A Bayesian perspective on sensory and cognitive integration in pain perception and placebo analgesia

Bayesian surprise intensifies pain in a novel visual-noxious association

Pain perception is not solely determined by noxious stimuli, but also varies due to other factors, such as beliefs about pain and its uncertainty. A widely accepted theory posits that the brain integrates prediction of pain with noxious stimuli, to estimate pain intensity. This theory assumes that the estimated pain value is adjusted to minimize surprise, mathematically defined as errors between predictions and outcomes. However, it is still unclear whether the represented surprise directly influences pain perception or merely serves to update this estimate. In this study, we empirically examined this question using virtual reality. In the task, participants reported felt pain via VAS after their arm was stimulated by noxious heat and thrusted into by a virtual knife actively. To manipulate surprise level, the visual threat suddenly disappeared randomly, and noxious heat was presented in the on- or post-action phases. We observed that a transphysical surprising event, created by sudden disappearance of a visual threat cue combined with delayed noxious heat, amplified pain intensity. Subsequent model-based analysis using Bayesian theory revealed significant modulation of pain by the Bayesian surprise value. These results illustrated a real-time computational process for pain perception during a single task trial, suggesting that the brain anticipates pain using an efference copy of actions, integrates it with multimodal stimuli, and perceives it as a surprise.

Beyond Needling: Integrating a Bayesian Brain Model into Acupuncture Treatment

Acupuncture is a medical tool in which a sterile needle is used to penetrate and stimulate a certain body area (acupoint), inducing a series of sensations such as numbness, dullness, or aching, often referred to as de-qi. But is that all? In this article, we adopt a Bayesian perspective to explore the cognitive and affective aspects of acupuncture beyond needling, specifically, how the body integrates bottom-up sensory signals with top-down predictions of acupuncture perception. We propose that the way in which we discern acupuncture treatment is the result of predictive coding, a probabilistic, inferential process of our brain. Active inference from both prior experience and expectations of acupuncture, when integrated with incoming sensory signals, creates a unique, individual internal generative model of our perception of acupuncture. A Bayesian framework and predictive coding may, therefore, aid in elucidating and quantifying the cognitive components of acupuncture and facilitate understanding of their differential interactions in determining individual expectations of treatment. Thus, a perception-based Bayesian model of acupuncture presented in this article may expand on how we perceive acupuncture treatment, from simply inserting needles into our body to one that encompasses a complex healing process supported by belief and hope of regaining health. By exploring how cognitive factors influence individual responsiveness to acupuncture treatment, this review sheds light on why acupuncture treatment is more effective in some individuals than in others.

The role of pain expectancy and its confidence in placebo hypoalgesia and nocebo hyperalgesia

ABSTRACT

Placebo hypoalgesia and nocebo hyperalgesia, which exemplify the impact of expectations on pain, have recently been conceptualised as Bayesian inferential processes, yet empirical evidence remains limited. Here, we explore whether these phenomena can be unified within the same Bayesian framework by testing the predictive role of expectations and their level of precision (ie, expectation confidence) on pain, with both predictors measured at the metacognitive level. Sixty healthy volunteers underwent a pain test (ie, 8 noxious electrical stimuli) before (Baseline) and after (T0, T1, T2) receiving a sham treatment associated with hypoalgesic (placebo), hyperalgesic (nocebo), or neutral (control) verbal suggestions, depending on group allocation. Trial-by-trial expectations, their precision, and perceived pain were measured. Skin conductance response (SCR) was also recorded as an autonomic response marker. Bayesian linear mixed models analyses revealed that, for both placebo and nocebo, pain was predicted by expectations alone and by their interaction with expectations precision. In addition, the discrepancy between expected and perceived pain was predicted by expectation precision, with greater alignment between expected and perceived pain when precision was higher. This suggests that both placebo and nocebo responses are well described from a Bayesian perspective. A main effect of time for SCR was observed, suggesting habituation to painful stimuli. Our data provide evidence indicating that both placebo hypoalgesia and nocebo hyperalgesia can be unified within the same Bayesian framework in which not only expectations but also their level of precision, both measured at the metacognitive level, are key determinants of the pain inferential process.

How control modulates pain

Pain, an indicator of potential tissue damage, ideally falls under individual control. Although previous work shows a trend towards reduced pain in contexts where pain is controllable, there is a large variability across studies that is probably related to different aspects of control. We therefore outline a taxonomy of different aspects of control relevant to pain, sketch how control over pain can be integrated into a Bayesian pain model, and suggest changes in expectations and their precision as potential mechanisms. We also highlight confounding cognitive factors, particularly predictability, that emphasize the necessity for careful experimental designs. Finally, we describe the neurobiological underpinnings of how control affects pain processing in studies using different types of control, and highlight the roles of the anterior insula, middle frontal gyrus (MFG), and anterior cingulate cortex (ACC).

Spatiotemporal integration of contextual and sensory information within the cortical hierarchy in human pain experience

Pain is not a mere reflection of noxious input. Rather, it is constructed through the dynamic integration of current predictions with incoming sensory input. However, the temporal dynamics of the behavioral and neural processes underpinning this integration remain elusive. In the current study involving 59 human participants, we identified a series of brain mediators that integrated cue-induced expectations with noxious inputs into ongoing pain predictions using a semicircular scale designed to capture rating trajectories. Temporal mediation analysis revealed that during the early-to-mid stages of integration, the frontoparietal and dorsal attention network regions, such as the lateral prefrontal, premotor, and parietal cortex, mediated the cue effects. Conversely, during the mid-to-late stages of integration, the somatomotor network regions mediated the effects of stimulus intensity, suggesting that the integration occurs along the cortical hierarchy from the association to sensorimotor brain systems. Our findings advance the understanding of how the brain integrates contextual and sensory information into pain experience over time.

A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain

Pain perception is influenced not only by sensory input from afferent neurons but also by cognitive factors such as prior expectations. It has been suggested that overly precise priors may be a key contributing factor to chronic pain states such as neuropathic pain. However, it remains an open question how overly precise priors in favor of pain might arise. Here, we first verify that a Bayesian approach can describe how statistical integration of prior expectations and sensory input results in pain phenomena such as placebo hypoalgesia, nocebo hyperalgesia, chronic pain, and spontaneous neuropathic pain. Our results indicate that the value of the prior, which is determined by the internal model parameters, may be a key contributor to these phenomena. Next, we apply a hierarchical Bayesian approach to update the parameters of the internal model based on the difference between the predicted and the perceived pain, to reflect that people integrate prior experiences in their future expectations. In contrast with simpler approaches, this hierarchical model structure is able to show for placebo hypoalgesia and nocebo hyperalgesia how these phenomena can arise from prior experiences in the form of a classical conditioning procedure. We also demonstrate the phenomenon of offset analgesia, in which a disproportionally large pain decrease is obtained following a minor reduction in noxious stimulus intensity. Finally, we turn to simulations of neuropathic pain, where our hierarchical model corroborates that persistent non-neuropathic pain is a risk factor for developing neuropathic pain following denervation, and additionally offers an interesting prediction that complete absence of informative painful experiences could be a similar risk factor. Taken together, these results provide insight to how prior experiences may contribute to pain perception, in both experimental and neuropathic pain, which in turn might be informative for improving strategies of pain prevention and relief.

Identification of group differences in predictive anticipatory biasing of pain during uncertainty: preparing for the worst but hoping for the best

ABSTRACT

Pain anticipation during conditions of uncertainty can unveil intrinsic biases, and understanding these biases can guide pain treatment interventions. This study used machine learning and functional magnetic resonance imaging to predict anticipatory responses in a pain anticipation experiment. One hundred forty-seven participants that included healthy controls (n = 57) and individuals with current and/or past mental health diagnosis (n = 90) received cues indicating upcoming pain stimuli: 2 cues predicted high and low temperatures, while a third cue introduced uncertainty. Accurate differentiation of neural patterns associated with specific anticipatory conditions was observed, involving activation in the anterior short gyrus of the insula and the nucleus accumbens. Three distinct response profiles emerged: subjects with a negative bias towards high pain anticipation, those with a positive bias towards low pain anticipation, and individuals whose predictions during uncertainty were unbiased. These profiles remained stable over one year, were consistent across diagnosed psychopathologies, and correlated with cognitive coping styles and underlying insula anatomy. The findings suggest that individualized and stable pain anticipation occurs in uncertain conditions.

Statistical learning shapes pain perception and prediction independently of external cues

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion

An intriguing perspective about human emotion, the theory of constructed emotion considers emotions as generative models according to the Bayesian brain hypothesis. This theory brings fresh insight to existing findings, but its complexity renders it challenging to test experimentally. We argue that laboratory studies of pain could support the theory because although some may not consider pain to be a genuine emotion, the theory must at minimum be able to explain pain perception and its dysfunction in pathology. We review emerging evidence that bear on this question. We cover behavioral and neural laboratory findings, computational models, placebo hyperalgesia, and chronic pain. We conclude that there is substantial evidence for a predictive processing account of painful experience, paving the way for a better understanding of neuronal and computational mechanisms of other emotions.

Updating beliefs about pain following advice: Trustworthiness of social advice predicts pain expectations and experience

Prior expectations influence pain experience. These expectations, in turn, rely on prior pain experience, but they may also be socially influenced. Yet, most research has focused on self rather than social expectations about pain, and hardly any studies examined their combined effects on pain. Here, we adopted a Bayesian learning perspective to investigate how explicitly communicated social expectations ('advice about pain tolerance') affect own pain expectations, and ultimately pain tolerance, under varying conditions of social epistemic uncertainty (trustworthiness of the advice). N = 72 female participants took part in a coldpressor (cold water) task before (self-learning baseline) and after (socially-influenced learning) receiving advice about their likely pain tolerance from a confederate, the trustworthiness of whom was experimentally manipulated. We used path analysis to test the hypothesis that social advice from a highly trustworthy confederate would influence participants' expectations about pain more than advice from a less trustworthy source, and that the degree of this social influence would in turn predict pain tolerance. We further used a simplified, Bayesian learning, computational approach for explicit belief updating to examine the role of latent parameters of precision optimisation in how participants subsequently changed their future pain expectations (prospective posterior beliefs) based on the combined effect of the confederate's advice on their own pain expectations, and their own task experience. Results confirmed that participants adjusted their pain expectations towards the confederate's advice more in the high- vs. low-trustworthiness condition, and this advice taking predicted their pain tolerance. Furthermore, the confederate's trustworthiness influenced how participants weighted the confederate's advice in relation to their own expectations and task experience in forming prospective posterior beliefs. When participants received advice from a less trustworthy confederate, their own sensory experience was weighted more highly than their socially-influenced prior expectations. Thus, explicit social advice appears to impact pain by influencing one's own pain expectations, but low social trustworthiness leads to these expectations becoming more malleable to novel, sensory learning.

Placebo Effects Are Small on Average in the 7.5% CO2 Inhalational Model of Generalized Anxiety

BACKGROUND

Anxiety disorders are highly prevalent and socio-economically costly. Novel pharmacological treatments for these disorders are needed because many patients do not respond to current agents or experience unwanted side effects. However, a barrier to treatment development is the variable and large placebo response rate seen in trials of novel anxiolytics. Despite this, the mechanisms that drive placebo responses in anxiety disorders have been little investigated, possibly due to low availability of convenient experimental paradigms. We aimed to develop and test a novel protocol for inducing placebo anxiolysis in the 7.5% CO2 inhalational model of generalized anxiety in healthy volunteers.

METHODS

Following a baseline 20-minute CO2 challenge, 32 healthy volunteers were administered a placebo intranasal spray labelled as either the anxiolytic "lorazepam" or "saline." Following this, participants surreptitiously underwent a 20-minute inhalation of normal air. Post-conditioning, a second dose of the placebo was administered, after which participants completed another CO2 challenge.

RESULTS

Participants administered sham "lorazepam" reported significant positive expectations of reduced anxiety (P = .001), but there was no group-level placebo effect on anxiety following CO2 challenge post-conditioning (Ps > .350). Surprisingly, we found many participants exhibited unexpected worsening of anxiety, despite positive expectations.

CONCLUSIONS

Contrary to our hypothesis, our novel paradigm did not induce a placebo response, on average. It is possible that effects of 7.5% CO2 inhalation on prefrontal cortex function or behavior in line with a Bayesian predictive coding framework attenuated the effect of expectations on subsequent placebo response. Future studies are needed to explore these possibilities.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

The role of expectations, control and reward in the development of pain persistence based on a unified model

Chronic, or persistent pain affects more than 10% of adults in the general population. This makes it one of the major physical and mental health care problems. Although pain is an important acute warning signal that allows the organism to take action before tissue damage occurs, it can become persistent and its role as a warning signal thereby inadequate. Although per definition, pain can only be labeled as persistent after 3 months, the trajectory from acute to persistent pain is likely to be determined very early and might even start at the time of injury. The biopsychosocial model has revolutionized our understanding of chronic pain and paved the way for psychological treatments for persistent pain, which routinely outperform other forms of treatment. This suggests that psychological processes could also be important in shaping the very early trajectory from acute to persistent pain and that targeting these processes could prevent the development of persistent pain. In this review, we develop an integrative model and suggest novel interventions during early pain trajectories, based on predictions from this model.

A novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing

Pain perception can be studied as an inferential process in which prior information influences the perception of nociceptive input. To date, there are no suitable psychophysical paradigms to measure this at an individual level. We developed a quantitative sensory testing paradigm allowing for quantification of the influence of prior expectations versus current nociceptive input during perception. Using a Pavlovian-learning task, we investigated the influence of prior expectations on the belief about the varying strength of association between a painful electrical cutaneous stimulus and a visual cue in healthy subjects (N = 70). The belief in cue-pain associations was examined with computational modelling using a Hierarchical Gaussian Filter (HGF). Prior weighting estimates in the HGF model were compared with the established measures of conditioned pain modulation (CPM) and temporal summation of pain (TSP) assessed by cuff algometry. Subsequent HGF-modelling and estimation of the influence of prior beliefs on perception showed that 70% of subjects had a higher reliance on nociceptive input during perception of acute pain stimuli, whereas 30% showed a stronger weighting of prior expectations over sensory evidence. There was no association between prior weighting estimates and CPM or TSP. The data demonstrates relevant individual differences in prior weighting and suggests an importance of top-down cognitive processes on pain perception. Our new psychophysical testing paradigm provides a method to identify individuals with traits suggesting greater reliance on prior expectations in pain perception, which may be a risk factor for developing chronic pain and may be differentially responsive to learning-based interventions.

Agency affects pain inference through prior shift as opposed to likelihood precision modulation in a Bayesian pain model

Agency and expectations play a crucial role in pain perception and treatment. In the Bayesian pain model, somatosensation (likelihood) and expectations (prior) are weighted by their precision and integrated to form a pain percept (posterior). Combining pain treatment with stimulus-related expectations allows the mechanistic assessment of whether agency enters this model as a shift of the prior or a relaxation of the likelihood precision. In two experiments, heat pain was sham treated either externally or by the subject, while a predictive cue was utilized to create high or low treatment expectations. Both experiments revealed additive effects and greater pain relief under self-treatment and high treatment expectations. Formal model comparisons favored a prior shift rather than a modulation of likelihood precision. Electroencephalography revealed a theta-to-alpha effect, temporally associated with expectations, which was correlated with trial-by-trial pain ratings, further supporting a prior shift through which agency exerts its influence in the Bayesian pain model.

Pain, from perception to action: A computational perspective

Pain is driven by sensation and emotion, and in turn, it motivates decisions and actions. To fully appreciate the multidimensional nature of pain, we formulate the study of pain within a closed-loop framework of sensory-motor prediction. In this closed-loop cycle, prediction plays an important role, as the interaction between prediction and actual sensory experience shapes pain perception and subsequently, action. In this Perspective, we describe the roles of two prominent computational theories-Bayesian inference and reinforcement learning-in modeling adaptive pain behaviors. We show that prediction serves as a common theme between these two theories, and that each of these theories can explain unique aspects of the pain perception-action cycle. We discuss how these computational theories and models can improve our mechanistic understandings of pain-centered processes such as anticipation, attention, placebo hypoalgesia, and pain chronification.

A Bayesian model for chronic pain

The perceiving mind constructs our coherent and embodied experience of the world from noisy, ambiguous and multi-modal sensory information. In this paper, we adopt the perspective that the experience of pain may similarly be the result of a probabilistic, inferential process. Prior beliefs about pain, learned from past experiences, are combined with incoming sensory information in a Bayesian manner to give rise to pain perception. Chronic pain emerges when prior beliefs and likelihoods are biased towards inferring pain from a wide range of sensory data that would otherwise be perceived as harmless. We present a computational model of interoceptive inference and pain experience. It is based on a Bayesian graphical network which comprises a hidden layer, representing the inferred pain state; and an observable layer, representing current sensory information. Within the hidden layer, pain states are inferred from a combination of priors p(pain), transition probabilities between hidden states p(paint+1∣paint) and likelihoods of certain observations p(sensation∣pain). Using variational inference and free-energy minimization, the model is able to learn from observations over time. By systematically manipulating parameter settings, we demonstrate that the model is capable of reproducing key features of both healthy- and chronic pain experience. Drawing on mathematical concepts, we finally simulate treatment resistant chronic pain and discuss mathematically informed treatment options.

Direct Verbal Suggestibility as a Predictor of Placebo Hypoalgesia Responsiveness

OBJECTIVE

Reliably identifying good placebo responders has pronounced implications for basic research on, and clinical applications of, the placebo response. Multiple studies point to direct verbal suggestibility as a potentially valuable predictor of individual differences in placebo responsiveness, but previous research has produced conflicting results on this association.

METHODS

In two double-blind studies, we examined whether behavioral direct verbal suggestibility measures involving a correction for compliance would be associated with individual differences in responsiveness to conditioned and unconditioned placebo hypoalgesia using an established placebo analgesia paradigm. In study 1 (n = 57; mean [standard deviation] age = 23.7 [8.1] years; 77% women), we used behavioral hypnotic suggestibility as a predictor of placebo hypoalgesia induced through conditioning and verbal suggestion, whereas in study 2 (n = 78; mean [standard deviation] = 26.1 [7.4] years; 65% women), we measured nonhypnotic suggestibility and placebo hypoalgesia induced through verbal suggestion without conditioning.

RESULTS

In study 1, the placebo hypoalgesia procedure yielded a moderate placebo response (g = 0.63 [95% confidence interval = 0.32 to 0.97]), but the response magnitude did not significantly correlate with hypnotic suggestibility (rs = 0.11 [-0.17 to 0.37]). In study 2, the placebo procedure did not yield a significant placebo response across the full sample (g = 0.11 [-0.11 to 0.33]), but the magnitude of individual placebo responsiveness significantly correlated with nonhypnotic suggestibility (rs = 0.27 [0.03 to 0.48]).

CONCLUSIONS

These results suggest that the extent to which direct verbal suggestibility captures variability in placebo responsiveness depends on the use of conditioning and highlights the utility of suggestibility as a potential contributing factor to placebo responding when placebo hypoalgesia is induced through verbal suggestions.

A social affective neuroscience lens on placebo analgesia

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.

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

Predictive processing approaches to brain function are increasingly delivering promise for illuminating the computational underpinnings of a wide range of phenomenological states. It remains unclear, however, whether predictive processing is equipped to accommodate a theory of consciousness itself. Furthermore, objectors have argued that without specification of the core computational mechanisms of consciousness, predictive processing is unable to inform the attribution of consciousness to other non-human (biological and artificial) systems. In this paper, I argue that an account of consciousness in the predictive brain is within reach via recent accounts of phenomenal self-modelling in the active inference framework. The central claim here is that phenomenal consciousness is underpinned by 'subjective valuation'-a deep inference about the precision or 'predictability' of the self-evidencing ('fitness-promoting') outcomes of action. Based on this account, I argue that this approach can critically inform the distribution of experience in other systems, paying particular attention to the complex sensory attenuation mechanisms associated with deep self-models. I then consider an objection to the account: several recent papers argue that theories of consciousness that invoke self-consciousness as constitutive or necessary for consciousness are undermined by states (or traits) of 'selflessness'; in particular the 'totally selfless' states of ego-dissolution occasioned by psychedelic drugs. Drawing on existing work that accounts for psychedelic-induced ego-dissolution in the active inference framework, I argue that these states do not threaten to undermine an active inference theory of consciousness. Instead, these accounts corroborate the view that subjective valuation is the constitutive facet of experience, and they highlight the potential of psychedelic research to inform consciousness science, computational psychiatry and computational phenomenology.

Psychomotor Predictive Processing

Psychomotor experience can be based on what people predict they will experience, rather than on sensory inputs. It has been argued that disconnects between human experience and sensory inputs can be addressed better through further development of predictive processing theory. In this paper, the scope of predictive processing theory is extended through three developments. First, by going beyond previous studies that have encompassed embodied cognition but have not addressed some fundamental aspects of psychomotor functioning. Second, by proposing a scientific basis for explaining predictive processing that spans objective neuroscience and subjective experience. Third, by providing an explanation of predictive processing that can be incorporated into the planning and operation of systems involving robots and other new technologies. This is necessary because such systems are becoming increasingly common and move us farther away from the hunter-gatherer lifestyles within which our psychomotor functioning evolved. For example, beliefs that workplace robots are threatening can generate anxiety, while wearing hardware, such as augmented reality headsets and exoskeletons, can impede the natural functioning of psychomotor systems. The primary contribution of the paper is the introduction of a new formulation of hierarchical predictive processing that is focused on psychomotor functioning.

The placebo effect: To explore or to exploit?

How people choose between options with differing outcomes (explore-exploit) is a central question to understanding human behaviour. However, the standard explore-exploit paradigm relies on gamified tasks with low-stake outcomes. Consequently, little is known about decision making for biologically-relevant stimuli. Here, we combined placebo and explore-exploit paradigms to examine detection and selection of the most effective treatment in a pain model. During conditioning, where 'optimal' and 'suboptimal' sham-treatments were paired with a reduction in electrical pain stimulation, participants learnt which treatment most successfully reduced pain. Modelling participant responses revealed three important findings. First, participants' choices reflected both directed and random exploration. Second, expectancy modulated pain - indicative of recursive placebo effects. Third, individual differences in terms of expectancy during conditioning predicted placebo effects during a subsequent test phase. These findings reveal directed and random exploration when the outcome is biologically-relevant. Moreover, this research shows how placebo and explore-exploit literatures can be unified.

The temporal and spectral characteristics of expectations and prediction errors in pain and thermoception

In the context of a generative model, such as predictive coding, pain and heat perception can be construed as the integration of expectation and input with their difference denoted as a prediction error. In a previous neuroimaging study (Geuter et al., 2017) we observed an important role of the insula in such a model but could not establish its temporal aspects. Here, we employed electroencephalography to investigate neural representations of predictions and prediction errors in heat and pain processing. Our data show that alpha-to-beta activity was associated with stimulus intensity expectation, followed by a negative modulation of gamma band activity by absolute prediction errors. This is in contrast to prediction errors in visual and auditory perception, which are associated with increased gamma band activity, but is in agreement with observations in working memory and word matching, which show gamma band activity for correct, rather than violated, predictions.

Design and conduct of confirmatory chronic pain clinical trials

The purpose of this article is to provide readers with a basis for understanding the emerging science of clinical trials and to provide a set of practical, evidence-based suggestions for designing and executing confirmatory clinical trials in a manner that minimizes measurement error. The most important step in creating a mindset of quality clinical research is to abandon the antiquated concept that clinical trials are a method for capturing data from clinical practice and shifting to a concept of the clinical trial as a measurement system, consisting of an interconnected set of processes, each of which must be in calibration for the trial to generate an accurate and reliable estimate of the efficacy (and safety) of a given treatment. The status quo of inaccurate, unreliable, and protracted clinical trials is unacceptable and unsustainable. This article gathers aspects of study design and conduct under a single broad umbrella of techniques available to improve the accuracy and reliability of confirmatory clinical trials across traditional domain boundaries.

Conditioned Placebo- and Nocebo-Like Effects in Adolescents: The Role of Conscious Awareness, Sensory Discrimination, and Executive Function

Background: Conditioning is a key mechanism of placebo and nocebo effects in adults. Little is known about the underlying mechanisms of placebo and nocebo effects in youth and how they might be influenced by conscious awareness and cognitive abilities. In this study, the role of conditioning on thermal perception in youth was investigated. Methods: Differences in thermal ratings were assessed in response to consciously and non-consciously perceived cues that were conditioned to either low or high heat. Furthermore, we tested whether executive function mediates the effect of conditioning on thermal perception. Thirty-five high-school students (14-17 years) completed an executive function task and underwent a sensory perception paradigm. In a conditioning phase, two distinct neutral faces (conditioned cues) were coupled to either a low or a high temperature stimulus delivered to participants' forearms. In a testing phase, the conditioned cues, and novel faces (non-conditioned control cues), were paired with identical moderate thermal stimuli. In this testing phase, for half of the participants cues were presented consciously (supraliminally) and for the other half non-consciously (subliminally). Results: We found a significant main effect of cue type on thermal ratings (p = 0.003) in spite of identical heat being administered following all cues. Post-hoc analyses indicated that the nocebo-like effect (conditioned high cue compared to control) was significant (p = 0.027); the placebo-like effect (conditioned low cue compared to control) was non-significant. No difference between cues presented supra- vs. subliminally and no significant interaction effects were found. The association between sensory discrimination and the magnitude of the nocebo-like effect was mediated by executive function. Conclusions: To our best knowledge, this is the first study establishing a relationship between thermal perception, nocebo effects, and executive function in youth. Our results may have important implications for understanding cognitive/ learning processes involved in nocebo effects.

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

Computational models of pain consider how the brain processes nociceptive information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. Here, we consider how they may comprise a parallel hierarchical architecture that combines inference, information-seeking, and adaptive value-based control. This sheds light on the complex neural architecture of the pain system, and takes us closer to understanding from where pain 'arises' in the brain.

Certainty in ascending sensory signals - The unexplored driver of analgesic placebo response

Previous frameworks have failed to adequately explain the observed correlation between within-subject variability in pain reporting and analgesic placebo response. These relationships have been observed in both clinical and experimental setups. Within-subject variability of clinical pain scores is traditionally assessed based on daily pain diaries collected during the pre-intervention stage. Experimental variability can be assessed by the Focused Analgesia Selection Test (FAST), which calculates the relationship between noxious stimuli administrated at various intensities and pain reports. The variability, either clinical or experimental, has been shown to predict the placebo response. In explaining the placebo response, Bayesian Brain Hypothesis (BBH) posits that pain perception (posterior), is composed of certainty (precision) of expectations (priors due to belief or conditioning) and incoming sensory information (likelihood), with the bulk of research focused on the precision of priors. Virtually all placebo analgesia research has focused on the priors and their certainty, rather than on the certainty of the likelihood, mainly because it cannot be assessed directly. We propose that the within-subject variability, as encapsulated by the FAST, is a proxy for certainty in (or, precision of) ascending sensory signals, and our results suggest that it could not only be assessed, but also manipulated. If true, our hypothesis will facilitate new lines of research and could potentially promote precision analgesic medicine by use of variability of pain scores as a diagnostic method to identify pain patients who will benefit from specific treatments.

Placebos in chronic pain: evidence, theory, ethics, and use in clinical practice

Despite their ubiquitous presence, placebos and placebo effects retain an ambiguous and unsettling presence in biomedicine. Specifically focused on chronic pain, this review examines the effect of placebo treatment under three distinct frameworks: double blind, deception, and open label honestly prescribed. These specific conditions do not necessarily differentially modify placebo outcomes. Psychological, clinical, and neurological theories of placebo effects are scrutinized. In chronic pain, conscious expectation does not reliably predict placebo effects. A supportive patient-physician relationship may enhance placebo effects. This review highlights "predictive coding" and "bayesian brain" as emerging models derived from computational neurobiology that offer a unified framework to explain the heterogeneous evidence on placebos. These models invert the dogma of the brain as a stimulus driven organ to one in which perception relies heavily on learnt, top down, cortical predictions to infer the source of incoming sensory data. In predictive coding/bayesian brain, both chronic pain (significantly modulated by central sensitization) and its alleviation with placebo treatment are explicated as centrally encoded, mostly non-conscious, bayesian biases. The review then evaluates seven ways in which placebos are used in clinical practice and research and their bioethical implications. In this way, it shows that placebo effects are evidence based, clinically relevant, and potentially ethical tools for relieving chronic pain.

Threat Prediction from Schemas as a Source of Bias in Pain Perception

Our sensory impressions of pain are generally thought to represent the noxious properties of an agent but can be influenced by the predicted level of threat. Predictions can be sourced from higher-order cognitive processes, such as schemas, but the extent to which schemas can influence pain perception relative to bottom-up sensory inputs and the underlying neural underpinnings of such a phenomenon are unclear. Here, we investigate how threat predictions generated from learning a cognitive schema lead to inaccurate sensory impressions of the pain stimulus. Healthy male and female participants first detected a linear association between cue values and stimulus intensity and rated pain to reflect the linear schema when compared with uncued heat stimuli. The effect of bias on pain ratings was reduced when prediction errors (PEs) increased, but pain perception was only partially updated when measured against stepped increases in PEs. Cognitive, striatal, and sensory regions graded their responses to changes in predicted threat despite the PEs (p < 0.05, corrected). Individuals with more catastrophic thinking about pain and with low mindfulness were significantly more reliant on the schema than on the sensory evidence from the pain stimulus. These behavioral differences mapped to variability in responses of the striatum and ventromedial prefrontal cortex. Thus, this study demonstrates a significant role of higher-order schemas in pain perception and indicates that pain perception is biased more toward predictions and less toward nociceptive inputs in individuals who report less mindfulness and more fear of pain.SIGNIFICANCE STATEMENT This study demonstrates that threat predictions generated from cognitive schemas continue to influence pain perception despite increasing prediction errors arising in pain pathways. Individuals first formed a cognitive schema of linearity in the relationship between the cued threat value and the stimulus intensity. Subsequently, the linearity was reduced gradually, and participants partially updated their evaluations of pain in relation to the stepped increases in prediction errors. Individuals who continued to rate pain based more on the predicted threat than on changes in nociceptive inputs reported high pain catastrophizing and less mindful-awareness scores. These two affects mapped to activity in the ventral and dorsal striatum, respectively. These findings direct us to a significant role of top-down processes in pain perception.

Modeling Learning Patterns to Predict Placebo Analgesic Effects in Healthy and Chronic Orofacial Pain Participants

Successfully predicting the susceptibility of individuals to placebo analgesics will aid in developing more effective pain medication and therapies, as well as aiding potential future clinical use of placebos. In pursuit of this goal, we analyzed healthy and chronic pain patients' patterns of responsiveness during conditioning rounds and their links to conditioned placebo analgesia and the mediating effect of expectation on those responses. We recruited 579 participants (380 healthy, 199 with temporomandibular disorder [TMD]) to participate in a laboratory placebo experiment. Individual pain sensitivity dictated the temperatures used for high- and low-pain stimuli, paired with red or green screens, respectively, and participants were told there would be an analgesic intervention paired with the green screens. Over two conditioning sessions and one testing session, participants rated the painfulness of each stimulus on a visual analogue scale from 0 to 100. During the testing phase, the same temperature was used for both red and green screens to assess responses to the placebo effect, which was defined as the difference between the average of the high-pain-cue stimuli and low-pain-cue stimuli. Delta scores, defined as each low-pain rating subtracted from its corresponding high-pain rating, served as a means of modeling patterns of conditioning strength and placebo responsiveness. Latent class analysis (LCA) was then conducted to classify the participants based on the trajectories of the delta values during the conditioning rounds. Classes characterized by persistently greater or increasing delta scores during conditioning displayed greater placebo analgesia during testing than those with persistently lower or decreasing delta scores. Furthermore, the identified groups' expectation of pain relief acted as a mediator for individual placebo analgesic effects. This study is the first to use LCA to discern the relationship between patterns of learning and the resultant placebo analgesia in chronic pain patients. In clinical settings, this knowledge can be used to enhance clinical pain outcomes, as chronic pain patients with greater prior experiences of pain reduction may benefit more from placebo analgesia.

Pain: A Precision Signal for Reinforcement Learning and Control

Since noxious stimulation usually leads to the perception of pain, pain has traditionally been considered sensory nociception. But its variability and sensitivity to a broad array of cognitive and motivational factors have meant it is commonly viewed as inherently imprecise and intangibly subjective. However, the core function of pain is motivational-to direct both short- and long-term behavior away from harm. Here, we illustrate that a reinforcement learning model of pain offers a mechanistic understanding of how the brain supports this, illustrating the underlying computational architecture of the pain system. Importantly, it explains why pain is tuned by multiple factors and necessarily supported by a distributed network of brain regions, recasting pain as a precise and objectifiable control signal.

Boundary effects of expectation in human pain perception

Perception of sensory stimulation is influenced by numerous psychological variables. One example is placebo analgesia, where expecting low pain causes a painful stimulus to feel less painful. Yet, because pain evolved to signal threats to survival, it should be maladaptive for highly-erroneous expectations to yield unrealistic pain experiences. Therefore, we hypothesised that a cue followed by a highly discrepant stimulus intensity, which generates a large prediction error, will have a weaker influence on the perception of that stimulus. To test this hypothesis we collected two independent pain-cueing datasets. The second dataset and the analysis plan were preregistered ( https://osf.io/5r6z7/ ). Regression modelling revealed that reported pain intensities were best explained by a quartic polynomial model of the prediction error. The results indicated that the influence of cues on perceived pain decreased when stimulus intensity was very different from expectations, suggesting that prediction error size has an immediate functional role in pain perception.

The Landscape of Chronic Pain: Broader Perspectives

Chronic pain is a global health concern. This special issue on matters related to chronic pain aims to draw on research and scholarly discourse from an eclectic mix of areas and perspectives. The purpose of this non-systematic topical review is to précis an assortment of contemporary topics related to chronic pain and its management to nurture debate about research, practice and health care policy. The review discusses the phenomenon of pain, the struggle that patients have trying to legitimize their pain to others, the utility of the acute-chronic dichotomy, and the burden of chronic pain on society. The review describes the introduction of chronic primary pain in the World Health Organization's International Classification of Disease, 11th Revision and discusses the importance of biopsychosocial approaches to manage pain, the consequences of overprescribing and shifts in service delivery in primary care settings. The second half of the review explores pain perception as a multisensory perceptual inference discussing how contexts, predictions and expectations contribute to the malleability of somatosensations including pain, and how this knowledge can inform the development of therapies and strategies to alleviate pain. Finally, the review explores chronic pain through an evolutionary lens by comparing modern urban lifestyles with genetic heritage that encodes physiology adapted to live in the Paleolithic era. I speculate that modern urban lifestyles may be painogenic in nature, worsening chronic pain in individuals and burdening society at the population level.

Behavioural and neural evidence for self-reinforcing expectancy effects on pain

Beliefs and expectations often persist despite evidence to the contrary. Here we examine two potential mechanisms underlying such 'self-reinforcing' expectancy effects in the pain domain: modulation of perception and biased learning. In two experiments, cues previously associated with symbolic representations of high or low temperatures preceded painful heat. We examined trial-to-trial dynamics in participants' expected pain, reported pain and brain activity. Subjective and neural pain responses assimilated towards cue-based expectations, and pain responses in turn predicted subsequent expectations, creating a positive dynamic feedback loop. Furthermore, we found evidence for a confirmation bias in learning: higher- and lower-than-expected pain triggered greater expectation updating for high- and low-pain cues, respectively. Individual differences in this bias were reflected in the updating of pain-anticipatory brain activity. Computational modelling provided converging evidence that expectations influence both perception and learning. Together, perceptual assimilation and biased learning promote self-reinforcing expectations, helping to explain why beliefs can be resistant to change.

Testing the Possibility of Model-based Pavlovian Control of Attention to Threat

Signals for reward or punishment attract attention preferentially, a principle termed value-modulated attention capture (VMAC). The mechanisms that govern the allocation of attention can be described with a terminology that is more often applied to the control of overt behaviors, namely, the distinction between instrumental and Pavlovian control, and between model-free and model-based control. Although instrumental control of VMAC can be either model-free or model-based, it is not known whether Pavlovian control of VMAC can be model-based. To decide whether this is possible, we measured steady-state visual evoked potentials (SSVEPs) while 20 healthy adults took part in a novel task. During the learning stage, participants underwent aversive threat conditioning with two conditioned stimuli (CSs): one that predicted pain (CS+) and one that predicted safety (CS-). Instructions given before the test stage allowed participants to infer whether novel, ambiguous CSs (new_CS+/new_CS-) were threatening or safe. Correct inference required combining stored internal representations and new propositional information, the hallmark of model-based control. SSVEP amplitudes quantified the amount of attention allocated to novel CSs on their very first presentation, before they were ever reinforced. We found that SSVEPs were higher for new_CS+ than new_CS-. This result is potentially indicative of model-based Pavlovian control of VMAC, but additional controls are necessary to verify this conclusively. This result underlines the potential transformative role of information and inference in emotion regulation.

Lysergic acid diethylamide and psilocybin for the management of patients with persistent pain: a potential role?

Recently, there has been interest in lysergic acid diethylamide (LSD) and psilocybin for depression, anxiety and fear of death in terminal illness. The aim of this review is to discuss the potential use of LSD and psilocybin for patients with persistent pain. LSD and psilocybin are 5-hydroxytryptamine receptor agonists and may interact with nociceptive and antinociceptive processing. Tentative evidence from a systematic review suggests that LSD (7 studies, 323 participants) and psilocybin (3 studies, 92 participants) may be beneficial for depression and anxiety associated with distress in life-threatening diseases. LSD and psilocybin are generally safe if administered by a healthcare professional, although further investigations are needed to assess their utility for patients with persistent pain, especially associated with terminal illness.

The periaqueductal gray and Bayesian integration in placebo analgesia

In placebo hypoalgesia research, the strength of treatment expectations and experiences are key components. However, the reliability or precision of expectations had been mostly ignored although being a likely source for interindividual differences. In the present study, we adopted a Bayesian framework, naturally combining expectation magnitudes and precisions. This postulates that expectations (prior) are integrated with incoming nociceptive information (likelihood) and both are weighted by their relative precision to form the pain percept and placebo effect. Sixty-two healthy subjects received heat pain during fMRI. Placebo effects were more pronounced in subjects with more precise treatment expectations and correlated positively with the relative precision of the prior expectation. Neural correlates of this precision were observed in the periaqueductal gray and the rostral ventromedial medulla, indicating that already at the level of the brainstem the precision of an expectation can influence pain perception presenting strong evidence for Bayesian integration in placebo hypoalgesia.

On a battlefield in World War II, surgeon Henry Beecher ran out of morphine. To his surprise, he found that replacing the missing morphine with saltwater allowed him to continue operating on wounded soldiers. Although saltwater contains no active pain-relieving ingredients, it reduced the soldiers’ pain. This is an example of the placebo effect. Placebos have been shown to reduce autonomic responses to pain, such as sweating. They also modulate activity in brain regions that process pain. But why do some of us experience larger placebo effects than others?

Grahl et al. propose that the size of the placebo effect depends on our expectations about a treatment. More specifically, it depends on how precise those expectations are. Imagine two people who have taken the same treatment many times, and who have experienced the same average reduction in pain. But for one person, the treatment reduced their pain by roughly the same amount each time. For the other, the treatment sometimes reduced their pain by a large amount and other times hardly at all. The first person will have more precise expectations than the second about how effective the treatment will be in future. Grahl et al. propose that the first person will thus experience a greater placebo effect in response to a ‘fake’ version of the treatment.

To test this idea, Grahl et al. applied painful heat to the forearms of healthy volunteers lying inside a brain scanner. On half the trials, the volunteers were told that they would also receive an electrical pain-relieving therapy. In reality, this treatment was never applied. After each trial, the volunteers rated the intensity of the pain they had experienced. As expected, the volunteers reported less pain when they thought they were receiving a pain-relieving treatment. Moreover, those volunteers with more precise expectations about the treatment reported greater pain relief than volunteers with less precise expectations. The former group also showed less activity in one of the brain’s major pain-processing centers, the periaqueductal gray.

These findings help shed light on why some people experience larger placebo effects than others. They suggest that helping patients form precise expectations about their treatment, by giving them precise information about its likely effectiveness, may boost the placebo effect. Further studies are needed to determine whether this phenomenon also occurs in patients with pain disorders. If it does, it could help such patients manage their pain using fewer active painkillers.

Perceptual Inference in Chronic Pain: An Investigation Into the Economy of Action Hypothesis

OBJECTIVE

The experience of chronic pain critically alters one's ability to interact with their environment. One fundamental issue that has received little attention, however, is whether chronic pain disrupts how one perceives their environment in the first place. The Economy of Action hypothesis purports that the environment is spatially scaled according to the ability of the observer. Under this hypothesis it has been proposed that the perception of the world is different between those with and without chronic pain. Such a possibility has profound implications for the investigation and treatment of pain. The present investigation tested the application of this hypothesis to a heterogenous chronic pain population.

METHODS

Individuals with chronic pain (36; 27F) and matched pain-free controls were recruited. Each participant was required to judge the distance to a series of target cones, to which they were to subsequently walk. In addition, at each distance, participants used Numerical Rating Scales to indicate their perceived effort and perceived pain associated with the distance presented.

RESULTS

Our findings do not support the Economy of Action hypothesis: there were no significant differences in distance estimates between the chronic pain and pain-free groups (F1,60=0.927; P=0.340). In addition, we found no predictive relationship in the chronic pain group between anticipated pain and estimated distance (F1,154=0.122, P=0.727), nor anticipated effort (1.171, P=0.281) and estimated distance (F1,154=1.171, P=0.281).

DISCUSSION

The application of the Economy of Action hypothesis and the notion of spatial perceptual scaling as a means to assess and treat the experience of chronic pain are not supported by the results of this study.

Bayesian prediction of placebo analgesia in an instrumental learning model

Placebo analgesia can be primarily explained by the Pavlovian conditioning paradigm in which a passively applied cue becomes associated with less pain. In contrast, instrumental conditioning employs an active paradigm that might be more similar to clinical settings. In the present study, an instrumental conditioning paradigm involving a modified trust game in a simulated clinical situation was used to induce placebo analgesia. Additionally, Bayesian modeling was applied to predict the placebo responses of individuals based on their choices. Twenty-four participants engaged in a medical trust game in which decisions to receive treatment from either a doctor (more effective with high cost) or a pharmacy (less effective with low cost) were made after receiving a reference pain stimulus. In the conditioning session, the participants received lower levels of pain following both choices, while high pain stimuli were administered in the test session even after making the decision. The choice-dependent pain in the conditioning session was modulated in terms of both intensity and uncertainty. Participants reported significantly less pain when they chose the doctor or the pharmacy for treatment compared to the control trials. The predicted pain ratings based on Bayesian modeling showed significant correlations with the actual reports from participants for both of the choice categories. The instrumental conditioning paradigm allowed for the active choice of optional cues and was able to induce the placebo analgesia effect. Additionally, Bayesian modeling successfully predicted pain ratings in a simulated clinical situation that fits well with placebo analgesia induced by instrumental conditioning.

Pain: A Statistical Account

Perception is seen as a process that utilises partial and noisy information to construct a coherent understanding of the world. Here we argue that the experience of pain is no different; it is based on incomplete, multimodal information, which is used to estimate potential bodily threat. We outline a Bayesian inference model, incorporating the key components of cue combination, causal inference, and temporal integration, which highlights the statistical problems in everyday perception. It is from this platform that we are able to review the pain literature, providing evidence from experimental, acute, and persistent phenomena to demonstrate the advantages of adopting a statistical account in pain. Our probabilistic conceptualisation suggests a principles-based view of pain, explaining a broad range of experimental and clinical findings and making testable predictions.

Rethinking Explicit Expectations: Connecting Placebos, Social Cognition, and Contextual Perception

Expectancy effects are a widespread phenomenon, and they come with a lasting influence on cognitive operations, from basic stimulus processing to higher cognitive functions. Their impact is often profound and behaviorally significant, as evidenced by an enormous body of literature investigating the characteristics and possible processes underlying expectancy effects. The literature on this topic spans diverse fields, from clinical psychology to cognitive neuroscience, and from social psychology to behavioral biology. We present an emerging perspective on these diverse phenomena and show how this perspective stimulates new toeholds for investigation, provides insight in underlying mechanisms, improves awareness of methodological confounds, and can lead to a deeper understanding of the effects of expectations on a broad spectrum of cognitive processes.