De Weerd P, Vandenbussche E, Orban G A
Laboratorium voor Neuro- en Psychofysiologie, Katholieke Universiteit te Leuven, Belgium.
Vision Res. 1992 Feb;32(2):305-22. doi: 10.1016/0042-6989(92)90141-5.
We have investigated how different texture parameters affect texture segregation in the cat, and which strategies cats use to solve the segregation task. Five cats were presented with stimuli consisting of two adjacent panels. One side contained a square area of a particular texture embedded in a different background texture; the other side was filled with only the background texture. The animal's task was to detect at which side the texture difference was presented. Sensitivity for the texture difference was assessed by making one aspect of the texture (in most instances the size of the texture elements) dependent upon performance by means of a staircase procedure. Among the most prominent parametric effects are those of density and element position randomization. In general, segregation was optimal at intermediate densities and deteriorated at larger and smaller densities. Element position randomization caused a slight but systematic decrease in segregation performance. Furthermore, we found texture elements at the border between different textures to be of primary importance for segregation. Which strategy the animals used for solving the segregation task depended upon the presence of random figure/background reversals in subsequent stimulus presentations during training. The animals learned to detect texture differences if these reversals were present, and without reversals, they learned to identify the particular texture in the target square. Interestingly, parameter dependencies of segregation did not depend upon the detection strategy used. We have speculated that the two different strategies used by the cats to solve the segregation tasks are related to different hierarchical levels of texture segregation which can be traced back to different stages of texture processing in human models of segregation performance.
我们研究了不同的纹理参数如何影响猫的纹理分离,以及猫使用哪些策略来解决分离任务。给五只猫呈现由两个相邻面板组成的刺激。一侧包含嵌入不同背景纹理中的特定纹理的方形区域;另一侧仅填充背景纹理。动物的任务是检测纹理差异出现在哪一侧。通过使用阶梯程序使纹理的一个方面(在大多数情况下是纹理元素的大小)取决于表现来评估对纹理差异的敏感性。最显著的参数效应是密度和元素位置随机化的效应。一般来说,在中等密度下分离最佳,而在较大和较小密度下分离会变差。元素位置随机化导致分离性能略有但系统性的下降。此外,我们发现不同纹理之间边界处的纹理元素对分离至关重要。动物用于解决分离任务的策略取决于训练期间后续刺激呈现中是否存在随机的图形/背景反转。如果存在这些反转,动物学会检测纹理差异,而没有反转时,它们学会识别目标方形中的特定纹理。有趣的是,分离的参数依赖性并不取决于所使用的检测策略。我们推测,猫用于解决分离任务的两种不同策略与纹理分离的不同层次水平有关,这可以追溯到人类分离性能模型中纹理处理的不同阶段。