Witte Kristin, Thalmann Mirko, Schulz Eric
Center for Computational Health, Helmholtz Munich, 85764, Neuherberg, Germany.
Sci Rep. 2025 Jul 28;15(1):27479. doi: 10.1038/s41598-025-09152-2.
An increasing number of studies have used multi-armed bandit tasks to investigate individual differences in exploration behavior. However, the psychometric properties of exploration measures remain unexplored. We examine the test-retest reliability, convergent, divergent, and external validity of model-based estimates of exploration strategies using three canonical paradigms. Our results revealed poor to moderate reliability, with minimal correlations for the same strategy across tasks. We then provide actionable recommendations for how to improve reliability and convergence across tasks: Simplifying common computational models enabled us to identify two convergently valid latent factors representing value-guided and directed exploration. Still, these factors showed neither a significant correlation with self-reported exploration tendencies nor with mood fluctuations, symptoms of anxiety, and depression. The exploration factors were, however, highly correlated with working memory capacity, questioning whether they provide additional information beyond performance-related constructs. To improve future research, we suggest simplifying common computational models and using multiple tasks to more accurately measure exploration strategies and mitigate spurious correlations arising from task-specific factors.
越来越多的研究使用多臂老虎机任务来探究探索行为中的个体差异。然而，探索测量的心理测量特性仍未得到探索。我们使用三种典型范式检验了基于模型的探索策略估计的重测信度、聚合效度、区分效度和外部效度。我们的结果显示信度从低到中等，不同任务中相同策略的相关性极小。然后，我们针对如何提高跨任务的信度和聚合性提供了可行的建议：简化常见的计算模型使我们能够识别出两个具有聚合效度的潜在因素，分别代表价值引导探索和定向探索。不过，这些因素与自我报告的探索倾向、情绪波动、焦虑和抑郁症状均无显著相关性。然而，探索因素与工作记忆容量高度相关，这引发了对于它们是否能提供超出与表现相关结构之外的额外信息的质疑。为了改进未来的研究，我们建议简化常见的计算模型，并使用多个任务来更准确地测量探索策略，以及减少由特定任务因素引起的虚假相关性。
