Instructions and experiential learning have similar impacts on pain and pain-related brain responses but produce dissociations in value-based reversal learning
- National Center for Complementary and Integrative Health, United States
- University of Pittsburgh, United States
- National Institute of Mental Health, United States
Abstract
Recent data suggest that interactions between systems involved in higher order knowledge and associative learning drive responses during value-based learning. However, it is unknown how these systems impact subjective responses, such as pain. We tested how instructions and reversal learning influence pain and pain-evoked brain activation. Healthy volunteers (n = 40) were either instructed about contingencies between cues and aversive outcomes or learned through experience in a paradigm where contingencies reversed three times. We measured predictive cue effects on pain and heat-evoked brain responses using functional magnetic resonance imaging. Predictive cues dynamically modulated pain perception as contingencies changed, regardless of whether participants received contingency instructions. Heat-evoked responses in the insula, anterior cingulate, and other regions updated as contingencies changed, and responses in the prefrontal cortex mediated dynamic cue effects on pain, whereas responses in the brainstem's rostroventral medulla (RVM) were shaped by initial contingencies throughout the task. Quantitative modeling revealed that expected value was shaped purely by instructions in the Instructed Group, whereas expected value updated dynamically in the Uninstructed Group as a function of error-based learning. These differences were accompanied by dissociations in the neural correlates of value-based learning in the rostral anterior cingulate, thalamus, and posterior insula, among other regions. These results show how predictions dynamically impact subjective pain. Moreover, imaging data delineate three types of networks involved in pain generation and value-based learning: those that respond to initial contingencies, those that update dynamically during feedback-driven learning as contingencies change, and those that are sensitive to instruction. Together, these findings provide multiple points of entry for therapies designs to impact pain.
Data availability
Pain, SCR, and signature data are available on OSF; neuroimaging data has been uploaded to neurovault.
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Instructions and experiential learning have similar impacts on pain and pain-related brain responses but produce dissociations in value-based reversal learningNeurovault, https://identifiers.org/neurovault.collection:12874.
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How instructions and learning shape pain and expectancy-based pain modulation: Pain, SCR, and brain signaturesOSF, https://osf.io/adc8e/?view_only=7aa2dd534265459b9ae163c7c372d41c.
Article and author information
Author details
Funding
National Center for Complementary and Integrative Health (ZIAAT-000030)
- Lauren Y Atlas
National Institute of Mental Health (ZIAMH-002782)
- Daniel S Pine
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Joshua Johansen, RIKEN Center for Brain Science, Japan
Ethics
Human subjects: Consent was obtained as described in Materials and methods. Data were collected under NIH protocol 15-AT-0132, identifier NCT02446262 at clinicaltrials.gov
Version history
- Received: August 25, 2021
- Preprint posted: August 27, 2021 (view preprint)
- Accepted: October 25, 2022
- Accepted Manuscript published: November 1, 2022 (version 1)
- Version of Record published: November 22, 2022 (version 2)
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Instructions and experiential learning have similar impacts on pain and pain-related brain responses but produce dissociations in value-based reversal learning
eLife 11:e73353.
https://doi.org/10.7554/eLife.73353
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