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Impulsivity and predictive control are associated with suboptimal action-selection and action-value learning in regular gamblers.

ABSTRACT: Heightened impulsivity and cognitive biases are risk factors for gambling problems. However, little is known about precisely how these factors increase the risks of gambling-related harm in vulnerable individuals. Here, we modelled the behaviour of eighty-seven community-recruited regular, but not clinically problematic, gamblers during a binary-choice reinforcement-learning game, to characterise the relationships between impulsivity, cognitive biases, and the capacity to make optimal action selections and learn about action-values. Impulsive gamblers showed diminished use of an optimal (Bayesian-derived) probability estimate when selecting between candidate actions, and showed slower learning rates and enhanced non-linear probability weighting while learning action values. Critically, gamblers who believed that it is possible to predict winning outcomes (as 'predictive control') failed to use the game's reinforcement history to guide their action selections. Extensive evidence attests to the ease with which gamblers can erroneously perceive structure in the reinforcement history of games when there is none. Our findings demonstrate that the generic and specific risk factors of impulsivity and cognitive biases can interfere with the capacity of some gamblers to utilise structure when it is available in the reinforcement history of games, potentially increasing their risks of sustaining gambling-related harms.

SUBMITTER: Lim MS

PROVIDER: S-EPMC4890653 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Impulsivity and predictive control are associated with suboptimal action-selection and action-value learning in regular gamblers.

Lim Matthew S M MS Jocham Gerhard G Hunt Laurence T LT Behrens Timothy E J TE Rogers Robert D RD

International gambling studies 20151115 3

Heightened impulsivity and cognitive biases are risk factors for gambling problems. However, little is known about precisely how these factors increase the risks of gambling-related harm in vulnerable individuals. Here, we modelled the behaviour of eighty-seven community-recruited regular, but not clinically problematic, gamblers during a binary-choice reinforcement-learning game, to characterise the relationships between impulsivity, cognitive biases, and the capacity to make optimal action sel ...[more]

PMID: 27274706

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Project description:BACKGROUND:Ongoing pain after surgery is a major problem and influences recovery and the quality of life of the patient. Associations between anxiety and their impact on postoperative pain after herniated disc surgery have been reported, but the results are inconsistent. The aim of the present longitudinal study was to evaluate the predictive value of preoperative anxiety for postoperative ongoing pain and prolonged analgesic intake after herniated disc surgery. METHODS:106 patients with lumbar disc herniation were evaluated in the study. Anxiety was measured with the Generalized Anxiety Disorder 7-Item Scale (GAD-7) before surgery. Pain intensity was assessed on a numeric rating scale (NRS) at baseline, 6-weeks and 6-months after surgery. Regression analysis was performed to identify independent predictors of pain and regular utilization of analgesics up to 6 months after surgery while controlling for confounding variables. RESULTS:42.5% of the patients were rated as anxiety cases (sum scores GAD-7?>?5), mean scores of anxiety showed mild to moderate symptom severity, and 43% suffered from chronic pain before surgery. Six months after surgery, 55.6% of the patients indicated pain levels of 4/10 (NRS) or higher and about 40% still took pain medication on a regular basis, regardless of their preoperative classification as anxiety-case (37.7% and 41.5%). The preoperative pain level was statistically significant for ongoing postsurgical pain in all four analyses (p?<?0.001). With binary logistic regression analyses, preoperative pain intensity, but neither demographic factors nor preoperative anxiety, was identified as predictor for postoperative pain and need for analgesic medication up to 6 months after lumbar disc surgery. CONCLUSION:We found no evidence for the presence of anxiety before disc surgery being a prognostic factor for ongoing pain and regular postoperative intake of analgesics. Only preoperative pain intensity was predictive for increased pain and continued need for analgesic medication up to 6 months after lumbar disc surgery. TRIAL REGISTRATION:Clinicaltrials.gov NCT01488617 . Registered 6 December 2011.

Project description:From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints with antagonistic muscle pairs is finding methods robust to address this ill-posed nonlinear problem. To address this computational problem, we implemented a twofold optimization and learning framework to be specialized in addressing the redundancies in the muscle control . In the first part, we used model predictive control to obtain energy efficient skeletal trajectories to mimick human movements. The second part is to use deep reinforcement learning to obtain a sequence of stimulus to be given to muscles in order to obtain the skeletal trajectories with muscle control. We observed that the desired stimulus to muscles is only efficiently constructed by integrating the state and control input in a closed-loop setting as it resembles the proprioceptive integration in the spinal cord circuits. In this work, we showed how a variety of different reference trajectories can be obtained with optimal control and how these reference trajectories are mapped to the musculoskeletal control with deep reinforcement learning. Starting from the characteristics of human arm movement to obstacle avoidance experiment, our simulation results confirm the capabilities of our optimization and learning framework for a variety of dynamic movement trajectories. In summary, the proposed framework is offering a pipeline to complement the lack of experiments to record human motion-capture data as well as study the activation range of muscles to replicate the specific trajectory of interest. Using the trajectories from optimal control as a reference signal for reinforcement learning implementation has allowed us to acquire optimum and human-like behaviour of the musculoskeletal system which provides a framework to study human movement in-silico experiments. The present framework can also allow studying upper-arm rehabilitation with assistive robots given that one can use healthy subject movement recordings as reference to work on the control architecture of assistive robotics in order to compensate behavioural deficiencies. Hence, the framework opens to possibility of replicating or complementing labour-intensive, time-consuming and costly experiments with human subjects in the field of movement studies and digital twin of rehabilitation.

Dataset Information

Impulsivity and predictive control are associated with suboptimal action-selection and action-value learning in regular gamblers.

Publications

Impulsivity and predictive control are associated with suboptimal action-selection and action-value learning in regular gamblers.

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