Biased pain reports through vicarious information: A computational approach to investigate the role of uncertainty

Zaman, Jonas; Vanpaemel, Wolf; Aelbrecht, Celine; Tuerlinckx, Francis

doi:10.1016/j.cognition.2017.07.009

Cited by 19 publications

(27 citation statements)

References 35 publications

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“…However, in the case of tonic pain, its relief acts as the teaching signal, and ongoing pain has a direct suppressive effect on cognition, and so ongoing pain should be reduced to enhance relief learning. Existing support for this is mixed (Yoshida et al, 2013;Brown et al, 2008;Zaman et al, 2017;Br€ ascher et al, 2016;Wiech et al, 2006;Salomons et al, 2007;Zhang et al, 2018a), partly because uncertainty is not always studied in the context of controllability, and increased controllability often results in reduced uncertainty, leaving this issue to be fully demonstrated. d Endogenous control should drive exploration.…”

Section: Modulation By Informational Valuementioning

confidence: 99%

Pain: A Precision Signal for Reinforcement Learning and Control

Seymour

2019

Neuron

115

104

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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.Despite the advent of brain imaging, a clear picture of how pain is processed in the brain has been much harder to unravel than anticipated, being beset by three problems. First, pain is associated with robust responses in multiple and diverse brain regions, most of which are not specific to pain (at least on a macroscopic scale), and so it has been hard to ''pin down'' the pain system to any specific brain region. Second, pain is an inherently private percept, but an individual's self-reports of pain can vary widely from moment to moment, and it has remained unclear whether this fluctuation represents irreducible noise and subjectivity or a precise tuning of pain based on hidden factors. Third, pain is exquisitely sensitive to a broad range of emotional, environmental, and cognitive factors-a phenomenon called endogenous modulation. Although this has led to an appreciation that pain is more than a simple readout of nociceptive input, it has not led to any satisfactory unified explanation as to what pain really is. This has left the view that pain is simply a highly variable and malleable representation of assumed actual or potential tissue damage.In this review, we propose a model of pain that centralizes its role as a learning and control signal and argue that this can solve these problems. We begin with a perspective of how theories of pain have evolved over recent decades, and how insights have emerged that have moved thinking beyond purely sensory accounts of pain. We then argue that current accounts still don't fully capture how pain controls behavior to minimize harm, which is its primary function. Importantly, although this is often achieved by immediate nocifensive responses, a substantial part of this comes from learning-allowing an animal to mitigate or avoid predictable harm long into the future. The foundations of a learning account of pain are rooted in psychological models of animal learning, and we describe how these can be developed in computational terms to provide a mechanistic model of the architecture of the pain system. Critically, we argue that this requires pain to be shaped by a set of factors to optimize its role as a learning and control signal and review evidence that suggests that a great...

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Section: Modulation By Informational Valuementioning

confidence: 99%

Pain: A Precision Signal for Reinforcement Learning and Control

Seymour

2019

Neuron

115

104

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“…40 However, a recent study failed to replicate their finding in a larger sample (N 5 40). 41 Another way to induce uncertainty, where differences in expected pain levels between conditions are less likely to occur, is to manipulate onset predictability. In those paradigms, participants have information about the stimulus intensity, but their ability to predict its onset varies between conditions.…”

Section: Eventmentioning

confidence: 99%

Uncertainty in a context of pain: disliked but also more painful?

Zaman

Oudenhove

Vlaeyen

2020

Pain

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“…Expectations have been shown to affect pain perception in an experimental context 3,8,15,33 and to predict pain treatment outcomes including long-term disability 24 and psychological functioning 7,9,11 in clinical populations.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Decision Parameters and Their Relation to Self-Reported Pain: A Drift Diffusion Account

Zaman

Wiech

2020

The Journal of Pain

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Pain intensity ratings are subject to various cognitive modulations-yet the mechanisms underlying this influence are still not understood. In a conditioning protocol, pain-related expectations were induced through pairing predefined movements with a noxious or innocuous stimulus in either a predictable or unpredictable fashion. Healthy volunteers (N = 37) categorized the stimuli as either painful or non-painful and rated their perceived intensity. Using a Hierarchical Drift Diffusion model based on the categorization data, we found that an a priori decision-making bias evolved towards the expected sensations (p < .001). In particular, our findings suggest that differences in both the amount of decision-making bias (p = .004) and the speed sensory processing predict pain intensity ratings (p < .001). As such, changes in pain ratings could be based in either of these processes, which may require a different approach when targeted as part of psychological pain treatment. Perspective Changes in reported pain levels were linked to two distinct mechanisms, suggesting that increased pain reports could be attributed to either enhanced sensory processing or biased inferences. Our results might contribute to development of person-tailored treatments based on the identification of latent mechanisms using computational models.

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Biased pain reports through vicarious information: A computational approach to investigate the role of uncertainty

Cited by 19 publications

References 35 publications

Pain: A Precision Signal for Reinforcement Learning and Control

Pain: A Precision Signal for Reinforcement Learning and Control

Uncertainty in a context of pain: disliked but also more painful?

Perceptual Decision Parameters and Their Relation to Self-Reported Pain: A Drift Diffusion Account

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