Gheorghiu Elena, Diggiss Cassandra, Kingdom Frederick A A
Department of Psychology, University of Stirling, Stirling, FK9 4LA, Scotland, UK.
McGill Vision Research, Department of Ophthalmology and Visual Sciences, McGill University, Montreal, Canada.
Sci Rep. 2024 Aug 2;14(1):17953. doi: 10.1038/s41598-024-68976-6.
Texture segregation studies indicate that some types of textures are processed by edge-based and others by region-based mechanisms. However, studies employing nominally edge-based textures have found evidence for region-based processing mechanisms when the task was to detect rather than segregate the textures. Here we investigate directly whether the nature of the task determines if region-based or edge-based mechanisms are involved in texture perception. Stimuli consisted of randomly positioned Gabor micropattern texture arrays with five types of modulation: orientation modulation, orientation variance modulation, luminance modulation, contrast modulation and contrast variance modulation (CVM). There were four modulation frequencies: 0.1, 0.2, 0.4 and 0.8 cpd. Each modulation type was defined by three types of waveforms: sinewave (SN) with its smooth variations, square-wave (SQ) and cusp-wave (CS) with its sharp texture edges. The CS waveform was constructed by removing a sinewave from an equal amplitude square-wave. Participants performed two tasks: detection in which participants selected which of two stimuli contained the modulation and discrimination in which participants indicated which of two textures had a different modulation orientation. Our results indicate that threshold amplitudes in the detection task followed the ordering SQ < SN < CS across all spatial frequencies, consistent with detection being mediated by the overall energy in the stimulus and hence region based. With the discrimination task at low texture spatial frequencies and with CVM textures at all spatial frequencies the order was CS ≤ SQ with both < SN, consistent with being edge-based. We modeled the data by estimating the spatial frequency of a Difference of Gaussian filter that gave the largest peak amplitude response to the data. We found that the peak amplitude was lower for detection than discrimination across all texture types except for the CVM texture. We conclude that task requirements are critical to whether edges or regions underpin texture processing.
纹理分离研究表明,某些类型的纹理是通过基于边缘的机制进行处理的,而其他纹理则通过基于区域的机制进行处理。然而,使用名义上基于边缘的纹理的研究发现,当任务是检测而不是分离纹理时,存在基于区域的处理机制的证据。在这里,我们直接研究任务的性质是否决定了基于区域的机制还是基于边缘的机制参与纹理感知。刺激由随机定位的Gabor微图案纹理阵列组成,具有五种调制类型:方向调制、方向方差调制、亮度调制、对比度调制和对比度方差调制(CVM)。有四个调制频率:0.1、0.2、0.4和0.8周/度。每种调制类型由三种波形定义:具有平滑变化的正弦波(SN)、方波(SQ)和具有尖锐纹理边缘的尖波(CS)。CS波形是通过从等幅方波中去除正弦波而构建的。参与者执行两项任务:检测任务,参与者选择两个刺激中哪个包含调制;辨别任务,参与者指出两个纹理中哪个具有不同的调制方向。我们的结果表明,在所有空间频率上,检测任务中的阈值幅度遵循SQ < SN < CS的顺序,这与检测由刺激中的总能量介导并因此基于区域一致。在低纹理空间频率下的辨别任务以及在所有空间频率下的CVM纹理中,顺序为CS ≤ SQ,两者均 < SN,这与基于边缘一致。我们通过估计对数据给出最大峰值幅度响应的高斯差分滤波器的空间频率对数据进行建模。我们发现,除了CVM纹理外,在所有纹理类型中,检测的峰值幅度都低于辨别。我们得出结论,任务要求对于边缘还是区域支撑纹理处理至关重要。