对人类恐惧条件反射和消退的心理生理测量当前数据分析策略的批判性评估。
Critical evaluation of current data analysis strategies for psychophysiological measures of fear conditioning and extinction in humans.
机构信息
School of Psychology, University of Tasmania, Australia.
School of Psychology, University of Tasmania, Australia.
出版信息
Int J Psychophysiol. 2018 Dec;134:95-107. doi: 10.1016/j.ijpsycho.2018.10.010. Epub 2018 Oct 26.
Fear conditioning and extinction is a construct integral to understanding trauma-, stress- and anxiety-related disorders. In the laboratory, associative learning paradigms that pair aversive with neutral stimuli are used as analogues to real-life fear learning. These studies use physiological indices, such as skin conductance, to sensitively measure rates and intensity of learning and extinction. In this review, we discuss some of the potential limitations in interpreting and analysing physiological data during the acquisition or extinction of conditioned fear. We argue that the utmost attention should be paid to the development of modelling approaches of physiological data in associative learning paradigms, by illustrating the lack of replicability and interpretability of results in current methods. We also show that statistical significance may be easily achieved in this paradigm without more stringent data and data analysis reporting requirements, leaving this particular field vulnerable to misleading conclusions. This review is written so that issues and potential solutions are accessible to researchers without mathematical training. We conclude the review with some suggestions that all laboratories should be able to implement, including visualising the full data set in publications and adopting modelling, or at least regression-based, approaches.
恐惧条件反射和消退是理解创伤、应激和焦虑相关障碍的一个重要概念。在实验室中,将厌恶刺激与中性刺激配对的联想学习范式被用作现实生活中恐惧学习的模拟。这些研究使用生理指标,如皮肤电导率,来敏感地测量学习和消退的速度和强度。在这篇综述中,我们讨论了在条件性恐惧的获得或消退过程中解释和分析生理数据时可能存在的一些局限性。我们认为,应该非常关注在联想学习范式中对生理数据建模方法的开发,通过说明当前方法中结果的可重复性和可解释性缺乏。我们还表明,在这个范式中,很容易达到统计学意义,而不需要更严格的数据和数据分析报告要求,这使得这个特定领域容易得出误导性的结论。这篇综述的写作目的是让没有数学训练的研究人员能够理解其中的问题和潜在的解决方案。我们在综述的最后提出了一些建议,所有实验室都应该能够实施这些建议,包括在出版物中可视化完整数据集,并采用建模,或者至少是基于回归的方法。