DiMattina Christopher
Computational Perception Laboratory, Florida Gulf Coast University, Fort Myers, FL, USA 33965-6565.
Department of Psychology, Florida Gulf Coast University, Fort Myers, FL, USA 33965-6565.
bioRxiv. 2023 Jul 11:2023.07.10.548431. doi: 10.1101/2023.07.10.548431.
Previous studies have demonstrated that density is an important perceptual aspect of textural appearance to which the visual system is highly attuned. Furthermore, it is known that density cues not only influence texture segmentation, but can enable segmentation by themselves, in the absence of other cues. A popular computational model of texture segmentation known as the "Filter-Rectify-Filter" (FRF) model predicts that density should be a second-order cue enabling segmentation. For a compound texture boundary defined by superimposing two single-micropattern density boundaries, a version of the FRF model in which different micropattern-specific channels are analyzed separately by different second-stage filters makes the prediction that segmentation thresholds should be identical in two cases: (1) Compound boundaries with an equal number of micropatterns on each side but different relative proportions of each variety () and (2) Compound boundaries with different numbers of micropatterns on each side, but with each side having an identical number of each variety (). We directly tested this prediction by comparing segmentation thresholds for second-order compound feature and density boundaries, comprised of two superimposed single-micropattern density boundaries comprised of complementary micropattern pairs differing either in orientation or contrast polarity. In both cases, we observed lower segmentation thresholds for compound density boundaries than compound feature boundaries, with identical results when the compound density boundaries were equated for RMS contrast. In a second experiment, we considered how two varieties of micropatterns summate for compound boundary segmentation. In the case where two single micro-pattern density boundaries are superimposed to form a compound density boundary, we find that the two channels combine via probability summation. By contrast, when they are superimposed to form a compound feature boundary, segmentation performance is worse than for either channel alone. From these findings, we conclude that density segmentation may rely on neural mechanisms different from those which underlie feature segmentation, consistent with recent findings suggesting that density comprises a separate psychophysical 'channel'.
先前的研究表明，密度是纹理外观的一个重要感知方面，视觉系统对其高度敏感。此外，众所周知，密度线索不仅会影响纹理分割，而且在没有其他线索的情况下，自身就能实现分割。一种流行的纹理分割计算模型，即“滤波-整流-滤波”（FRF）模型预测，密度应该是一种能够实现分割的二阶线索。对于通过叠加两个单微模式密度边界定义的复合纹理边界，FRF模型的一个版本（其中不同的微模式特定通道由不同的第二阶段滤波器分别分析）做出如下预测：在两种情况下分割阈值应该相同：（1）两侧微模式数量相等但各品种相对比例不同的复合边界（），以及（2）两侧微模式数量不同但每侧各品种数量相同的复合边界（）。我们通过比较二阶复合特征边界和密度边界的分割阈值，直接检验了这一预测，二阶复合特征边界和密度边界由两个叠加的单微模式密度边界组成，这些单微模式密度边界由在方向或对比度极性上不同的互补微模式对组成。在这两种情况下，我们都观察到复合密度边界的分割阈值低于复合特征边界，当复合密度边界的均方根对比度相等时，结果相同。在第二个实验中，我们考虑了两种微模式如何对复合边界分割进行求和。在两个单微模式密度边界叠加形成复合密度边界的情况下，我们发现两个通道通过概率求和进行组合。相比之下，当它们叠加形成复合特征边界时，分割性能比单独的任何一个通道都要差。从这些发现中，我们得出结论，密度分割可能依赖于与特征分割不同的神经机制，这与最近的研究结果一致，即密度构成一个独立的心理物理“通道”。
