATEMENT Social anxiety is known to influence a broad range of cognitive functions. This study tests whether and how social anxiety affects human value-based learning as a function of uncertainty in the learning environment. The findings indicate that, in a threatening context evoked by an angry face, socially anxious individuals fail to benefit from a stable learning environment with highly predictable stimulus-response-outcome associations. Under those circumstances, socially anxious individuals failed to use their dorsal anterior cingulate cortex, a region known to adjust learning rate to environmental uncertainty. These findings open the way to modify neurobiological mechanisms of maladaptive learning in anxiety and depressive disorders.

Mechanisms Underlying Dopamine-Induced Risky Choice in Parkinson's Disease With and Without Depression (History).

Patients with Parkinson's disease (PD) are often treated with dopaminergic medication. Dopaminergic medication is known to improve both motor and certain nonmotor symptoms, such as depression. However, it can contribute to behavioral impairment, for example, by enhancing risky choice. Here we characterize the computational mechanisms that contribute to dopamine-induced changes in risky choice in PD patients with and without a depression (history). We adopt a clinical-neuroeconomic approach to investigate the effects of dopaminergic medication on specific components of risky choice in PD. Twenty-three healthy controls, 21 PD patients with a depression (history), and 22 nondepressed PD patients were assessed using a well-established risky choice paradigm. Patients were tested twice: once after taking their normal dopaminergic medication and once after withdrawal of their medication. Dopaminergic medication increased a value-independent gambling propensity in nondepressed PD patients, while leaving loss aversion unaffected. By contrast, dopaminergic medication effects on loss aversion were associated with current depression severity and with drug effects on depression scores. The present findings demonstrate that dopaminergic medication increases a value-independent gambling bias in nondepressed PD patients. Moreover, the current study raises the hypothesis that dopamine-induced reductions in loss aversion might underlie previously observed comorbidity between depression and medication-related side effects in PD, such as impulse control disorder.

Dopaminergic Modulation of the Functional Ventrodorsal Architecture of the Human Striatum.

Interactions between motivational, cognitive, and motor regions of the striatum are crucial for implementing behavioral control. Work with experimental animals indicates that such interactions are sensitive to modulation by dopamine. Using systematic pharmacological manipulation of dopamine D2-receptors and resting-state functional imaging, we defined the functional architecture of the human striatum and quantified the effects of dopaminergic drugs on intrinsic effective connectivity between striatal subregions. We found that dopamine modulates interactions between motivational and cognitive regions, as well cognitive and motor regions of the striatum. Stimulation and blockade of the dopamine D2-receptor had opposite (increasing and decreasing) effects on the efficacy of those interactions. Furthermore, trait impulsivity was specifically associated with dopaminergic modulation of ventral-to-dorsal striatal connectivity. Individuals with high trait impulsivity exhibited greater drug-induced increases (after stimulation) and decreases (after blockade) of ventral-to-dorsal striatal connectivity than those with low trait impulsivity. These observations establish a key link between dopamine, intrinsic effective connectivity between striatal subregions, and trait impulsivity.

Human Choice Strategy Varies with Anatomical Projections from Ventromedial Prefrontal Cortex to Medial Striatum.

Two distinct systems, goal-directed and habitual, support decision making. It has recently been hypothesized that this distinction may arise from two computational mechanisms, model-based and model-free reinforcement learning, neuronally implemented in frontostriatal circuits involved in learning and behavioral control. Here, we test whether the relative strength of anatomical connectivity within frontostriatal circuits accounts for variation in human individuals' reliance on model-based and model-free control. This hypothesis was tested by combining diffusion tensor imaging with a multistep decision task known to distinguish model-based and model-free control in humans. We found large interindividual differences in the degree of model-based control, and those differences are predicted by the structural integrity of white-matter tracts from the ventromedial prefrontal cortex to the medial striatum. Furthermore, an analysis based on masking out of bottom-up tracts suggests that this effect is driven by top-down influences from ventromedial prefrontal cortex to medial striatum. Our findings indicate that individuals with stronger afferences from the ventromedial prefrontal cortex to the medial striatum are more likely to rely on a model-based strategy to control their instrumental actions. These findings suggest a mechanism for instrumental action control through which medial striatum determines, at least partly, the relative contribution of model-based and model-free systems during decision-making according to top-down model-based information from the ventromedial prefrontal cortex. These findings have important implications for understanding the neural circuitry that might be susceptible to pathological computational processes in impulsive/compulsive psychiatric disorders.

Impulse control disorders in Parkinson's disease are associated with dysfunction in stimulus valuation but not action valuation.

A substantial subset of Parkinson's disease (PD) patients suffers from impulse control disorders (ICDs), which are side effects of dopaminergic medication. Dopamine plays a key role in reinforcement learning processes. One class of reinforcement learning models, known as the actor-critic model, suggests that two components are involved in these reinforcement learning processes: a critic, which estimates values of stimuli and calculates prediction errors, and an actor, which estimates values of potential actions. To understand the information processing mechanism underlying impulsive behavior, we investigated stimulus and action value learning from reward and punishment in four groups of participants: on-medication PD patients with ICD, on-medication PD patients without ICD, off-medication PD patients without ICD, and healthy controls. Analysis of responses suggested that participants used an actor-critic learning strategy and computed prediction errors based on stimulus values rather than action values. Quantitative model fits also revealed that an actor-critic model of the basal ganglia with different learning rates for positive and negative prediction errors best matched the choice data. Moreover, whereas ICDs were associated with model parameters related to stimulus valuation (critic), PD was associated with parameters related to action valuation (actor). Specifically, PD patients with ICD exhibited lower learning from negative prediction errors in the critic, resulting in an underestimation of adverse consequences associated with stimuli. These findings offer a specific neurocomputational account of the nature of compulsive behaviors induced by dopaminergic drugs.

Neuroeconomics and the study of addiction.

We review the key findings in the application of neuroeconomics to the study of addiction. Although there are not "bright line" boundaries between neuroeconomics and other areas of behavioral science, neuroeconomics coheres around the topic of the neural representations of "Value" (synonymous with the "decision utility" of behavioral economics). Neuroeconomics parameterizes distinct features of Valuation, going beyond the general construct of "reward sensitivity" widely used in addiction research. We argue that its modeling refinements might facilitate the identification of neural substrates that contribute to addiction. We highlight two areas of neuroeconomics that have been particularly productive. The first is research on neural correlates of delay discounting (reduced Valuation of rewards as a function of their delay). The second is work that models how Value is learned as a function of "prediction-error" signaling. Although both areas are part of the neuroeconomic program, delay discounting research grows directly out of behavioral economics, whereas prediction-error work is grounded in models of learning. We also consider efforts to apply neuroeconomics to the study of self-control and discuss challenges for this area. We argue that neuroeconomic work has the potential to generate breakthrough research in addiction science.

Speed/accuracy trade-off between the habitual and the goal-directed processes.

Instrumental responses are hypothesized to be of two kinds: habitual and goal-directed, mediated by the sensorimotor and the associative cortico-basal ganglia circuits, respectively. The existence of the two heterogeneous associative learning mechanisms can be hypothesized to arise from the comparative advantages that they have at different stages of learning. In this paper, we assume that the goal-directed system is behaviourally flexible, but slow in choice selection. The habitual system, in contrast, is fast in responding, but inflexible in adapting its behavioural strategy to new conditions. Based on these assumptions and using the computational theory of reinforcement learning, we propose a normative model for arbitration between the two processes that makes an approximately optimal balance between search-time and accuracy in decision making. Behaviourally, the model can explain experimental evidence on behavioural sensitivity to outcome at the early stages of learning, but insensitivity at the later stages. It also explains that when two choices with equal incentive values are available concurrently, the behaviour remains outcome-sensitive, even after extensive training. Moreover, the model can explain choice reaction time variations during the course of learning, as well as the experimental observation that as the number of choices increases, the reaction time also increases. Neurobiologically, by assuming that phasic and tonic activities of midbrain dopamine neurons carry the reward prediction error and the average reward signals used by the model, respectively, the model predicts that whereas phasic dopamine indirectly affects behaviour through reinforcing stimulus-response associations, tonic dopamine can directly affect behaviour through manipulating the competition between the habitual and the goal-directed systems and thus, affect reaction time.

Individual differences in nucleus accumbens dopamine receptors predict development of addiction-like behavior: a computational approach.

Clinical and experimental observations show individual differences in the development of addiction. Increasing evidence supports the hypothesis that dopamine receptor availability in the nucleus accumbens (NAc) predisposes drug reinforcement. Here, modeling striatal-midbrain dopaminergic circuit, we propose a reinforcement learning model for addiction based on the actor-critic model of striatum. Modeling dopamine receptors in the NAc as modulators of learning rate for appetitive--but not aversive--stimuli in the critic--but not the actor--we define vulnerability to addiction as a relatively lower learning rate for the appetitive stimuli, compared to aversive stimuli, in the critic. We hypothesize that an imbalance in this learning parameter used by appetitive and aversive learning systems can result in addiction. We elucidate that the interaction between the degree of individual vulnerability and the duration of exposure to drug has two progressive consequences: deterioration of the imbalance and establishment of an abnormal habitual response in the actor. Using computational language, the proposed model describes how development of compulsive behavior can be a function of both degree of drug exposure and individual vulnerability. Moreover, the model describes how involvement of the dorsal striatum in addiction can be augmented progressively. The model also interprets other forms of addiction, such as obesity and pathological gambling, in a common mechanism with drug addiction. Finally, the model provides an answer for the question of why behavioral addictions are triggered in Parkinson's disease patients by D2 dopamine agonist treatments.

A neurocomputational model for cocaine addiction.

Based on the dopamine hypotheses of cocaine addiction and the assumption of decrement of brain reward system sensitivity after long-term drug exposure, we propose a computational model for cocaine addiction. Utilizing average reward temporal difference reinforcement learning, we incorporate the elevation of basal reward threshold after long-term drug exposure into the model of drug addiction proposed by Redish. Our model is consistent with the animal models of drug seeking under punishment. In the case of nondrug reward, the model explains increased impulsivity after long-term drug exposure. Furthermore, the existence of a blocking effect for cocaine is predicted by our model.