Wiebel Christiane B, Aguilar Guillermo, Maertens Marianne
Modeling of Cognitive Processes, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.
Modeling of Cognitive Processes, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin and Bernstein Center for Computational Neuroscience, Berlin, Germany.
J Vis. 2017 Apr 1;17(4):1. doi: 10.1167/17.4.1.
One central problem in perception research is to understand how internal experiences are linked to physical variables. Most commonly, this relationship is measured using the method of adjustment, but this has two shortcomings: The perceptual scales that relate physical and perceptual variables are not measured directly, and the method often requires perceptual comparisons between viewing conditions. To overcome these problems, we measured perceptual scales of surface lightness using maximum likelihood difference scaling, asking observers only to compare the lightness of surfaces presented in the same context. Observers were lightness constant, and the perceptual scales qualitatively and quantitatively predicted perceptual matches obtained in a conventional adjustment experiment. Additionally, we show that a contrast-based model of lightness perception predicted 98% of the variance in the scaling and 88% in the matching data. We suggest that the predictive power was higher for scales because they are closer to the true variables of interest.
知觉研究中的一个核心问题是理解内部体验如何与物理变量相联系。最常见的是，这种关系通过调整法来测量，但这有两个缺点：关联物理和知觉变量的知觉量表并非直接测量得到，而且该方法常常需要在不同观察条件下进行知觉比较。为克服这些问题，我们使用最大似然差异量表法测量了表面明度的知觉量表，只要求观察者比较在相同情境中呈现的表面的明度。观察者的明度保持恒定，并且这些知觉量表在定性和定量方面都预测了在传统调整实验中获得的知觉匹配。此外，我们表明，基于对比度的明度知觉模型预测了量表数据中98%的方差以及匹配数据中88%的方差。我们认为，量表的预测能力更高，因为它们更接近真正感兴趣的变量。
