BACKGROUND: Impulse control disorders (ICD) in Parkinson\'s disease (PD) are associated with a heavy burden on patients and caretakers. While recovery can occur, ICD persists in many patients despite optimal management. The basis for this inter-individual variability in recovery is unclear and poses a major challenge to personalized health care.
METHODS: We adopt a computational psychiatry approach and leverage the longitudinal, prospective Personalized Parkinson Project (N=136 persons with PD, within 5 years of diagnosis) to combine dopaminergic learning theory-informed fMRI with machine learning (at baseline) to predict ICD symptom recovery after two years of follow-up. We focused on a change in QUIP-rs across the entire cohort, regardless of an ICD diagnosis.
RESULTS: Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum, medial prefrontal cortex and the behavioral accuracy score measured while ON medication were associated with greater recovery from impulse control symptoms two years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use.
CONCLUSIONS: Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. Hence, we showed that RL modelling parameters predict recovery from ICD symptoms in PD.

The learning process encompasses exploration and exploitation phases. While reinforcement learning models have revealed functional and neuroscientific distinctions between these phases, knowledge regarding how they affect visual attention while observing the external environment is limited. This study sought to elucidate the interplay between these learning phases and visual attention allocation using visual adjustment tasks combined with a two-armed bandit problem tailored to detect serial effects only when attention is dispersed across both arms. Per our findings, human participants exhibited a distinct serial effect only during the exploration phase, suggesting enhanced attention to the visual stimulus associated with the non-target arm. Remarkably, although rewards did not motivate attention dispersion in our task, during the exploration phase, individuals engaged in active observation and searched for targets to observe. This behavior highlights a unique information-seeking process in exploration that is distinct from exploitation.

The stabilization of human quiet stance is achieved by a combination of the intrinsic elastic properties of ankle muscles and an active closed-loop activation of the ankle muscles, driven by the delayed feedback of the ongoing sway angle and the corresponding angular velocity in a way of a delayed proportional (P) and derivative (D) feedback controller. It has been shown that the active component of the stabilization process is likely to operate in an intermittent manner rather than as a continuous controller: the switching policy is defined in the phase-plane, which is divided in dangerous and safe regions, separated by appropriate switching boundaries. When the state enters a dangerous region, the delayed PD control is activated, and it is switched off when it enters a safe region, leaving the system to evolve freely. In comparison with continuous feedback control, the intermittent mechanism is more robust and capable to better reproduce postural sway patterns in healthy people. However, the superior performance of the intermittent control paradigm as well as its biological plausibility, suggested by experimental evidence of the intermittent activation of the ankle muscles, leaves open the quest of a feasible learning process, by which the brain can identify the appropriate state-dependent switching policy and tune accordingly the P and D parameters. In this work, it is shown how such a goal can be achieved with a reinforcement motor learning paradigm, building upon the evidence that, in general, the basal ganglia are known to play a central role in reinforcement learning for action selection and, in particular, were found to be specifically involved in postural stabilization.

Different dopamine subtypes have opposing dynamics at post-synaptic receptors, with the ratio of D1 to D2 receptors determining the relative sensitivity to gains and losses, respectively, during value-based learning. This effective sensitivity to different reward feedback interacts with phasic dopamine levels to determine the effectiveness of learning, particularly in dynamic feedback situations where frequency and magnitude of rewards need to be integrated over time to make optimal decisions. We modeled this effect in simulations of the underlying basal ganglia pathways and then tested the predictions in individuals with a variant of the human dopamine receptor D2 (DRD2; -141C Ins/Del and Del/Del) gene that associates with lower levels of D2 receptor expression (N=119) and compared their performance in the Iowa Gambling Task (IGT) to non-carrier controls (N=319). Ventral striatal (VS) reactivity to rewards was measured in the Cards task with fMRI. DRD2 variant carriers made less effective decisions than non-carriers, but this effect was not moderated by VS reward reactivity as is hypothesized by our model. These results suggest that the interaction between dopamine receptor subtypes and reactivity to rewards during learning may be more complex than originally thought.

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don\'t need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

The reinforcement learning based hyper-heuristics (RL-HH) is a popular trend in the field of optimization. RL-HH combines the global search ability of hyper-heuristics (HH) with the learning ability of reinforcement learning (RL). This synergy allows the agent to dynamically adjust its own strategy, leading to a gradual optimization of the solution. Existing researches have shown the effectiveness of RL-HH in solving complex real-world problems. However, a comprehensive introduction and summary of the RL-HH field is still blank. This research reviews currently existing RL-HHs and presents a general framework for RL-HHs. This article categorizes the type of algorithms into two categories: value-based reinforcement learning hyper-heuristics and policy-based reinforcement learning hyper-heuristics. Typical algorithms in each category are summarized and described in detail. Finally, the shortcomings in existing researches on RL-HH and future research directions are discussed.

Stochastic Calculus-guided Reinforcement learning (SCRL) is a new way to make decisions in situations where things are uncertain. It uses mathematical principles to make better choices and improve decision-making in complex situations. SCRL works better than traditional Stochastic Reinforcement Learning (SRL) methods. In tests, SCRL showed that it can adapt and perform well. It was better than the SRL methods. SCRL had a lower dispersion value of 63.49 compared to SRL\'s 65.96. This means SCRL had less variation in its results. SCRL also had lower risks than SRL in the short- and long-term. SCRL\'s short-term risk value was 0.64, and its long-term risk value was 0.78. SRL\'s short-term risk value was much higher at 18.64, and its long-term risk value was 10.41. Lower risk values are better because they mean less chance of something going wrong. Overall, SCRL is a better way to make decisions when things are uncertain. It uses math to make smarter choices and has less risk than other methods. Also, different metrics, viz training rewards, learning progress, and rolling averages between SRL and SCRL, were assessed, and the study found that SCRL outperforms well compared to SRL. This makes SCRL very useful for real-world situations where decisions must be made carefully.•By leveraging mathematical principles derived from stochastic calculus, SCRL offers a robust framework for making informed choices and enhancing performance in complex scenarios.•In comparison to traditional SRL methods, SCRL demonstrates superior adaptability and efficacy, as evidenced by empirical tests.

Reward processing dysfunction and inhibition control deficiency have been observed in Internet gaming disorder (IGD). However, it is still unclear whether the previous reinforcement learning depends on reward/punishment feedback influences on the cognitive inhibitory control of IGD. This study compared the differences between an IGD group and healthy people without game experiences in the probability selection task and the subsequent stop signal task by the method of behavioral experiments, in order to explore whether the reward learning ability is impaired in the IGD group. We also discuss the influence of previous reward learning on subsequent inhibition control. The results showed that (1) during the reward learning phase, the IGD group\'s accuracy was significantly lower than that of the control group; (2) compared with the control group, the IGD group\'s reaction times were longer in the transfer phase; (3) for no-go trials of the inhibitory control phase after reward learning, the accuracy of the reward-related stimulation in the IGD group was lower than that of punishment-related or neutral stimulation, but there was no significant difference among the three conditions in the control group. These findings indicated that the reinforcement learning ability of the IGD group was impaired, which further caused the abnormal response to reinforcement stimuli.

SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and fragment linking applications are sometimes desirable which requires a different grammar, architecture, training dataset and therefore, re-training of a new model. In this work, we describe a simple procedure to conduct constrained molecule generation with a SMILES-based generative model to extend applicability to scaffold decoration and fragment linking by providing SMILES prompts, without the need for re-training. In combination with reinforcement learning, we show that pre-trained, decoder-only models adapt to these applications quickly and can further optimize molecule generation towards a specified objective. We compare the performance of this approach to a variety of orthogonal approaches and show that performance is comparable or better. For convenience, we provide an easy-to-use python package to facilitate model sampling which can be found on GitHub and the Python Package Index.Scientific contributionThis novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn\'t require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.

Songbirds\' vocal mastery is impressive, but to what extent is it a result of practice? Can they, based on experienced mismatch with a known target, plan the necessary changes to recover the target in a practice-free manner without intermittently singing? In adult zebra finches, we drive the pitch of a song syllable away from its stable (baseline) variant acquired from a tutor, then we withdraw reinforcement and subsequently deprive them of singing experience by muting or deafening. In this deprived state, birds do not recover their baseline song. However, they revert their songs toward the target by about 1 standard deviation of their recent practice, provided the sensory feedback during the latter signaled a pitch mismatch with the target. Thus, targeted vocal plasticity does not require immediate sensory experience, showing that zebra finches are capable of goal-directed vocal planning.