Hemispheric dissociation of reward processing in humans: insights from deep brain stimulation

Human thalamic low-frequency oscillations correlate with expected value and outcomes during reinforcement learning

Reinforcement-based adaptive decision-making is believed to recruit fronto-striatal circuits. A critical node of the fronto-striatal circuit is the thalamus. However, direct evidence of its involvement in human reinforcement learning is lacking. We address this gap by analyzing intra-thalamic electrophysiological recordings from eight participants while they performed a reinforcement learning task. We found that in both the anterior thalamus (ATN) and dorsomedial thalamus (DMTN), low frequency oscillations (LFO, 4-12 Hz) correlated positively with expected value estimated from computational modeling during reward-based learning (after outcome delivery) or punishment-based learning (during the choice process). Furthermore, LFO recorded from ATN/DMTN were also negatively correlated with outcomes so that both components of reward prediction errors were signaled in the human thalamus. The observed differences in the prediction signals between rewarding and punishing conditions shed light on the neural mechanisms underlying action inhibition in punishment avoidance learning. Our results provide insight into the role of thalamus in reinforcement-based decision-making in humans.

A Causal Role for the Pedunculopontine Nucleus in Human Instrumental Learning

A critical mechanism for maximizing reward is instrumental learning. In standard instrumental learning models, action values are updated on the basis of reward prediction errors (RPEs), defined as the discrepancy between expectations and outcomes. A wealth of evidence across species and experimental techniques has established that RPEs are signaled by midbrain dopamine neurons. However, the way dopamine neurons receive information about reward outcomes remains poorly understood. Recent animal studies suggest that the pedunculopontine nucleus (PPN), a small brainstem structure considered as a locomotor center, is sensitive to reward and sends excitatory projection to dopaminergic nuclei. Here, we examined the hypothesis that the PPN could contribute to reward learning in humans. To this aim, we leveraged a clinical protocol that assessed the therapeutic impact of PPN deep-brain stimulation (DBS) in three patients with Parkinson disease. PPN local field potentials (LFPs), recorded while patients performed an instrumental learning task, showed a specific response to reward outcomes in a low-frequency (alpha-beta) band. Moreover, PPN DBS selectively improved learning from rewards but not from punishments, a pattern that is typically observed following dopaminergic treatment. Computational analyses indicated that the effect of PPN DBS on instrumental learning was best captured by an increase in subjective reward sensitivity. Taken together, these results support a causal role for PPN-mediated reward signals in human instrumental learning.

Why not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases

Motivation deficits, such as apathy, are pervasive in both neurological and psychiatric diseases. Even when they are not the core symptom, they reduce quality of life, compromise functional outcome and increase the burden for caregivers. They are currently assessed with clinical scales that do not give any mechanistic insight susceptible to guide therapeutic intervention. Here, we present another approach that consists of phenotyping the behaviour of patients in motivation tests, using computational models. These formal models impose a precise and operational definition of motivation that is embedded in decision theory. Motivation can be defined as the function that orients and activates the behaviour according to two attributes: a content (the goal) and a quantity (the goal value). Decision theory offers a way to quantify motivation, as the cost that patients would accept to endure in order to get the benefit of achieving their goal. We then review basic and clinical studies that have investigated the trade-off between the expected cost entailed by potential actions and the expected benefit associated with potential rewards. These studies have shown that the trade-off between effort and reward involves specific cortical, subcortical and neuromodulatory systems, such that it may be shifted in particular clinical conditions, and reinstated by appropriate treatments. Finally, we emphasize the promises of computational phenotyping for clinical purposes. Ideally, there would be a one-to-one mapping between specific neural components and distinct computational variables and processes of the decision model. Thus, fitting computational models to patients' behaviour would allow inferring of the dysfunctional mechanism in both cognitive terms (e.g. hyposensitivity to reward) and neural terms (e.g. lack of dopamine). This computational approach may therefore not only give insight into the motivation deficit but also help personalize treatment.

Influence of subthalamic deep-brain stimulation on cognitive action control in incentive context

Subthalamic nucleus deep-brain stimulation (STN-DBS) is an effective treatment in Parkinson's disease (PD), but can have cognitive side effects, such as increasing the difficulty of producing appropriate responses when a habitual but inappropriate responses represent strong alternatives. STN-DBS also appears to modulate representations of incentives such as monetary rewards. Furthermore, conflict resolution can be modulated by incentive context. We therefore used a rewarded Simon Task to assess the influence of promised rewards on cognitive action control in 50 patients with PD, half of whom were being treated with STN-DBS. Results were analyzed according to the activation-suppression model. We showed that STN-DBS (i) favored the expression of motor impulsivity, as measured with the Barratt Impulsiveness Scale, (ii) facilitated the expression of incentive actions as observed with a greater increase in speed according to promised reward in patients with versus without DBS and (iii) may increase impulsive action selection in an incentive context. In addition, analysis of subgroups of implanted patients suggested that those who exhibited the most impulsive action selection had the least severe disease. This may indicate that patients with less marked disease are more at risk of developing impulsivity postoperatively. Finally, in these patients, incentive context increased the difficulty of resolving conflict situations. As a whole, the current study revealed that in patients with PD, STN-DBS affects the cognitive processes involved in conflict resolution, reward processing and the influence of promised rewards on conflict resolution.

The Computational Development of Reinforcement Learning during Adolescence

Adolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. Here, we aimed to trace the developmental time-course of the computational modules responsible for learning from reward or punishment, and learning from counterfactual feedback. Adolescents and adults carried out a novel reinforcement learning paradigm in which participants learned the association between cues and probabilistic outcomes, where the outcomes differed in valence (reward versus punishment) and feedback was either partial or complete (either the outcome of the chosen option only, or the outcomes of both the chosen and unchosen option, were displayed). Computational strategies changed during development: whereas adolescents’ behaviour was better explained by a basic reinforcement learning algorithm, adults’ behaviour integrated increasingly complex computational features, namely a counterfactual learning module (enabling enhanced performance in the presence of complete feedback) and a value contextualisation module (enabling symmetrical reward and punishment learning). Unlike adults, adolescent performance did not benefit from counterfactual (complete) feedback. In addition, while adults learned symmetrically from both reward and punishment, adolescents learned from reward but were less likely to learn from punishment. This tendency to rely on rewards and not to consider alternative consequences of actions might contribute to our understanding of decision-making in adolescence.

We employed a novel learning task to investigate how adolescents and adults learn from reward versus punishment, and to counterfactual feedback about decisions. Computational analyses revealed that adults and adolescents did not implement the same algorithm to solve the learning task. In contrast to adults, adolescents’ performance did not take into account counterfactual information; adolescents also learned preferentially to seek rewards rather than to avoid punishments, whereas adults learned to seek and avoid both equally. Increasing our understanding of computational changes in reinforcement learning during adolescence may provide insights into adolescent value-based decision-making. Our results might also have implications for education, since they suggest that adolescents benefit more from positive feedback than from negative feedback in learning tasks.

The left hemisphere learns what is right: Hemispatial reward learning depends on reinforcement learning processes in the contralateral hemisphere

Orienting biases refer to consistent, trait-like direction of attention or locomotion toward one side of space. Recent studies suggest that such hemispatial biases may determine how well people memorize information presented in the left or right hemifield. Moreover, lesion studies indicate that learning rewarded stimuli in one hemispace depends on the integrity of the contralateral striatum. However, the exact neural and computational mechanisms underlying the influence of individual orienting biases on reward learning remain unclear. Because reward-based behavioural adaptation depends on the dopaminergic system and prediction error (PE) encoding in the ventral striatum, we hypothesized that hemispheric asymmetries in dopamine (DA) function may determine individual spatial biases in reward learning. To test this prediction, we acquired fMRI in 33 healthy human participants while they performed a lateralized reward task. Learning differences between hemispaces were assessed by presenting stimuli, assigned to different reward probabilities, to the left or right of central fixation, i.e. presented in the left or right visual hemifield. Hemispheric differences in DA function were estimated through differential fMRI responses to positive vs. negative feedback in the left vs. right ventral striatum, and a computational approach was used to identify the neural correlates of PEs. Our results show that spatial biases favoring reward learning in the right (vs. left) hemifield were associated with increased reward responses in the left hemisphere and relatively better neural encoding of PEs for stimuli presented in the right (vs. left) hemifield. These findings demonstrate that trait-like spatial biases implicate hemisphere-specific learning mechanisms, with individual differences between hemispheres contributing to reinforcing spatial biases.

Reinforcement learning and Tourette syndrome

In this chapter, we report the first experimental explorations of reinforcement learning in Tourette syndrome, realized by our team in the last few years. This report will be preceded by an introduction aimed to provide the reader with the state of the art of the knowledge concerning the neural bases of reinforcement learning at the moment of these studies and the scientific rationale beyond them. In short, reinforcement learning is learning by trial and error to maximize rewards and minimize punishments. This decision-making and learning process implicates the dopaminergic system projecting to the frontal cortex-basal ganglia circuits. A large body of evidence suggests that the dysfunction of the same neural systems is implicated in the pathophysiology of Tourette syndrome. Our results show that Tourette condition, as well as the most common pharmacological treatments (dopamine antagonists), affects reinforcement learning performance in these patients. Specifically, the results suggest a deficit in negative reinforcement learning, possibly underpinned by a functional hyperdopaminergia, which could explain the persistence of tics, despite their evident inadaptive (negative) value. This idea, together with the implications of these results in Tourette therapy and the future perspectives, is discussed in Section 4 of this chapter.