BACKGROUND
Chronic pain is a significant world-wide health problem. It has been reported that people with chronic pain experience decision-making impairments, but these findings have been based on conventional lab experiments to date. In such experiments researchers have extensive control of conditions and can more precisely eliminate potential confounds. In contrast, there is much less known regarding how chronic pain impacts decision-making captured via lab-in-the-field experiments. Although such settings can introduce more experimental uncertainty, it is believed that collecting data in more ecologically valid contexts can better characterize the real-world impact of chronic pain.
OBJECTIVE
We aim to quantify decision-making differences between chronic pain individuals and healthy controls in a lab-in-the-field environment through taking advantage of internet technologies and social media.
METHODS
A cross-sectional design with independent groups was employed. A convenience sample of 45 participants were recruited through social media - 20 participants who self-reported living with chronic pain, and 25 people with no pain or who were living with pain for less than 6 months acting as controls. All participants completed a self-report questionnaire assessing their pain experiences and a neuropsychological task measuring their decision-making, i.e. the Iowa Gambling Task (IGT) in their web browser at a time and location of their choice without supervision.
RESULTS
Standard behavioral analysis revealed no differences in learning strategies between the two groups although qualitative differences could be observed in learning curves. However, computational modelling revealed that individuals with chronic pain were quicker to update their behavior relative to healthy controls, which reflected their increased learning rate (95% HDI from 0.66 to 0.99) when fitted with the VPP model. This result was further validated and extended on the ORL model because higher differences (95% HDI from 0.16 to 0.47) between the reward and punishment learning rates were observed when fitted on this model, indicating that chronic pain individuals were more sensitive to rewards. It was also found that they were less persistent in their choices during the IGT compared to controls, a fact reflected by their decreased outcome perseverance (95% HDI from -4.38 to -0.21) when fitted using the ORL model. Moreover, correlation analysis revealed that the estimated parameters had predictive value for the self-reported pain experiences, suggesting that the altered cognitive parameters could be potential candidates for inclusion in chronic pain assessments.
CONCLUSIONS
We found that individuals with chronic pain were more driven by rewards and less consistent when making decisions in our lab-in-the-field experiment. In this case study, it was demonstrated that compared to standard statistical summaries of behavioral performance, computational approaches offered superior ability to resolve, understand and explain the differences in decision- making behavior in the context of chronic pain outside the lab.
Assessing the influence of expert video aid on assembly learning curves
