The exploration-exploitation dilemma in pain: an experimental investigation

From the lab to the clinic: Advancing pain exposure using principles of functional analysis

In this article, we use the example of pain exposure therapy to illustrate how behavioral pain treatments can be systematically personalized following the principles of functional analysis. Based on the fear-avoidance model, pain exposure therapy has evolved as a mechanistically-based treatment to modify the mechanism of avoidance learning with the aim to reduce disability levels. We first present experimental evidence on avoidance learning from a general psychological perspective. We then illustrate how functional analysis can help therapists understand and address individual drivers of pain avoidance behavior. Finally, we explore how the network theory can translate the principles of functional analysis into statistical parameters using intensive longitudinal data, potentially making exposure experiments more relevant to the daily lives of individuals.

Exploring when to exploit: the cognitive underpinnings of foraging-type decisions in relation to psychopathy

Impairments in reinforcement learning (RL) might underlie the tendency of individuals with elevated psychopathic traits to behave exploitatively, as they fail to learn from their mistakes. Most studies on the topic have focused on binary choices, while everyday functioning requires us to learn the value of multiple options. In this study, we evaluated the cognitive correlates of naturalistic foraging-type decision-making and their electrophysiological signatures in a community sample (n = 108) with varying degrees of psychopathic traits. Reinforcers with different salience were included in a foraging-type decision-making task. Recruitment of various cognitive processes was estimated with a computational model and electrophysiology, and the relationships to psychopathic traits were assessed. Higher Antisocial traits were associated with a bias towards expecting more volatility in the environment when high-salience reinforcers were used. Additionally, higher levels of Interpersonal traits were associated with reduced learning from personalized rewards, as evidenced by reductions in the prediction errors (PEs) about rate of change. Higher Affective traits were associated with lower PEs and aberrant learning from painful punishments. Lastly, the PEs about rate of change were reflected in the trial-wise trajectories of Feedback-Related Negativity event-related potentials. Together, our results point to the importance of volatility processing in understanding aberrant decision-making in relation to psychopathy, demonstrate the relationships between psychopathic traits and learning through reward and punishment, and emphasise the potentially more beneficial effect of personalized rewards and punishment for improving reinforcement-based decision-making in individuals with elevated psychopathic traits.

Diminished pain sensitivity mediates the relationship between psychopathic traits and reduced learning from pain

Individuals with elevated psychopathic traits exhibit decision-making deficits linked to a failure to learn from negative outcomes. We investigated how reduced pain sensitivity affects reinforcement-based decision-making in individuals with varying levels of psychopathic traits, as measured by the Self-Report Psychopathy Scale-Short Form. Using computational modelling, we estimated the latent cognitive processes in a community non-offender sample (n = 111) that completed a task with choices leading to painful and non-painful outcomes. Higher psychopathic traits were associated with reduced pain sensitivity and disturbances in reinforcement learning from painful outcomes. In a Structural Equation Model, a superordinate psychopathy factor was associated with a faster return to original stimulus-outcome associations as pain tolerance increased. This provides evidence directly linking reduced pain sensitivity and learning from painful outcomes with elevated psychopathic traits. Our results offer insights into the computational mechanisms of maladaptive decision-making in psychopathy and antisocial behavior.

Pavlovian-to-instrumental transfer in individuals with chronic pain

Avoidance of pain has been argued to be key factor leading pain events to chronic disability. In this respect, research has focused on investigating the working mechanisms of avoidance's acquisition. Avoidance of painful stimuli has been traditionally studied using a combination of Pavlovian and Instrumental procedures. However, such approach seems to go against real-life scenarios where avoidance is commonly acquired more readily. Using a novel pain avoidance paradigm, we tested whether pain avoidance can be installed in absence of associations between a cue and pain omission, and whether such avoidance differs between pain patients and healthy controls. Participants first learned to avoid painful stimuli by pressing a grip bar. Then, they passively encountered pairings of one geometrical shape with pain and of another geometrical shape without pain. Lastly, participants encountered the geometrical shapes while being able to use the grip bar. Results showed that participants pressed the bar more vigorously when encountering the previously pain-related shape compared to the pain-unrelated shape. This effect did not seem to differ between pain patients and healthy control. Our study could inspire a new way in measuring avoidance in pain, possibly paving the way to better understanding how avoidance is installed in chronic pain.

Beyond yellow flags: The Big-Five personality traits and psychologically informed musculoskeletal rehabilitation

BACKGROUND

Psychosocial variables are known to play an important role in musculoskeletal pain. Recent efforts incorporating psychological theory into rehabilitative medicine, as part of patient-centred care or psychologically informed physical therapy, have gained broader acceptance. The fear-avoidance model is the dominant psychosocial model and has introduced a variety of phenomena which assess psychological distress (i.e., yellow flags). Yellow flags, such as fear, anxiety and catastrophizing, are useful concepts for musculoskeletal providers but reflect a narrow range of psychological responses to pain.

OBJECTIVE

Clinicians lack a more comprehensive framework to understand psychological profiles of each patient and provide individualised care. This narrative review presents the case for applying personality psychology and the Big-Five trait model (extraversion, agreeableness, conscientiousness, neuroticism and openness to experience) to musculoskeletal medicine. These traits have strong associations with various health outcomes and provide a robust framework to understand patient emotion, motivation, cognition and behaviour.

KEY RESULTS

High conscientiousness is associated with positive health outcomes and health promoting behaviours. High neuroticism with low conscientiousness increases the odds of negative health outcomes. Extraversion, agreeableness and openness have less direct effects but have positive correlations with important health behaviours, including active coping, positive affect, rehabilitation compliance, social connection and education level.

CLINICAL APPLICATION

The Big-Five model offers an evidence-based way for MSK providers to better understand the personality of their patients and how it relates to health. These traits offer the potential for additional prognostic factors, tailored treatments and psychological intervention.

The role of intolerance of uncertainty when solving the exploration-exploitation dilemma

When making behavioral decisions, individuals need to balance between exploiting known options or exploring new ones. How individuals solve this exploration-exploitation dilemma (EED) is a key research question across psychology, leading to attempting to disentangle the cognitive mechanisms behind it. A potential predictive factor of performance in an EED is intolerance of uncertainty (IU), an individual difference factor referring to the extent to which uncertain situations are reported to be aversive. Here, we present the results of a series of exploratory analyses in which we tested the relationship between IU and performance in an EED task. For this, we compiled data from 3 experiments, in which participants received the opportunity to exploit different movements in order to avoid a painful stimulus and approach rewards. For decomposing performance in this task, we used different computational models previously employed in studies on the EED. Then, the parameters of the winning model were correlated with the scores of participants in the IU scale. Correlational and cluster analyses, within both frequentists and Bayesian frameworks, did not provide strong evidence for a relation between EED and IU, apart from the decay rate and the subscale "tendency to become paralyzed in the face of uncertainty". Given the theoretical relation between EED and IU, we propose research with different experimental paradigms.