Generalization of learned pain modulation depends on explicit learning

Koban, Léonie; Kusko, Daniel; Wager, Tor D.

doi:10.1016/j.actpsy.2017.09.009

Cited by 24 publications

(26 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with our previous findings 42 , self-reported expectations mediated CS effects on pain. This reflects an important role of contingency awareness in this type of learned pain modulation, which is based on trace conditioning 74,75 (i.e., involving a delay between CS and reinforcer). In contrast, paradigms that use delay conditioning can elicit subliminal learning effects on pain 76,77 .…”

Section: Discussionmentioning

confidence: 99%

“…Further, in order to avoid a blurring of cue-evoked activity into the pain-related brain activity, pain stimuli were presented several seconds (jittered) after the cues, similar to previous studies 17,42,75,92 . This delay induces trace conditioning, which involves partially different mechanisms than “delay conditioning”, which has been used in some other pain learning studies 21,77,86,93 .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Different brain networks mediate the effects of social and conditioned expectations on pain

et al. 2019

Self Cite

View full text Add to dashboard Cite

Information about others’ experiences can strongly influence our own feelings and decisions. But how does such social information affect the neural generation of affective experience, and are the brain mechanisms involved distinct from those that mediate other types of expectation effects? Here, we used fMRI to dissociate the brain mediators of social influence and associative learning effects on pain. Participants viewed symbolic depictions of other participants’ pain ratings (social information) and classically conditioned pain-predictive cues before experiencing painful heat. Social information and conditioned stimuli each had significant effects on pain ratings, and both effects were mediated by self-reported expectations. Yet, these effects were mediated by largely separable brain activity patterns, involving different large-scale functional networks. These results show that learned versus socially instructed expectations modulate pain via partially different mechanisms—a distinction that should be accounted for by theories of predictive coding and related top-down influences.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Different brain networks mediate the effects of social and conditioned expectations on pain

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pavlovian predictions are sensitive to uncertainty, which enhances the learning rate and controls autonomic responses such as skin conductance putatively through an amygdala-dependent process (Li et al, 2011;Boll et al, 2013;Zhang et al, 2016). Pavlovian values also generalize to perceptually and conceptually similar cues, allowing pain predictions to be made to novel stimuli in an efficient way (Onat and B€ uchel, 2015;Dunsmoor et al, 2011;Dunsmoor and Kroes, 2019;Koban et al, 2018).…”

Section: Pavlovian Learningmentioning

confidence: 99%

Pain: A Precision Signal for Reinforcement Learning and Control

Seymour

2019

Neuron

115

104

View full text Add to dashboard Cite

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...

show abstract

“…This occurred regardless of whether a single or multiple exemplar(s) were used in the conditioning phase. Meanwhile, Koban, Kusko, and Wager (2018) conducted a similar experiment where one of the two line drawing images (one depicting an animal and another a vehicle) was paired with high-intensity pain (thermal stimulation), while another with low-intensity pain. In the generalization phase, they paired novel stimuli (line drawings, photographs, words) from the animal and vehicle categories with a medium-intensity pain.…”

Section: Semantic-based Associative Learning and Generalizationmentioning

confidence: 99%

Unconscious preparation: Effects of prime visibility on semantic generalization of task priming

Zher-Wen

2021

British J of Psychology

View full text Add to dashboard Cite

Studies found that subliminal primes can be associated with specific tasks to facilitate task performance, and such learning is highly adaptive and generalizable. Meanwhile, conditioning studies suggest that aversive/reward learning and generalization actually occur at the semantic level. The current study shows that prime-task associations can also be generalized to novel word/neighbour primes from the same semantic category, and this occurs without contingency awareness. Previous studies have counterintuitively suggested that both the learning of task priming and the semantic priming of word neighbours depend on the lack of visibility. Here, we show that semantic generalization indeed depends on reduced visibility, but cannot occur subliminally. The current study shows for the first time that semantic learning and generalization can occur without any emotional or motivational factors, and that semantic priming can occur for arbitrarylinked stimuli in a context completely devoid of semantics.

show abstract

Generalization of learned pain modulation depends on explicit learning

Cited by 24 publications

References 72 publications

Different brain networks mediate the effects of social and conditioned expectations on pain

Different brain networks mediate the effects of social and conditioned expectations on pain

Pain: A Precision Signal for Reinforcement Learning and Control

Unconscious preparation: Effects of prime visibility on semantic generalization of task priming

Contact Info

Product

Resources

About