Sensitivity to Pain Expectations: A Bayesian Model of Individual Differences
Sensitivity to Pain Expectations: A Bayesian Model of Individual Differences
复制标题
对疼痛预期的敏感性：个体差异的贝叶斯模型
DOI：
10.1101/176172
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Hoskin R
中科院分区：
文献类型：
--
作者：
Hoskin R
众所周知，人们对疼痛的想法和感觉(称为“疼痛预期”)会改变人们对疼痛的感知。然而，人们对支撑疼痛预期的认知过程知之甚少，也不知道是什么推动了疼痛预期在个体之间产生不同的影响。本文详细介绍了疼痛感知模型的测试，该模型以贝叶斯先验到后验的更新过程来形式化对疼痛的反应。使用从短而无欺骗性的预测线索任务中获得的数据，我们发现这种贝叶斯模型比其他更简单的模型更好地预测疼痛的评级。在群体层面上，研究结果证实了预测编码的两个核心预测：预期改变感知，预期不确定性的增加降低了预期对感知的影响。增加了与疼痛预期中的特征差异相关的参数，提高了模型的拟合度，这表明这些特征在感知上发挥着重要作用，超过了预测性线索引发的预期的影响。当允许模型参数因参与者而异时，模型的拟合度进一步提高。这个最终的模型描述了每个人对疼痛预期的敏感性。这个模型与理解疼痛预期的认知基础有关，并可能成为指导患者分层和临床实验的有用工具。
The thoughts and feelings people have about pain (referred to as ‘pain expectations’) are known to alter the perception of pain. However little is known about the cognitive processes that underpin pain expectations, or what drives the differing effect that pain expectations have between individuals. This paper details the testing of a model of pain perception which formalises the response to pain in terms of a Bayesian prior-to-posterior updating process. Using data acquired from a short and deception-free predictive cue task, it was found that this Bayesian model predicted ratings of pain better than other, simpler models. At the group level, the results confirmed two core predictions of predictive coding; that expectation alters perception, and that increased uncertainty in the expectation reduces its impact on perception. The addition of parameters relating to trait differences in pain expectation improved the fit of the model, suggesting that such traits play a significant role in perception above and beyond the influence of expectations triggered by predictive cues. When the model parameters were allowed to vary by participant, the model’s fit improved further. This final model produced a characterisation of each individual's sensitivity to pain expectations. This model is relevant for the understanding of the cognitive basis of pain expectations and could potentially act as a useful tool for guiding patient stratification and clinical experimentation.