疼痛状态下认知偏向的重塑与疼痛解码
Reshaping of pain-related cognitive biases during the pain state and their role in pain decoding
陈施豪 1金云馨 1李晓云 1许蛟婧 1彭微微1
作者信息
摘要
既往研究发现慢性疼痛患者存在认知偏向，但疼痛状态如何重塑注意、解释与记忆等认知偏向，以及这些偏向能否反映个体疼痛状态与疼痛强度，仍缺乏研究。本研究结合漂移扩散模型、事件相关电位(ERPs)与机器学习方法，考察疼痛状态下认知偏向的行为与神经机制，并检验其在疼痛解码中的价值。90名健康被试分别接受辣椒素(疼痛组)或对照乳膏(控制组)处理，随后完成注意偏向、模糊面孔解释偏向及记忆偏向任务，同时记录脑电信号。结果发现，疼痛状态主要改变了模糊信息的解释加工，表现为削弱模糊面孔的快乐解释优势，使其在决策模式及ERP神经表征上更接近疼痛解释方向；同时，疼痛状态还改变了对无效威胁线索的解除困难模式，而对疼痛记忆偏向影响有限。跨任务认知偏向及其神经特征具有疼痛解码潜力，其中解释偏向特征主要贡献于疼痛状态分类，而注意与记忆偏向特征更多贡献于疼痛强度估计。因此，认知偏向及其神经特征可能成为疼痛评估的重要补充指标。
Abstract
Previous studies have shown that chronic pain is associated with pain-related cognitive biases. However, how a pain state reshapes attentional, interpretive, and memory biases, and whether these biases can provide informative features for decoding pain states and pain intensity, remain unclear. The present study combined drift-diffusion modeling (DDM), event-related potentials (ERPs), and machine-learning approaches to investigate the behavioral and neural mechanisms of cognitive biases during a pain state, and to examine their value in pain decoding.Ninety healthy participants were randomly assigned to either a pain group or a control group. Sustained experimental pain was induced using topical capsaicin in the pain group, whereas the control group received a matched control cream. Participants completed three cognitive-bias tasks during EEG recording: a modified spatial cueing task assessing attentional bias, an incidental learning task assessing interpretation bias for ambiguous faces, and a recognition task assessing memory bias for pain-related pictures. DDM was applied to characterize latent decision processes underlying behavioral responses. ERP analyses were conducted to examine the temporal dynamics of cognitive-bias processing, including early attentional processing and later evaluative stages. Cross-task DDM and ERP features were further entered into machine-learning models for pain state classification and pain intensity estimation.The results showed that pain state did not uniformly enhance all pain-related cognitive biases. Instead, it selectively altered the interpretation of ambiguous information and the attentional processing of pain-related cues. Specifically, pain state weakened the happy interpretation advantage for ambiguous faces, with altered evidence accumulation, pre-decisional bias, and VPP and LPP neural representations becoming more similar to those associated with pain-related interpretations. Pain state also altered the pattern of disengagement difficulty under invalid threat-cue conditions, suggesting that ongoing pain influenced attentional control when attention was redirected away from pain-related cues. In contrast, pain-related pictures showed a stable recognition advantage over non-pain pictures, but this memory bias was not further amplified by pain state. Machine-learning analyses showed that cross-task cognitive-bias features and their neural signatures showed above-chance discrimination between pain and non-pain states, with the best-performing model achieving an AUC of 0.691. Within the pain group, these features also predicted individual pain intensity, with the best-performing model achieving an out-of-sample R2 of 0.182. Feature attribution analyses indicated that interpretation-bias features contributed more strongly to pain-state classification, whereas attention- and memory-bias features showed greater contributions to pain-intensity estimation.Together, by integrating cross-task cognitive-bias paradigms, ERPs, DDM, and machine-learning analysis, the present study systematically characterized the computational and neural signatures of cognitive biases during a pain state. These findings suggest that pain selectively reshapes different forms of cognitive bias, and that cognitive biases and their neural signatures may provide complementary indicators for individualized pain decoding and assessment beyond traditional subjective pain ratings.关键词
疼痛/认知偏向/事件相关电位/漂移扩散模型/机器学习Key words
pain/cognitive bias/event-related potentials/drift-diffusion model/machine learning引用本文复制引用
陈施豪,金云馨,李晓云,许蛟婧,彭微微.疼痛状态下认知偏向的重塑与疼痛解码[EB/OL].(2026-09-29)[2026-10-03].https://chinaxiv.org/abs/202610.00002.学科分类
基础医学
