论文标题
改善最大似然差缩放方法来测量内容间尺度
Improving Maximum Likelihood Difference Scaling method to measure inter content scale
论文作者
论文摘要
大多数主观研究的目的是将一组刺激放在感知量表上。这主要是通过评级直接完成的,例如使用单个或双刺激方法,或通过排名或成对比较间接使用。所有这些方法估计刺激的感知大小。但是,诸如最大似然差缩放(MLD)之类的程序表明,考虑感知距离可以在歧视性,观察者的认知负载和所需的试验数方面带来好处。 MLDS方法的缺点之一是,从不同源内容创建的刺激获得的感知量表通常不可比拟。在本文中,我们提出了MLDS方法的扩展,该方法可确保结果的间距可比性,并显示其有用性,尤其是在存在观察者误差的情况下。
The goal of most subjective studies is to place a set of stimuli on a perceptual scale. This is mostly done directly by rating, e.g. using single or double stimulus methodologies, or indirectly by ranking or pairwise comparison. All these methods estimate the perceptual magnitudes of the stimuli on a scale. However, procedures such as Maximum Likelihood Difference Scaling (MLDS) have shown that considering perceptual distances can bring benefits in terms of discriminatory power, observers' cognitive load, and the number of trials required. One of the disadvantages of the MLDS method is that the perceptual scales obtained for stimuli created from different source content are generally not comparable. In this paper, we propose an extension of the MLDS method that ensures inter-content comparability of the results and shows its usefulness especially in the presence of observer errors.