Pfefferle Dana, Talbot Steven R, Kahnau Pia, Cassidy Lauren C, Brockhausen Ralf R, Jaap Anne, Deikun Veronika, Yurt Pinar, Gail Alexander, Treue Stefan, Lewejohann Lars
Welfare and Cognition Group, Cognitive Neuroscience Laboratory, German Primate Center-Leibniz Institute for Primate Research, Kellnerweg 4, 37077, Göttingen, Germany.
Leibniz-Science Campus Primate Cognition, German Primate Center & University of Göttingen, Göttingen, Germany.
Behav Res Methods. 2025 Jun 6;57(7):193. doi: 10.3758/s13428-025-02668-5.
Preference tests help to determine how highly individuals value different options to choose from. During preference testing, two or more options are presented simultaneously, and options are ranked based on the choices made. Presented options, however, influence each other, where the amount of influence increases with the number of options. Multiple binary choice tests can reduce this degree of influence, but conventional analysis methods do not reveal the relative strengths of preference, i.e., the preference difference between options. Here, we demonstrate that multiple binary comparisons can be used not only to rank but also to scale preferences among many options (i.e., their worth value). We analyzed human image preference data with known valence scores to develop and validate our approach to determine how known valence ranges (high vs. low) converge on a scaled representation of preference data. Our approach allowed us to assess the valence of ranked options in mice and rhesus macaques. By conducting simulations, we developed an approach to incorporate additional option choices into existing rank orders without the need to conduct binary choice tests with all original options, thus reducing the number of animal experiments needed. Two quality measures, consensus error and intransitivity ratio, allow for assessing the achieved confidence of the scaled ranking and better tailoring of measurements required to improve it further. The software is available as an R package ("simsalRbim"). Our approach optimizes preference testing, e.g., in welfare assessment, and allows us to efficiently and quantitatively assess the relative value of options presented to animals.
偏好测试有助于确定个体对不同可供选择的选项的重视程度。在偏好测试期间，会同时呈现两个或更多选项，并根据所做的选择对选项进行排序。然而，所呈现的选项会相互影响，影响程度会随着选项数量的增加而增大。多项二元选择测试可以降低这种影响程度，但传统的分析方法无法揭示偏好的相对强度，即选项之间的偏好差异。在此，我们证明多项二元比较不仅可用于排序，还可用于衡量多个选项之间的偏好程度（即它们的价值）。我们分析了具有已知效价分数的人类图像偏好数据，以开发和验证我们的方法，来确定已知效价范围（高与低）如何在偏好数据的量表表示上趋同。我们的方法使我们能够评估小鼠和恒河猴中排序选项的效价。通过进行模拟，我们开发了一种方法，可将额外的选项选择纳入现有的排序中，而无需对所有原始选项进行二元选择测试，从而减少所需的动物实验数量。两种质量度量，即一致性误差和非传递性比率，可用于评估量表排序所达到的置信度，并更好地调整进一步改进所需的测量。该软件可作为一个R包（“simsalRbim”）获取。我们的方法优化了偏好测试，例如在福利评估中，并使我们能够高效且定量地评估呈现给动物的选项的相对价值。
