Hierarchical models of pain: Inference, information-seeking, and adaptive control.

Seymour, Ben; Mancini, Flavia

doi:10.1016/j.neuroimage.2020.117212

Cited by 35 publications

(49 citation statements)

References 137 publications

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“…Our study shows that the probabilistic inference of high pain frequency is encoded in the bilateral sensorimotor cortex, secondary somatosensory cortex, and right caudate (figure 5). The neural correlates of pain predictions arising from predictive Bayesian inference seem to contrast to a certain extent with those arising from value-based learning, which is typically characterised by non-probabilistic model-free learning and involves insula, anterior cingulate and ventromedial prefrontal cortices (Seymour and Mancini, 2020). An exception to this is the observation that the caudate nucleus correlates well with the posterior probability of high pain (i.e.…”

Section: Discussionmentioning

confidence: 99%

Learning the statistics of pain: computational and neural mechanisms

Mancini

Zhang

Seymour

2021

Preprint

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Pain invariably changes over time, and these temporal fluctuations are riddled with uncertainty about body safety. In theory, statistical regularities of pain through time contain useful information that can be learned, allowing the brain to generate expectations and inform behaviour. To investigate this, we exposed healthy participants to probabilistic sequences of low and high-intensity electrical stimuli to the left hand, containing sudden changes in stimulus frequencies. We demonstrate that humans can learn to extract these regularities, and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian models with dynamic update of beliefs. We studied brain activity using functional MRI whilst subjects performed the task, which allowed us to dissect the underlying neural correlates of these statistical inferences from their uncertainty and update. We found that the inferred frequency (posterior probability) of high intensity pain correlated with activity in bilateral sensorimotor cortex, secondary somatosensory cortex and right caudate. The uncertainty of statistical inferences of pain was encoded in the right superior parietal cortex. An intrinsic part of this hierarchical Bayesian model is the way that unexpected changes in frequency lead to shift beliefs and update the internal model. This is reflected by the KL divergence between consecutive posterior distributions and associated with brain responses in the premotor cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. In conclusion, this study extends what is conventionally considered a sensory pain pathway dedicated to process pain intensity, to include the generation of Bayesian internal models of temporal statistics of pain intensity levels in sensorimotor regions, which are updated dynamically through the engagement of premotor, prefrontal and parietal regions.

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Section: Discussionmentioning

confidence: 99%

Learning the statistics of pain: computational and neural mechanisms

Mancini

Zhang

Seymour

2021

Preprint

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“…Computational frameworks can indeed provide insights into the mechanisms of placebo. Some researchers have argued that placebo effects and pain modulation can be explained through predictive coding [19][20][21][22][23], which draws on Bayesian integration [24]. This framework predicts that the extent to which perception is biased toward prior expectations depends on the precision of the expectationthe more certain the expectation, the stronger the influence on perception.…”

Section: Trends Trends In In Cognitive Cognitive Sciences Sciencesmentioning

confidence: 99%

A social affective neuroscience lens on placebo analgesia

Atlas

2021

Trends in Cognitive Sciences

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Pain is a fundamental experience that promotes survival. In humans, pain stands at the intersection of multiple health crises: chronic pain, the opioid epidemic, and health disparities. The study of placebo analgesia highlights how social, cognitive, and affective processes can directly shape pain, and identifies potential paths for mitigating these crises. This review examines recent progress in the study of placebo analgesia through affective science. It focuses on how placebo effects are shaped by expectations, affect, and the social context surrounding treatment, and discusses neurobiological mechanisms of placebo, highlighting unanswered questions and implications for health. Collaborations between clinicians and social and affective scientists can address outstanding questions and leverage placebo to reduce pain and improve human health.Pain: a biopsychosocial phenomenon that requires integrative research Pain (see Glossary) lies at the core of multiple health crises. It is central to the opioid epidemic [1,2], there are substantial health disparities in pain [3][4][5], over 11% of American adults report experiencing daily pain [6], and chronic pain is highly comorbid with other central nervous system conditions, including mental health illnesses [7] and substance use disorders [8]. Healthcare practitioners have reduced opioid prescriptions in response to the opioid epidemic [9], but we lack effective alternatives, including nonaddictive and nonpharmacological therapies.Hope can be found in the progress the field has made in isolating the mechanisms that contribute to pain through animal models because the systems that process pain [10] and nociception are highly conserved across species. Nevertheless, pain in humans is ultimately a subjective, conscious experience that involves the integration of sensory processing, emotion, context, and decision making. Understanding human pain requires an integrative approach that draws on psychology, neuroscience, and medicine. In this review I focus on placebo analgesia, a phenomenon that lies at the intersection of these diverse fields. In the following sections I review the critical influences of expectations, affect, and the social context surrounding treatment (Figure 1, Key figure). I then discuss the brain mechanisms underlying placebo effects and the extent to which placebo analgesia provides insights into domains other than pain. Throughout, I emphasize that research on the placebo effect has important implications for the urgent crises surrounding opioid use disorder and health disparities in pain. HighlightsThe study of placebo analgesia provides insights into how pain and clinical outcomes are shaped by social, cognitive, and affective processes.

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“…From a Bayesian perspective, our brain is conceptualized as a predictor machine that generates predictions, known as priors , about the expected sensory inputs. The integration between the prior and the sensory input results in a posterior (the percept), which can be more or less influenced by the prior and by the sensory data, depending on their level of precision (i.e., data encoded as probabilistic representatations; Friston, 2008 , 2010 ; Büchel et al, 2014 ; Seymour and Mancini, 2020 ). Within this framework, a prior with high precision is considered reliable and, therefore, will exert greater influence on the interpretation of the incoming sensory input, resulting in a posterior (i.e., percept) which is biased toward the prior ( Figure 1B ).…”

Section: The Violex Model and The Bayesian Brainmentioning

confidence: 99%

“…Differently, a prior with low precision will be considered unreliable, and therefore, will be given less consideration when interpreting the incoming sensory input, resulting in a posterior (i.e., percept) that is a better proxy of the sensory data ( Figure 1C ). Consider a sensory input which does not match the prior; if the prior has higher precision this is likely to result in a smaller prediction error (PE) (i.e., since the percept is biased toward the prior, there will be less discrepancy between the prior and the percept), compared to a prior with less precision (i.e., since the percept is a better proxy of the sensory information there will be more discrepancy between the prior and the percept), resulting in greater prior updating in the latter case ( Friston, 2008 , 2010 ; Büchel et al, 2014 ; Seymour and Mancini, 2020 ).…”

Section: The Violex Model and The Bayesian Brainmentioning

confidence: 99%

Unraveling Negative Expectations and Nocebo-Related Effects in Musculoskeletal Pain

et al. 2022

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This Perspective adapts the ViolEx Model, a framework validated in several clinical conditions, to better understand the role of expectations in the recovery and/or maintenance of musculoskeletal (MSK) pain. Here, particular attention is given to the condition in which dysfunctional expectations are maintained despite no longer being supported by confirmatory evidence (i.e., belief—lifting the arm leads to permanent tendon damage; evidence—after the patient lifts the arm no tendon damage occurs). While the ViolEx Model suggests that cognitive immunization strategies are responsible for the maintenance of dysfunctional expectations, we suggest that such phenomenon can also be understood from a Bayesian Brain perspective, according to which the level of precision of the priors (i.e., expectations) is the determinant factor accounting for the extent of priors’ updating (i.e., we merge the two frameworks, suggesting that highly precise prior can lead to cognitive immunization responses). Importantly, this Perspective translates the theory behind these two frameworks into clinical suggestions. Precisely, it is argued that different strategies should be implemented when treating MSK pain patients, depending on the nature of their expectations (i.e., positive or negative and the level of their precision).

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Hierarchical models of pain: Inference, information-seeking, and adaptive control.

Cited by 35 publications

References 137 publications

Learning the statistics of pain: computational and neural mechanisms

Learning the statistics of pain: computational and neural mechanisms

A social affective neuroscience lens on placebo analgesia

Unraveling Negative Expectations and Nocebo-Related Effects in Musculoskeletal Pain

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