Pike Alexandra C, Tan Katrina H T, Tromblee Hoda, Wing Michelle, Robinson Oliver J
Department of Psychology, University of York, UK.
Anxiety Lab, Neuroscience and Mental Health Group, Institute of Cognitive Neuroscience, University College London, UK.
Comput Psychiatr. 2024 Dec 18;8(1):217-232. doi: 10.5334/cpsy.92. eCollection 2024.
Affective biases are commonly seen in disorders such as depression and anxiety, where individuals may show attention towards and preferential processing of negative or threatening stimuli. Affective biases have been shown to change with effective intervention: randomized controlled trials into these biases and the mechanisms that underpin them may allow greater understanding of how interventions can be improved and their success be maximized. For such trials to be informative, we must have reliable ways of measuring affective bias over time, so we can detect how and whether they are altered by interventions: the test-retest reliability of our measures puts an upper bound on our ability to detect any changes. In this online study we therefore examined the test-retest reliability of two behavioural affective bias tasks (an 'Ambiguous Midpoint' and a 'Go-Nogo' task). 58 individuals recruited from the general population completed the tasks twice, with at least 14 days in between sessions. We analysed the reliability of both summary statistics and parameters from computational models using Pearson's correlations and intra-class correlations. Standard summary statistic measures from these affective bias tasks had reliabilities ranging from 0.18 (poor) to 0.49 (moderate). Parameters from computational modelling of these tasks were in many cases less reliable than summary statistics. However, embedding the covariance between sessions within the generative modelling framework resulted in higher estimates of stability. We conclude that measures from these affective bias tasks are moderately reliable, but further work to improve the reliability of these tasks would improve still further our ability to draw inferences in randomized trials.
情感偏差常见于抑郁症和焦虑症等疾病中，患者可能会表现出对负面或威胁性刺激的关注和优先处理。研究表明，情感偏差会随着有效干预而改变：针对这些偏差及其潜在机制的随机对照试验，可能会让我们更好地理解如何改进干预措施并使其效果最大化。为了使这类试验具有参考价值，我们必须拥有可靠的方法来长期测量情感偏差，以便能够检测干预措施如何以及是否改变了情感偏差：我们测量方法的重测信度为检测任何变化的能力设定了上限。因此，在这项在线研究中，我们检验了两项行为情感偏差任务（“模糊中点”任务和“Go-Nogo”任务）的重测信度。从普通人群中招募的58名个体完成了这两项任务各两次，两次测试之间至少间隔14天。我们使用皮尔逊相关系数和组内相关系数分析了汇总统计量和计算模型参数的信度。这些情感偏差任务的标准汇总统计量测量方法的信度范围为0.18（较差）至0.49（中等）。这些任务的计算模型参数在许多情况下比汇总统计量的信度更低。然而，在生成模型框架内纳入两次测试之间的协方差，会得出更高的稳定性估计值。我们得出结论，这些情感偏差任务的测量方法具有中等信度，但进一步提高这些任务信度的工作将进一步提升我们在随机试验中进行推断的能力。
