Decision-making inflexibility in a reversal learning task is associated with severity of problem gambling symptoms but not with a diagnosis of substance use disorder

The effect of reward learning on inhibitory control in internet gaming disorder: Evidence from behavioral and ERP

Reward dysregulation and deficits in inhibitory control significantly contribute to the development of internet gaming disorder (IGD). While prior research demonstrates that reward history influences individuals' inhibitory control, it remains unclear whether this effect extends to individuals with IGD. The primary aim of this study was to investigate whether individuals with IGD exhibit impairments in reward learning and whether prior reward learning influences their inhibitory control, using both behavioral and event-related potential (ERP) measures. This study first employed a probability selection task to examine potential impairments in reward learning among individuals with IGD. Next, a stop-signal task incorporating reward- and punishment-associated stimuli was used to further investigate the behavioral and electroencephalographic effects of prior reward learning on subsequent inhibitory control. Results revealed that during the reward-learning phase, the IGD group exhibited significantly longer response times than the control group in both the learning and transfer phases. Additionally, the feedback-related negativity amplitude in the IGD group was significantly lower than that in the control group. Conversely, the P3 wave amplitude induced by positive and negative feedback in the IGD group were significantly higher than in the control group. In the inhibitory control phase following reward learning, the Nogo-P3 wave amplitude in response to reward cues was significantly greater in the IGD group than in the control group. Moreover, within the IGD group, the Nogo-P3 wave amplitude evoked by reward cues was significantly larger than the amplitude evoked by loss cues. These findings suggest that reward learning is impaired in individuals with IGD and that stimuli with a prior reward history may compromise inhibitory control, potentially serving as a critical factor in addiction development in this population.

Contingency-based flexibility mechanisms through a reinforcement learning model in adults with attention-deficit/hyperactivity disorder and obsessive-compulsive disorder

BACKGROUND AND AIMS

Impaired cognitive flexibility is associated with the characteristic symptomatology of ADHD and OCD. However, the mechanisms underlying learning and flexibility under uncertainty in adults with OCD or ADHD remain unclear. This study aimed to identify the mechanisms underlying contingency-based flexibility in a sample of adults with ADHD or OCD, using probabilistic learning reversal task, functional near-infrared spectroscopy, and computational modelling.

METHODS

148 Spanish-speaking adults (43 OCD, 53 ADHD and 52 healthy controls) completed a probabilistic learning reversal task. Previously, we obtained a resting-state functional connectivity (rsFC) record between several frontoparietal network regions using functional near-infrared spectroscopy. Contingency-based flexibility was explored by reinforcement learning model in combination with a Bayesian Generalized Logistic Model (GLM). The reinforcement learning parameters included reward and punishment learning rates (feedback sensitivity), and inverse temperature (decision consistency). Bayesian GLM parameters were defined to measure final accuracy, learning and speed of learning.

RESULTS

We found that the groups showed optimal performance in the discrimination block and a higher performance of healthy controls compared to patients in the reversal block. Model parameters predicted task performance differently by phase and group. In the discrimination block, while the performance of healthy controls was predicted by a combination of parameters such as high inverse temperature and punishment learning rate together with low values of reward learning rate, in the case of the clinical groups it was only by high inverse temperature. In the reversal block, the performance of OCD was predicted by high punishment learning rate and that of ADHD by low reward learning rate; in contrast, the performance of healthy controls was also predicted by a combination of parameters with high punishment learning rate and inverse temperature as predictors. We found the rsFC between the left and right posterior parietal cortex appears to credibly predict performance in the discrimination block in healthy controls.

CONCLUSIONS

These results suggest that OCD and ADHD in adults could be associated with poor behavioral adaptation when reinforcement-punishment contingencies changed. The difficulties observed in ADHD and OCD likely stem from different underlying mechanisms that affect both learning and switching processes. Findings highlighted how OCD appears to show greater sensitivity to punishment when there is uncertainty about the behavior-outcome association. Instead, the ADHD group can be guided by sensitivity to reinforcement. Interhemispheric rsFC posterior parietal cortex could be important for optimal learning of the task.

Assessing cognitive flexibility in mice using a custom-built touchscreen chamber

Automated touchscreen systems have become increasingly prevalent in rodent model screening. This technology has significantly enhanced cognitive and behavioral assessments in mice and has bridged the translational gap between basic research using rodent models and human clinical research. Our study introduces a custom-built touchscreen operant conditioning chamber powered by a Raspberry Pi and a commercially available computer tablet, which effectively addresses the significant cost barriers traditionally associated with this technology. In order to test our prototype, we decided to train C57BL/6 mice on a visual discrimination serial-reversal task, and both C57BL/6 and AppNL-G-Fstrain - an Alzheimer's Disease (AD) mouse model - on a new location discrimination serial-reversal task. The results demonstrated a clear progression toward asymptotic performance, particularly in the location discrimination task, which also revealed potential genotype-specific deficits, with AppNL-G-F mice displaying an increase in the average number of errors in the first reversal as well as in perseverative errors, compared to wild-type mice. These results validate the practical utility of our touchscreen apparatus and underline its potential to provide insights into the behavioral and cognitive markers of neurobiological disorders.

A bio-inspired reinforcement learning model that accounts for fast adaptation after punishment

Humans and animals can quickly learn a new strategy when a previously-rewarding strategy is punished. It is difficult to model this with reinforcement learning methods, because they tend to perseverate on previously-learned strategies - a hallmark of impaired response to punishment. Past work has addressed this by augmenting conventional reinforcement learning equations with ad hoc parameters or parallel learning systems. This produces reinforcement learning models that account for reversal learning, but are more abstract, complex, and somewhat detached from neural substrates. Here we use a different approach: we generalize a recently-discovered neuron-level learning rule, on the assumption that it captures a basic principle of learning that may occur at the whole-brain-level. Surprisingly, this gives a new reinforcement learning rule that accounts for adaptation and lose-shift behavior, and uses only the same parameters as conventional reinforcement learning equations. In the new rule, the normal reward prediction errors that drive reinforcement learning are scaled by the likelihood the agent assigns to the action that triggered a reward or punishment. The new rule demonstrates quick adaptation in card sorting and variable Iowa gambling tasks, and also exhibits a human-like paradox-of-choice effect. It will be useful for experimental researchers modeling learning and behavior.

Associations of Reversal Learning Performance With Personality Disorder Profile and Drug Abuse History in a Sample of Prison Inmates

Prison inmate samples present a high prevalence of impulsivity- and compulsivity-related behavioral problems. The Probabilistic Reversal Learning Task (PRLT) is a useful tool to assess decision-making, and we explore its associations with inmates' personality disorder (antisocial personality disorder, APD; obsessive-compulsive personality disorder, OCPD; or both) and history of drug abuse. Mixed-effects methods were used to model acquisition and reacquisition curves across PRLT, in a sample of 275 prison inmates diagnosed with OCPD, APD, or both. Two aspects were assessed: general discrimination learning and decision-making inflexibility. Participants with a mixed personality disorder profile showed a clear pattern of decisional inflexibility. A history of drug abuse was associated with a general poorer performance but not with decision-making inflexibility. Inability to adapt to changing contingencies, and thus to adverse consequences of previously rewarded choices, was not linked to compulsivity, as hypothesized to be present in OCPD and substance use disorders, but to the mixed APD/OCPD profile.

Cognitive influences on biosecurity measure compliance during a global pandemic

Introduction

During the first years of the pandemic, COVID-19 forced governments worldwide to take drastic measures to reduce the spread of the virus. Some of these measures included mandatory confinements, constant use of masks, and social distancing. Despite these measures being mandatory in many countries and the abundance of evidence on their effectiveness at slowing the spread of the virus, many people failed to comply with them.

Methods

This research explored the role of cognitive factors in predicting compliance with COVID-19 safety measures across two separate studies. Building on earlier work demonstrating the relevance of cognitive processes in health behaviour, this study aimed to identify key predictors of adherence to safety guidelines during the pandemic. Utilising hierarchical regression models, we investigated the influence of age, sex, cognitive control, cognitive flexibility (Study 1), working memory, psychological health, and beliefs about COVID-19 (Study 2) on compliance to biosafety measures.

Results

Demographic variables and cognitive control were significant predictors of compliance in both studies. However, cognitive flexibility and working memory did not improve the models' predictive capacities. In Study 2, integrating measures of psychological health and beliefs regarding COVID-19 severity significantly improved the model. Further, interaction effects between age and other variables also enhanced the predictive value.

Discussion

The findings emphasise the significant role cognitive control, age, psychological health, and perceptions about COVID-19 play in shaping compliance behaviour, highlighting avenues for targeted interventions to improve public health outcomes during a pandemic.

Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders

BACKGROUND

Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior.

AIMS

It is poorly understood whether impairments in flexibility differ between individuals with cocaine use and gambling disorders, and how this is instantiated by the brain. We applied computational modelling methods to gain a deeper mechanistic explanation of the latent processes underlying cognitive flexibility across two disorders of compulsivity.

METHOD

We present a re-analysis of probabilistic reversal data from individuals with either gambling disorder (n = 18) or cocaine use disorder (n = 20) and control participants (n = 18), using a hierarchical Bayesian approach. Furthermore, we relate behavioural findings to their underlying neural substrates through an analysis of task-based functional magnetic resonanceimaging (fMRI) data.

RESULTS

We observed lower 'stimulus stickiness' in gambling disorder, and report differences in tracking expected values in individuals with gambling disorder compared to controls, with greater activity during reward expected value tracking in the cingulate gyrus and amygdala. In cocaine use disorder, we observed lower responses to positive punishment prediction errors and greater activity following negative punishment prediction errors in the superior frontal gyrus compared to controls.

CONCLUSIONS

Using a computational approach, we show that individuals with gambling disorder and cocaine use disorder differed in their perseverative tendencies and in how they tracked value neurally, which has implications for psychiatric classification.

Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder

BACKGROUND

Alcohol use disorder is characterized by perseverative alcohol use despite negative consequences. This hallmark feature of addiction potentially relates to impairments in behavioral flexibility, which can be measured by probabilistic reversal learning (PRL) paradigms. We here aimed to examine the cognitive mechanisms underlying impaired PRL task performance in patients with alcohol use disorder (AUDP) using computational models of reinforcement learning.

METHODS

Twenty-eight early abstinent AUDP and 27 healthy controls (HC) performed an extensive PRL paradigm. We compared conventional behavioral variables of choices (perseveration; correct responses) between groups. Moreover, we fitted Bayesian computational models to the task data to compare differences in latent cognitive variables including reward and punishment learning and choice consistency between groups.

RESULTS

AUDP and HC did not significantly differ with regard to direct perseveration rates after reversals. However, AUDP made overall less correct responses and specifically showed decreased win-stay behavior compared to HC. Interestingly, AUDP showed premature switching after no or little negative feedback but elevated proneness to stay when accumulation of negative feedback would make switching a more optimal option. Computational modeling revealed that AUDP compared to HC showed enhanced learning from punishment, a tendency to learn less from positive feedback and lower choice consistency.

CONCLUSION

Our data do not support the assumption that AUDP are characterized by increased perseveration behavior. Instead our findings provide evidence that enhanced negative reinforcement and decreased non-drug-related reward learning as well as diminished choice consistency underlie dysfunctional choice behavior in AUDP.