Computational mechanisms underpinning greater exploratory behaviour in excess weight relative to healthy weight adolescents

Obesity in adolescence is associated with cognitive changes that lead to difficulties in shifting unhealthy habits in favour of alternative healthy behaviours, similar to addictive behaviours. An outstanding question is whether this shift in goal-directed behaviour is driven by over-exploitation or over-exploration of rewarding outcomes. Here, we addressed this question by comparing explore/exploit behaviour on the Iowa Gambling Task in 43 adolescents with excess weight against 38 adolescents with healthy weight. We computationally modelled both exploitation behaviour (e.g., reinforcement sensitivity and inverse decay parameters), and explorative behaviour (e.g., maximum directed exploration value). We found that overall, adolescents with excess weight displayed more behavioural exploration than their healthy-weight counterparts – specifically, demonstrating greater overall switching behaviour. Computational models revealed that this behaviour was driven by a higher maximum directed exploration value in the excess-weight group (U = 520.00, p = .005, BF10 = 5.11). Importantly, however, we found substantial evidence that groups did not differ in reinforcement sensitivity (U = 867.00, p = .641, BF10 = 0.30). Overall, our study demonstrates a preference for exploratory behaviour in adolescents with excess weight, independent of sensitivity to reward. This pattern could potentially underpin an intrinsic desire to explore energy-dense unhealthy foods – an as-yet untapped mechanism that could be targeted in future treatments of obesity in adolescents.Junta de AndaluciaNational Health and Medical Research Council (NHMRC) of Australia GNT200946

Impairments in reinforcement learning do not explain enhanced habit formation in cocaine use disorder.

RATIONALE: Drug addiction has been suggested to develop through drug-induced changes in learning and memory processes. Whilst the initiation of drug use is typically goal-directed and hedonically motivated, over time, drug-taking may develop into a stimulus-driven habit, characterised by persistent use of the drug irrespective of the consequences. Converging lines of evidence suggest that stimulant drugs facilitate the transition of goal-directed into habitual drug-taking, but their contribution to goal-directed learning is less clear. Computational modelling may provide an elegant means for elucidating changes during instrumental learning that may explain enhanced habit formation. OBJECTIVES: We used formal reinforcement learning algorithms to deconstruct the process of appetitive instrumental learning and to explore potential associations between goal-directed and habitual actions in patients with cocaine use disorder (CUD). METHODS: We re-analysed appetitive instrumental learning data in 55 healthy control volunteers and 70 CUD patients by applying a reinforcement learning model within a hierarchical Bayesian framework. We used a regression model to determine the influence of learning parameters and variations in brain structure on subsequent habit formation. RESULTS: Poor instrumental learning performance in CUD patients was largely determined by difficulties with learning from feedback, as reflected by a significantly reduced learning rate. Subsequent formation of habitual response patterns was partly explained by group status and individual variation in reinforcement sensitivity. White matter integrity within goal-directed networks was only associated with performance parameters in controls but not in CUD patients. CONCLUSIONS: Our data indicate that impairments in reinforcement learning are insufficient to account for enhanced habitual responding in CUD.This research was funded by the Medical Research Council (MR/J012084/1) and the NIHR Cambridge Biomedical Research Centre and was conducted at the NIHR Cambridge Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This research was also supported in part by a Medical Research Council (MRC) Clinical Research Infrastructure award (MR/M009041/1). R.N.C. consults for Campden Instruments and receives royalties from Cambridge Enterprise, Routledge, and Cambridge University Press. RNC’s research is supported by the UK Medical Research Council (MC_PC_17213). T.W.R. discloses consultancy with Cambridge Cognition, Lundbeck, Mundipharma and Unilever; he receives royalties for CANTAB from Cambridge Cognition and editorial honoraria from Springer Verlag and Elsevier. T.V.L., G.S. P.S.J., A.A.M. and K.D.E. declare to have no potential conflict of interest

Decision‐making and risk in bipolar disorder:A quantitative study using fuzzy trace theory

Objectives: This study characterizes risk-taking behaviours in a group of people with a self-reported diagnosis of BD using fuzzy trace theory (FTT). FTT hypothesizes that risk-taking is a ‘reasoned’ (but sometimes faulty) action, rather than an impulsive act associated with mood fluctuations. Design: We tested whether measures of FTT (verbatim and gist-based thinking) were predictive of risk-taking intentions in BD, after controlling for mood and impulsivity. We hypothesized that FTT scales would be significant predictors of risk-taking intentions even after accounting for mood and impulsivity. Methods: Fifty-eight participants with BD (age range 21–78, 68% female) completed a series of online questionnaires assessing risk intentions, mood, impulsivity, and FTT. Results: Fuzzy trace theory scales significantly predicted risk-taking intentions (medium effect sizes), after controlling for mood and impulsivity consistent with FTT (part range.26 to.49). Participants with BD did not show any statistically significant tendency towards verbatim-based thinking. Conclusions: Fuzzy trace theory gist and verbatim representations were both independent predictors of risk-taking intentions, even after controlling for mood and impulsivity. The results offer an innovative conceptualization of the mechanisms behind risk-taking in BD. Practitioner points: Risk-taking behaviour in bipolar disorder is not just a consequence of impulsivity. Measures of fuzzy trace theory help to understand risk-taking in bipolar disorder. FTT measures predict risk-taking intentions, after controlling for mood and impulsivity

Enhanced response switching after negative feedback and novelty seeking in adolescence are associated with reduced representation of choice probability in medial frontal pole

Precisely charting the maturation of core neurocognitive functions such as reinforcement learning (RL) and flexible adaptation to changing action-outcome contingencies is key for developmental neuroscience. It can also help us understand how disruptions during development might contribute to the onset of psychopathology. However, research in this area is both sparse and conflicted, especially regarding potentially asymmetric development of learning for different motives (obtain wins vs avoid losses) and learning from valenced feedback (positive vs negative). In the current study, we investigated the development of RL from adolescence to adulthood, using a probabilistic reversal learning task modified to experimentally separate motivational context and feedback valence, in a sample of 95 healthy participants between 12 and 45. We show that adolescence is characterized by enhanced novelty seeking and response shifting after negative feedback, which leads to poorer returns when reward contingencies are stable. Computationally, this is accounted for by reduced impact of positive feedback on behavior. We also show, using fMRI, that activity of the medial frontopolar cortex reflecting choice probability is attenuated in adolescence. We argue that this can be interpreted as reflecting diminished confidence in upcoming choices. Interestingly, we find no age-related differences between learning in win and loss contexts

Impairments in reinforcement learning do not explain enhanced habit formation in cocaine use disorder

Rationale Drug addiction has been suggested to develop through drug-induced changes in learning and memory processes. Whilst the initiation of drug use is typically goal-directed and hedonically motivated, over time, drug-taking may develop into a stimulus-driven habit, characterised by persistent use of the drug irrespective of the consequences. Converging lines of evidence suggest that stimulant drugs facilitate the transition of goal-directed into habitual drug-taking, but their contribution to goal-directed learning is less clear. Computational modelling may provide an elegant means for elucidating changes during instrumental learning that may explain enhanced habit formation. Objectives We used formal reinforcement learning algorithms to deconstruct the process of appetitive instrumental learning and to explore potential associations between goal-directed and habitual actions in patients with cocaine use disorder (CUD). Methods We re-analysed appetitive instrumental learning data in 55 healthy control volunteers and 70 CUD patients by applying a reinforcement learning model within a hierarchical Bayesian framework. We used a regression model to determine the influence of learning parameters and variations in brain structure on subsequent habit formation. Results Poor instrumental learning performance in CUD patients was largely determined by difficulties with learning from feedback, as reflected by a significantly reduced learning rate. Subsequent formation of habitual response patterns was partly explained by group status and individual variation in reinforcement sensitivity. White matter integrity within goal-directed networks was only associated with performance parameters in controls but not in CUD patients. Conclusions Our data indicate that impairments in reinforcement learning are insufficient to account for enhanced habitual responding in CUD

Pathological gambling in adolescence: a narrative review

Pathological gambling is an emerging and increasing phenomenon in Western counties. This work is aimed at reviewing the existing literature on this topic, paying special attention to its development, course and outcome in adolescence. We will explore epidemiological data, the instruments for the diagnostic and clinical assessment, the course and the outcome of the disorder, the comorbidity with other psychiatric syndromes and disorders. The main risk factors will be described at individual, social and community level. We provide an overview of the available pharmacological and psychological treatments and we report a clinical vignette in order to describe the psychological and psychopathological features of pathological gambling in adolescence. 

Pathological gambling in adolescence: A narrative review

Pathological gambling is an emerging and increasing phenomenon in Western counties. This work is aimed at reviewing the existing literature on this topic, paying special attention to its development, course and outcome in adolescence. We will explore epidemiological data, the instruments for the diagnostic and clinical assessment, the course and the outcome of the disorder, the comorbidity with other psychiatric syndromes and disorders. The main risk factors will be described at individual, social and community level. We provide an overview of the available pharmacological and psychological treatments and we report a clinical vignette in order to describe the psychological and psychopathological features of pathological gambling in adolescence

Neural perspectives on cognitive control development during childhood and adolescence

Pathways through Adolescenc

Adolescent-specific patterns of behavior and neural activity during social reinforcement learning

Humans are sophisticated social beings. Social cues from others are exceptionally salient, particularly during adolescence. Understanding how adolescents interpret and learn from variable social signals can provide insight into the observed shift in social sensitivity during this period. The current study tested 120 participants between the ages of 8 and 25 years on a social reinforcement learning task where the probability of receiving positive social feedback was parametrically manipulated. Seventy-eight of these participants completed the task during fMRI scanning. Modeling trial-by-trial learning, children and adults showed higher positive learning rates than adolescents, suggesting that adolescents demonstrated less differentiation in their reaction times for peers who provided more positive feedback. Forming expectations about receiving positive social reinforcement correlated with neural activity within the medial prefrontal cortex and ventral striatum across age. Adolescents, unlike children and adults, showed greater insular activity during positive prediction error learning and increased activity in the supplementary motor cortex and the putamen when receiving positive social feedback regardless of the expected outcome, suggesting that peer approval may motivate adolescents towards action. While different amounts of positive social reinforcement enhanced learning in children and adults, all positive social reinforcement equally motivated adolescents. Together, these findings indicate that sensitivity to peer approval during adolescence goes beyond simple reinforcement theory accounts and suggests possible explanations for how peers may motivate adolescent behavior.Psycholog