Impaired Adaptive Learning in Chronic Pain Contributes to Apathy

  1. Xinyuan Yan
  2. Crina M. Peterson
  3. Lisa M. Schmidt
  4. Seth Koenig
  5. Donald R. Nixdorf
  6. Alexander Herman
  7. David P. Darrow

  • Curated by eLife

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    eLife Assessment

    This study provides a useful application of computational modelling to examine how people with chronic pain learn under uncertainty, contributing to efforts to link pain with motivational processes. However, the evidence supporting the main claims is incomplete, as the modelling differences are not reflected in observable behaviour or pain measures, and the interpretation extends beyond what the data can substantiate. The conclusions would benefit from a more convincing explanation of what the behavioural difference is that drives the computational findings.

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Abstract

The cognitive mechanisms linking chronic pain to motivational symptoms remain poorly understood. We demonstrate that individuals with chronic temporomandibular disorder (TMD), a common cause of chronic pain, exhibit a specific deficit in adaptive learning in uncertain environments, characterized by failure to reduce uncertainty over time and maintain efficient learning rates. Using a probabilistic reward task, we pioneered the application of a novel volatile Kalman filter to model behavior in 26 TMD participants and 39 matched controls, uniquely tracking trial-wise updates in uncertainty, volatility, and learning rate. Although surface-level performance did not differ across groups, model-based analysis revealed that those with TMD failed to reduce uncertainty and adapt their learning over time. TMD participants also reported significantly greater apathy, depression, and pain catastrophizing, as well as lower health-related quality of life. Mediation analysis confirmed that impaired uncertainty adaptation partially mediated the relationship specifically between TMD and apathy. These findings identify a computational signature of disrupted uncertainty adaptation in people with TMD and provide evidence for a mechanistic link between chronic pain and motivational dysfunction. This work lays a foundation for future studies examining how belief-updating deficits contribute to broader affective and cognitive symptoms in chronic pain.

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  1. eLife

    eLife Assessment

    This study provides a useful application of computational modelling to examine how people with chronic pain learn under uncertainty, contributing to efforts to link pain with motivational processes. However, the evidence supporting the main claims is incomplete, as the modelling differences are not reflected in observable behaviour or pain measures, and the interpretation extends beyond what the data can substantiate. The conclusions would benefit from a more convincing explanation of what the behavioural difference is that drives the computational findings.

  2. eLife

    Reviewer #1 (Public review):

    Summary:

    This study investigates how individuals with chronic temporomandibular disorder (TMD) learn from uncertain rewards, using a probabilistic three-armed bandit task and computational modelling. The authors aim to identify whether people living with chronic pain show altered learning under uncertainty and how such differences might relate to psychological symptoms.

    Strengths:

    The work addresses an important question about how chronic pain may influence cognition and motivation. The task design is appropriate for probing adaptive learning, and the modelling approach is novel. The findings of altered uncertainty updating in the TMD group are interesting.

    Weaknesses:

    Several aspects of the paper limit the strength of the conclusions. The group differences appear only in model-derived parameters, with no corresponding behavioural differences in task performance. Model parameters do not correlate with pain severity, making the proposed mechanistic link between pain and learning speculative. Some of the interpretations extend beyond what the data can directly support.

  3. eLife

    Reviewer #2 (Public review):

    Summary:

    In this paper, the authors report on a case-control study in which participants with chronic pain (TMD) were compared to controls on performance of a three-option learning task. The authors find no difference in task behavior, but fit a model to this behavior and suggest that differences in the model-derived metrics (specifically, change in learning rate/estimated volatility/model estimated uncertainty) reveal a relevant between-group effect. They report a mediation effect suggesting that group differences on self-report apathy may be partially mediated by this uncertainty adaptation result.

    Strengths:

    The role of sensitivity to uncertainty in pathological states is an interesting question and is the focus of a reasonable amount of research at present. This paper provides a useful assessment of these processes in people with chronic pain.

    Weaknesses:

    (1) The interpretation of the model in the absence of any apparent behavioral effect is not convincing. The model is quite complex with a number of free parameters (what these parameters are is not well explained in the methods, although they seem to be presented in the supplement). These parameters are fitted to participant choice behavior - that is, they explain some sort of group difference in this choice behavior. The authors haven't been able to demonstrate what this difference is. The graphs of learning rate per group (Figure 2) suggest that the control group has a higher initial learning rate and a lower later learning rate. If this were actually the case, you would expect to see it reflected in the choice data (the control group should show higher lose-shift behavior earlier on, with this then declining over time, and the TMD group should show no change). This behavior is not apparent. The absence of a clear effect on behavior suggests that the model results are more likely to be spurious.

    (2) As far as I could see, the actual parameters of the model are not reported. The results (Figure 2) illustrate the trial-level model estimated uncertainty/learning rate, etc, but these differ because the fitted model parameters differ. The graphs look like there are substantial differences in v0 (which was not well recovered), but presumably lambda, at least, also differs. The mean(SD) group values for these parameters should be reported, as should the correlations between them (it looks very much like they will be correlated).

    (3) The task used seems ill-suited to measuring the reported process. The authors report the performance of a restless bandit task and find an effect on uncertainty adaptation. The task does not manipulate uncertainty (there are no periods of high/low uncertainty) and so the only adaptation that occurs in the task is the change from what appears to be the participants' prior beliefs about uncertainty (which appear to be very different between groups - i.e. the lines in Figure 2a,b,c are very different at trial 0). If the authors are interested in measuring adaptation to uncertainty, it would clearly be more useful to present participants with periods of higher or lower uncertainty.

    (4) The main factor driving the better fit of the authors' preferred model over listed alternatives seems to be the inclusion of an additive uncertainty term in the softmax-this differentiates the chosen model from the other two Kalman filter-based models that perform less well. But a similar term is not included in the RW models-given the uncertainty of a binary outcome can be estimated as p(1-p), and the RW models are estimating p, this would seem relatively straightforward to do. It would be useful to know if the factor that actually drives better model fit is indeed in the decision stage (rather than the learning stage).

  4. eLife

    Reviewer #3 (Public review):

    This paper applies a computational model to behavior in a probabilistic operant reward learning task (a 3-armed bandit) to uncover differences between individuals with temporomandibular disorder (TMD) compared with healthy controls. Integrating computational principles and models into pain research is an important direction, and the findings here suggest that TMD is associated with subtle changes in how uncertainty is represented over time as individuals learn to make choices that maximize reward. There are a number of strengths, including the comparison of a volatile Kalman filter (vKF) model to some standard base models (Rescorla Wagner with 1 or 2 learning rates) and parameter recovery analyses suggesting that the combination of task and vKF model may be able to capture some properties of learning and decision-making under uncertainty that may be altered in those suffering from chronic pain-related conditions.

    I've focused my comments in four areas: (1) Questions about the patient population, (2) Questions about what the findings here mean in terms of underlying cognitive/motivational processes, (3) Questions about the broader implications for understanding individuals with TMD and other chronic pain-related disorders, and (4) Technical questions about the models and results.

    (1) Patient population

    This is a computational modelling study, so it is light on characterization of the population, but the patient characteristics could matter. The paper suggests they were hospitalized, but this is not a condition that requires hospitalization per se. It would be helpful to connect and compare the patient characteristics with large-scale studies of TMD, such as the OPPERA study led by Maixner, Fillingim, and Slade.

    (2) What cognitive/motivational processes are altered in TMD

    The study finds a pattern of alterations in TMD patients that seems clear in Figure 2. Healthy controls (HC) start the task with high estimates of volatility, uncertainty, and learning rate, which drop over the course of the task session. This is consistent with a learner that is initially uncertain about the structure of the environment (i.e., which options are rewarded and how the contingencies change over time) but learns that there is a fixed or slowly changing mean and stationary variance. The TMD patients start off with much lower volatility, uncertainty, and learning rate - which are actually all near 0 - and they remain stable over the course of learning. This is consistent with a learner who believes they know the structure of the environment and ignores new information.

    What is surprising is that this pattern of changes over time was found in spite of null group differences in a number of aspects of performance: (1) stay rate, (2) switch rate, (3) win-stay/lose-switch behaviors, (4) overall performance (corrected for chance level), (5) response times, (6) autocorrelation, (7) correlations between participants' choice probability and each option's average reward rate, (7) choice consistency (though how operationalized is not described?), (8) win-stay-lose-shift patterns over time. I'm curious about how the patterns in Figure 2 would emerge if standard aspects of performance are essentially similar across groups (though the study cannot provide evidence in favor of the null). It will be important to replicate these patterns in larger, independent samples with preregistered analyses.

    The authors believe that this pattern of findings reveals that TMD patients "maintain a chronically heightened sensitivity to environmental changes" and relate the findings to predictive processing, a hallmark of which (in its simplest form) is precision-weighted updating of priors. They also state that the findings are not related to reduced overall attentiveness or failure to understand the task, but describe them as deficits or impairments in calibrating uncertainty.

    The pattern of differences could, in fact, result from differences in prior beliefs, conceptualization of the task, or learning. Unpacking these will be important steps for future work, along with direct measures of priors, cognitive processes during learning, and precision-weighted updating.

    (3) Implications for understanding chronic pain

    If the findings and conclusions of the paper are correct, individuals with TMD and perhaps other pain-related disorders may have fundamental alterations in the ways in which they make decisions about even simple monetary rewards. The broader questions for the field concern (1) how generalizable such alterations are across tasks, (2) how generalizable they are across patient groups and, conversely, how specific they are to TMD or chronic pain, (3) whether they are the result of neurological dysfunction, as opposed to (e.g.) adaptive strategies or assumptions about the environment/task structure.

    It will be important to understand which features of patients' and/or controls' cognition are driving the changes. For example, could the performance differences observed here be attributable to a reduced or altered understanding of the task instructions, more uncertainty about the rules of the game, different assumptions about environments (i.e., that they are more volatile/uncertain or less so), or reduced attention or interest in optimizing performance? Are the controls OVERconfident in their understanding of the environment?

    This set of questions will not be easy to answer and will be the work of many groups for many years to come. It is a judgment call how far any one paper must go to address them, but my view is that it is a collaborative effort. Start with a finding, replicate it across labs, take the replicable phenomena and work to unpack the underlying questions. The field must determine whether it is this particular task with this model that produces case-control differences (and why), or whether the findings generalize broadly. Would we see the same findings for monetary losses, sounds, and social rewards? Tasks with painful stimuli instead of rewards?

    Another set of questions concerns the space of computational models tested, and whether their parameters are identifiable. An alteration in estimated volatility or learning rate, for example, can come from multiple sources. In one model, it might appear as a learning rate change and in another as a confirmation bias. It would be interesting in this regard to compare the "mechanisms" (parameters) of other models used in pain neuroscience, e.g., models by Seymour, Mancini, Jepma, Petzschner, Smith, Chen, and others (just to name a few).

    One immediate next step here could be to formally compare the performance of both patients and controls to normatively optimal models of performance (e.g., Bayes optimal models under different assumptions). This could also help us understand whether the differences in patients reflect deficits and what further experiments we would need to pin that down.
    In addition, the volatility parameter in the computational model correlated with apathy. This is interesting. Is there a way to distinguish apathy as a particular clinical characteristic and feature of TMD from apathy in the sense of general disinterest in optimal performance that may characterize many groups?

    If we know this, what actionable steps does it lead us to take? Could we take steps to reduce apathy and thus help TMD patients better calibrate to environmental uncertainty in their lives? Or take steps to recalibrate uncertainty (i.e., increase uncertainty adaptation), with benefits on apathy? A hallmark of a finding that the field can build off of is the questions it raises.

    (4) Technical questions about the models and results

    Clarification of some technical points would help interpret the paper and findings further:

    (a) Was the reward probability truly random? Was the random walk different for each person, or constrained?

    (b) When were self-report measures administered, and how?

    (c) Pain assessments: What types of pain? Was a body map assessed? Widespreadness? Pain at the time of the test, or pain in general?

    (d) Parameter recovery: As you point out, r = 0.47 seems very low for recovery of the true quantity, but this depends on noise levels and on how the parameter space is sampled. Is this noise-free recovery, and is it robust to noise? Are the examples of true parameters drawn from the space of participants, or do they otherwise systematically sample the space of true parameters?

    (e) What are the covariances across parameter estimates and resultant confusability of parameter estimates (e.g., confusion matrix)?

    (f) It would be helpful to have a direct statistical comparison of controls and TMD on model parameter estimates.

    (g) Null statistical findings on differences in correlations should not be interpreted as a lack of a true effect. Bayes Factors could help, but an analysis of them will show that hundreds of people are needed before it is possible to say there are no differences with reasonable certainty. Some journals enforce rules around the kinds of language used to describe null statistical findings, and I think it would be helpful to adopt them more broadly.

    (h) What is normatively optimal in this task? Are TMD patients less so, or not? The paper states "aberrant precision (uncertainty) weighting and misestimation of environmental volatility". But: are they misestimates?

    (i) It's not clear how well the choice of prior variance for all parameters (6.25) is informed by previous research, as sensible values may be task- and context-dependent. Are the main findings robust to how priors are specified in the HBI model?

  5. Version published to 10.1101/2025.10.20.25336624 on medRxiv