Confidence of probabilistic predictions modulates the cortical response to pain

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.

Expectations underlie the effects of unpredictable pain: a behavioral and electroencephalogram study

ABSTRACT

Previous studies on the potential effects of unpredictability on pain perception and its neural correlates yielded divergent results. This study examined whether this may be explained by differences in acquired expectations. We presented 41 healthy volunteers with laser heat stimuli of different intensities. The stimuli were preceded either by predictable low, medium, or high cues or by unpredictable low-medium, medium-high, or low-high cues. We recorded self-reports of pain intensity and unpleasantness and laser-evoked potentials (LEPs). Furthermore, we investigated whether dynamic expectations that evolved throughout the experiment based on past trials were better predictors of pain ratings than fixed (nonevolving) expectations. Our results replicate previous findings that unpredictable pain is higher than predictable pain for low-intensity stimuli but lower for high-intensity stimuli. Moreover, we observed higher ratings for the medium-high unpredictable condition than the medium-low unpredictable condition, in line with an effect of expectation. We found significant interactions (N1, N2) for the LEP components between intensity and unpredictability. However, the few significant differences in LEP peak amplitudes between cue conditions did not survive correction for multiple testing. In line with predictive coding perspectives, pain ratings were best predicted by dynamic expectations. Surprisingly, expectations of reduced precision (increased variance) were associated with lower pain ratings. Our findings provide strong evidence that (dynamic) expectations contribute to the opposing effects of unpredictability on pain perception; therefore, we highlight the importance of controlling for them in pain unpredictability manipulations. We also suggest to conceptualize pain expectations more often as dynamic constructs incorporating previous experiences.

The effect of unpredictability on the perception of pain: a systematic review and meta-analysis

ABSTRACT

Despite being widely assumed, the worsening impact of unpredictability on pain perception remains unclear because of conflicting empirical evidence, and a lack of systematic integration of past research findings. To fill this gap, we conducted a systematic review and meta-analysis focusing on the effect of unpredictability on pain perception. We also conducted meta-regression analyses to examine the moderating effect of several moderators associated with pain and unpredictability: stimulus duration, calibrated stimulus pain intensity, pain intensity expectation, controllability, anticipation delay, state and trait negative affectivity, sex/gender and age of the participants, type of unpredictability (intensity, onset, duration, location), and method of pain induction (thermal, electrical, mechanical pressure, mechanical distention). We included 73 experimental studies with adult volunteers manipulating the (un)predictability of painful stimuli and measuring perceived pain intensity and pain unpleasantness in predictable and unpredictable contexts. Because there are insufficient studies with patients, we focused on healthy volunteers. Our results did not reveal any effect of unpredictability on pain perception. However, several significant moderators were found, ie, targeted stimulus pain intensity, expected pain intensity, and state negative affectivity. Trait negative affectivity and uncontrollability showed no significant effect, presumably because of the low number of included studies. Thus, further investigation is necessary to clearly determine their role in unpredictable pain perception.

The temporal effect of uncertain context on the perceptual processing of painful and non-painful stimulation

Uncertainty has been demonstrated to influence the perception of noxious stimuli, but little is known about the effects of prolonged uncertain contexts on the perception of painful and non-painful stimuli. To address this knowledge gap, the present study utilized a cue-based NPU-threat task, where uncertain and certain trials were separated into distinct blocks. The objective was to investigate the impact of uncertain contexts on the temporal dynamics of electroencephalogram (EEG) activity during the processing of painful and non-painful stimuli. The results revealed that the influence of uncertain contexts on neural responses extends beyond painful trials and is also evident in non-painful trials. In uncertain contexts, it has been observed that painful stimuli elicit larger P2 amplitudes and late beta band (13-30 Hz) event-related desynchronization (ERD) around 500-700 ms. However, in certain contexts, painful stimuli evoke stronger late gamma band (50-70 Hz) event-related synchronization (ERS) around 600-700 ms. For non-painful trials, in uncertain contexts, significantly higher amplitudes of the late positive potential (LPP) component and delta-theta band (2-7 Hz) ERS were observed compared to certain non-painful stimuli. These findings demonstrate that uncertain contexts exert a significant impact on the processing of both painful and non-painful stimuli, and this influence is mediated by distinct neural mechanisms.

Lifeworlds in pain: a principled method for investigation and intervention

The experience of pain spans biological, psychological and sociocultural realms, both basic and complex, it is by turns necessary and devastating. Despite an extensive knowledge of the constituents of pain, the ability to translate this into effective intervention remains limited. It is suggested that current, multiscale, medical approaches, largely informed by the biopsychosocial (BPS) model, attempt to integrate knowledge but are undermined by an epistemological obligation, one that necessitates a prior isolation of the constituent parts. To overcome this impasse, we propose that an anthropological stance needs to be taken, underpinned by a Bayesian apparatus situated in computational psychiatry. Here, pain is presented within the context of lifeworlds, where attention is shifted away from the constituents of experience (e.g. nociception, reward processing and fear-avoidance), towards the dynamic affiliation that occurs between these processes over time. We argue that one can derive a principled method of investigation and intervention for pain from modelling approaches in computational psychiatry. We suggest that these modelling methods provide the necessary apparatus to navigate multiscale ontology and epistemology of pain. Finally, a unified approach to the experience of pain is presented, where the relational, inter-subjective phenomenology of pain is brought into contact with a principled method of translation; in so doing, revealing the conditions and possibilities of lifeworlds in pain.