Diminished reinforcement sensitivity in adolescence is associated with enhanced response switching and reduced coding of choice probability in the medial frontal pole

Relevance of Probabilistic Reversal Learning for Adolescent Drinking Trajectories

One of the many human capabilities acquired during adolescence is the adaptivity in changing environments. In this longitudinal study, we investigated this adaptivity, as measured by probabilistic reversal learning (PReL) tasks, in N = 143 adolescents at ages 14, 16 and 18. Computational modelling and functional magnetic resonance imaging were applied to identify the neurocognitive processes underlying reversal learning and its development. Previous studies have demonstrated a correlation between heavy alcohol use and impaired reversal learning. Our hypothesis was that PReL is negatively associated with current and future alcohol use and that alcohol use impairs PReL by altering neurocognitive processes. Behaviourally, PReL performance improved, which was associated with a lower probability of switching choices and was considered an adaptive process. Computationally, this was accounted for by higher learning rates, enhanced sensitivity to wins and reduced sensitivity to losses in older adolescents. Alcohol consumption increased but remained at a low level for most participants. More risky drinking was associated with less medial frontal activity elicited by reward prediction errors. These findings suggest that reversal learning may be more relevant for the maintenance or escalation of risky than for low-level drinking. Challenges and potential solutions for longitudinal studies such as reliability are discussed.

Neuroscience-informed psychoeducation for addiction: a conceptual and feasibility study

Over the past few decades, our understanding of substance use disorders (SUD) has been reshaped by evidence from neuroscience, which suggests that SUD are characterized by specific neuromarkers that transcend traditional diagnostic boundaries and act as pre-diagnostic markers that could be targeted through preventive attempts. Connectivity-based neuromarkers or brain networks have emerged as a promising framework, providing new insights into the neurocognitive mechanisms of SUD. Utilizing this data-driven framework assists prevention and intervention developers in offering a non-judgmental insight for adolescents regarding the potential vulnerability of neurocognitive systems to continued substance use. Given the importance of such awareness, this paper proposes a neural network-informed approach based on research domain criteria (RDoC) to characterize the content of neuroscience-informed psychoeducation designed for SUD. Furthermore, we argue that various features related to content and structure need to be considered when developing such interventions delivered through digital platforms (e.g., apps and websites). Finally, we introduce a theory-driven app called "NIPA", developed with the aim of increasing adolescents' awareness and resilience to the effects of drugs and other emotional triggers on brain and cognitive functions.

Neurocomputational Mechanisms Underlying Differential Reinforcement Learning From Wins and Losses in Obesity With and Without Binge Eating

BACKGROUND

Binge-eating disorder (BED) is thought of as a disorder of cognitive control, but evidence regarding its neurocognitive mechanisms is inconclusive. Key limitations of previous research include a lack of consistent separation between effects of BED and obesity and a disregard for self-report evidence suggesting that neurocognitive alterations may emerge primarily in loss- or harm-avoidance contexts.

METHODS

To address these gaps, in this longitudinal study we investigated behavioral flexibility and its underlying neurocomputational processes in reward-seeking and loss-avoidance contexts. Obese participants with BED, obese participants without BED, and healthy normal-weight participants (n = 96) performed a probabilistic reversal learning task during functional imaging, with different blocks focused on obtaining wins or avoiding losses. They were reinvited for a 6-month follow-up assessment.

RESULTS

Analyses informed by computational models of reinforcement learning showed that unlike obese participants with BED, obese participants without BED performed worse in the win than in the loss condition. Computationally, this was explained by differential learning sensitivities in the win versus loss conditions in the groups. In the brain, this was echoed in differential neural learning signals in the ventromedial prefrontal cortex per condition. The differences were subtle but scaled with BED symptoms, such that more severe BED symptoms were associated with increasing bias toward improved learning from wins versus losses. Across conditions, obese participants with BED switched more between choice options than healthy normal-weight participants. This was reflected in diminished representation of choice certainty in the ventromedial prefrontal cortex.

CONCLUSIONS

Our study highlights the importance of distinguishing between obesity with and without BED to identify unique neurocomputational alterations underlying different styles of maladaptive eating behavior.

Decrease in decision noise from adolescence into adulthood mediates an increase in more sophisticated choice behaviors and performance gain

Learning and decision-making undergo substantial developmental changes, with adolescence being a particular vulnerable window of opportunity. In adolescents, developmental changes in specific choice behaviors have been observed (e.g., goal-directed behavior, motivational influences over choice). Elevated levels of decision noise, i.e., choosing suboptimal options, were reported consistently in adolescents. However, it remains unknown whether these observations, the development of specific and more sophisticated choice processes and higher decision noise, are independent or related. It is conceivable, but has not yet been investigated, that the development of specific choice processes might be impacted by age-dependent changes in decision noise. To answer this, we examined 93 participants (12 to 42 years) who completed 3 reinforcement learning (RL) tasks: a motivational Go/NoGo task assessing motivational influences over choices, a reversal learning task capturing adaptive decision-making in response to environmental changes, and a sequential choice task measuring goal-directed behavior. This allowed testing of (1) cross-task generalization of computational parameters focusing on decision noise; and (2) assessment of mediation effects of noise on specific choice behaviors. Firstly, we found only noise levels to be strongly correlated across RL tasks. Second, and critically, noise levels mediated age-dependent increases in more sophisticated choice behaviors and performance gain. Our findings provide novel insights into the computational processes underlying developmental changes in decision-making: namely a vital role of seemingly unspecific changes in noise in the specific development of more complex choice components. Studying the neurocomputational mechanisms of how varying levels of noise impact distinct aspects of learning and decision processes may also be key to better understand the developmental onset of psychiatric diseases.

Adolescent-to-adult gains in cognitive flexibility are adaptively supported by reward sensitivity, exploration, and neural variability

Cognitive flexibility exhibits dynamic changes throughout development, with different forms of flexibility showing dissociable developmental trajectories. In this review, we propose that an adolescent-specific mode of flexibility in the face of changing environmental contingencies supports the emergence of adolescent-to-adult gains in cognitive shifting efficiency. We first describe how cognitive shifting abilities monotonically improve from childhood to adulthood, accompanied by increases in brain state flexibility, neural variability, and excitatory/inhibitory balance. We next summarize evidence supporting the existence of a dopamine-driven, adolescent peak in flexible behavior that results in reward seeking, undirected exploration, and environmental sampling. We propose a neurodevelopmental framework that relates these adolescent behaviors to the refinement of neural phenotypes relevant to mature cognitive flexibility, and thus highlight the importance of the adolescent period in fostering healthy neurocognitive trajectories.

A cognitive-computational account of mood swings in adolescence

Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

Impaired flexible reward learning in ADHD patients is associated with blunted reinforcement sensitivity and neural signals in ventral striatum and parietal cortex

Reward-based learning and decision-making are prime candidates to understand symptoms of attention deficit hyperactivity disorder (ADHD). However, only limited evidence is available regarding the neurocomputational underpinnings of the alterations seen in ADHD. This concerns flexible behavioral adaption in dynamically changing environments, which is challenging for individuals with ADHD. One previous study points to elevated choice switching in adolescent ADHD, which was accompanied by disrupted learning signals in medial prefrontal cortex. Here, we investigated young adults with ADHD (n = 17) as compared to age- and sex-matched controls (n = 17) using a probabilistic reversal learning experiment during functional magnetic resonance imaging (fMRI). The task requires continuous learning to guide flexible behavioral adaptation to changing reward contingencies. To disentangle the neurocomputational underpinnings of the behavioral data, we used reinforcement learning (RL) models, which informed the analysis of fMRI data. ADHD patients performed worse than controls particularly in trials before reversals, i.e., when reward contingencies were stable. This pattern resulted from 'noisy' choice switching regardless of previous feedback. RL modelling showed decreased reinforcement sensitivity and enhanced learning rates for negative feedback in ADHD patients. At the neural level, this was reflected in a diminished representation of choice probability in the left posterior parietal cortex in ADHD. Moreover, modelling showed a marginal reduction of learning about the unchosen option, which was paralleled by a marginal reduction in learning signals incorporating the unchosen option in the left ventral striatum. Taken together, we show that impaired flexible behavior in ADHD is due to excessive choice switching ('hyper-flexibility'), which can be detrimental or beneficial depending on the learning environment. Computationally, this resulted from blunted sensitivity to reinforcement of which we detected neural correlates in the attention-control network, specifically in the parietal cortex. These neurocomputational findings remain preliminary due to the relatively small sample size.

Understanding patch foraging strategies across development

Patch foraging is a near-ubiquitous behaviour across the animal kingdom and characterises many decision-making domains encountered by humans. We review how a disposition to explore in adolescence may reflect the evolutionary conditions under which hunter-gatherers foraged for resources. We propose that neurocomputational mechanisms responsible for reward processing, learning, and cognitive control facilitate the transition from exploratory strategies in adolescence to exploitative strategies in adulthood - where individuals capitalise on known resources. This developmental transition may be disrupted by psychopathology, as there is emerging evidence of biases in explore/exploit choices in mental health problems. Explore/exploit choices may be an informative marker for mental health across development and future research should consider this feature of decision-making as a target for clinical intervention.

Expecting the unexpected: a review of learning under uncertainty across development

Many of our decisions take place under uncertainty. To successfully navigate the environment, individuals need to estimate the degree of uncertainty and adapt their behaviors accordingly by learning from experiences. However, uncertainty is a broad construct and distinct types of uncertainty may differentially influence our learning. We provide a semi-systematic review to illustrate cognitive and neurobiological processes involved in learning under two types of uncertainty: learning in environments with stochastic outcomes, and with volatile outcomes. We specifically reviewed studies (N = 26 studies) that included an adolescent population, because adolescence is a period in life characterized by heightened exploration and learning, as well as heightened uncertainty due to experiencing many new, often social, environments. Until now, reviews have not comprehensively compared learning under distinct types of uncertainties in this age range. Our main findings show that although the overall developmental patterns were mixed, most studies indicate that learning from stochastic outcomes, as indicated by increased accuracy in performance, improved with age. We also found that adolescents tended to have an advantage compared with adults and children when learning from volatile outcomes. We discuss potential mechanisms explaining these age-related differences and conclude by outlining future research directions.