Zhang, Lili ORCID: 0000-0002-2203-2949, Vashisht, Himanshu ORCID: 0000-0002-2726-946X, Nethra, Alekhya, Slattery, Brian ORCID: 0000-0001-8342-5781 and Ward, Tomás E. ORCID: 0000-0002-6173-6607 (2022) Differences in learning and persistency characterizing behavior in chronic pain for the Iowa gambling task: web-based laboratory-in-the-field study. Journal of Medical Internet Research (JMIR), 24 (4). ISSN 1438-8871
Abstract
Background: Chronic pain is a significant worldwide health problem. It has been reported that people with chronic pain
experience decision-making impairments, but these findings have been based on conventional laboratory 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 affects decision-making captured via laboratory-in-the-field
experiments. Although such settings can introduce more experimental uncertainty, 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 individuals with chronic pain and healthy controls in a
laboratory-in-the-field environment by taking advantage of internet technologies and social media.
Methods: A cross-sectional design with independent groups was used. A convenience sample of 45 participants was recruited
through social media: 20 (44%) participants who self-reported living with chronic pain, and 25 (56%) people with no pain or
who were living with pain for <6 months acting as controls. All participants completed a self-report questionnaire assessing their
pain experiences and a neuropsychological task measuring their decision-making (ie, the Iowa Gambling Task) 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 2 groups, although qualitative
differences could be observed in the learning curves. However, computational modeling revealed that individuals with chronic
pain were quicker to update their behavior than healthy controls, which reflected their increased learning rate (95%
highest–posterior-density interval [HDI] 0.66-0.99) when fitted to the Values-Plus-Perseverance model. This result was further
validated and extended on the Outcome-Representation Learning model as higher differences (95% HDI 0.16-0.47) between the
reward and punishment learning rates were observed when fitted to this model, indicating that individuals with chronic pain were
more sensitive to rewards. It was also found that they were less persistent in their choices during the Iowa Gambling Task compared
with controls, a fact reflected by their decreased outcome perseverance (95% HDI −4.38 to −0.21) when fitted using the
Outcome-Representation Learning 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 laboratory-in-the-field experiment. In this case study, it was demonstrated that, compared with standard statistical
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Uncontrolled Keywords: | chronic pain; decision-making; computational modeling; Iowa Gambling Task; lab-in-the-field experiment |
Subjects: | UNSPECIFIED |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing DCU Faculties and Schools > Faculty of Science and Health > School of Psychology Research Institutes and Centres > INSIGHT Centre for Data Analytics |
Publisher: | JMIR Publications |
Official URL: | https://doi.org/10.2196/26307 |
Copyright Information: | © 2022 The Authors. |
Funders: | Science Foundation Ireland under grant SFI/12/RC/2289_P2, cofunded by the European Regional Development Fund, Allied Irish Banks |
ID Code: | 28124 |
Deposited On: | 07 Mar 2023 13:44 by Thomas Murtagh . Last Modified 07 Mar 2023 13:44 |
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