Applying Histogram Contrast Analysis to Texture Perception

许多大脑过程在感觉输入上并行操作，以区分(边界、层次、调制等)。在感知空间/时间的结构中。例如，有视觉过程对光强度的时空变化进行操作，以产生亮度差异。其他过程通过光的光谱含量在空间和时间上的变化来构建颜色差异。还有其他过程基于纹理属性的时空变化(例如，纹理对比度、粒度和/或取向)来绘制区别。这些实验将研究的正是这最后一类区分过程或渠道。直方图对比分析是测量理论的一个新分支，其目的是(I)分离单个感知通道，(Ii)精确表征这些通道执行的计算。没有人确切地知道有多少不同的渠道致力于绘制基于纹理的感知区别，尽管人们普遍认为涉及多个渠道。也没有任何数据表明纹理感知中使用的任何一个假定通道都执行了哪些特定计算。到目前为止，纹理感知模型一直依赖于对绘制纹理区别所使用的通道的性质的任意、特别的猜测。这些实验将使用直方图对比分析(I)分离用于绘制纹理区别的单个通道，以及(Ii)确定它们的计算特性。因此，这些实验将从构建一个类似于物理元素元素周期表的计算性“纹理感知元素”表开始。任何足够精确和全面的纹理感知理论都必须以这样一个元素表为基石，才能预测在实际纹理识别任务中的表现概率。

Many brain processes operate in parallel on sensory input to draw distinctions (boundaries, gradations, modulations, etc.) in the fabric of perceptual space/time. For instance, there are visual processes that operate on the spatiotemporal variations in light intensity to generate brightness distinctions. Other processes operate on variations in the spectral content of light over space and time to construct color distinctions. Still other processes draw distinctions based on spatiotemporal variations in textural properties (e.g., texture contrast, granularity and/or orientation). It is this last class of distinction-drawing processes, or channels, that these experiments will investigate. Histogram contrast analysis is a new branch of measurement theory whose purpose is to (i) isolate individual perceptual channels, and (ii) precisely characterize the computations those channels perform. No one knows exactly how many distinct channels are devoted to drawing texture-based perceptual distinctions, though there is general agreement that multiple channels are involved. Nor is there any data to indicate what specific computations are performed by any one of the putative channels used in texture perception. Heretofore, models of texture perception have relied on arbitrary, ad hoc guesses as to the nature of the channels used in drawing textural distinctions. These experiments will use histogram contrast analysis (i) to isolate individual channels used in drawing textural distinctions and (ii) to determine their computational characteristics. These experiments will thus make a start at the task of constructing a table of the computational "elements of texture perception" analogous to the periodic table of physical elements. Any theory of texture perception precise and comprehensive enough to predict performance probabilities on actual texture discrimination tasks must have such a table of elements as its cornerstone.