Brand Katja, Wise Toby, Hess Alexander J, Russell Bruce R, Stephan Klaas E, Harrison Olivia K
Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
Department of Psychology, University of Otago, Dunedin, New Zealand.
Front Psychol. 2024 Apr 5;15:1254564. doi: 10.3389/fpsyg.2024.1254564. eCollection 2024.
Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty.
Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures.
Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty.
These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.
内感受，即对身体内部状态的感知，已被证明与情绪和心理健康密切相关。特别值得关注的是内感受学习过程，它捕捉环境线索与身体信号之间的关联，以此作为对未来进行与内稳态相关预测的基础。一种测量呼吸内感受学习且已显示出有前景结果的方法是呼吸学习任务（BLT）。虽然最初的BLT要求对即将到来的吸气阻力是否存在进行二元预测，但在此我们扩展了这一范式以获取预测（不）确定性的连续测量值。
16名健康参与者完成了BLT的连续版本，要求他们在从0.0到10.0的连续尺度上预测呼吸阻力的可能性。为了解释参与者的反应，将联想学习的雷斯克拉 - 瓦格纳模型分别与适用于连续或二元预测的合适观察模型相结合。为了进行验证，我们将这两个模型与相应的零模型进行比较，并检查观察到的预测与建模预测之间的相关性。该模型还被进一步扩展以测试学习率是否因刺激效价而异。最后，针对内感受和心理健康问卷测量值，考虑了预测确定性的汇总测量值以及学习率的模型估计值。
我们的结果表明，连续模型拟合紧密地利用经验数据捕捉了参与者的行为，并且与先前收集的数据相比，二值化预测显示出出色的可重复性。然而，模型扩展表明，负性（即呼吸阻力）和正性（即无呼吸阻力）刺激的学习率之间没有显著差异。最后，发现疲劳严重程度与预测确定性和学习率之间以及焦虑敏感性与预测确定性之间存在显著相关性。
这些结果证明了在感知学习任务中收集丰富的连续预测数据的效用，并表明更新后的BLT是未来对内感受学习及其与心理健康潜在联系进行研究的有前景的范式。
