Task-dependent contribution to edge-based versus region-based texture perception

Citation

Gheorghiu E, Diggiss C & Kingdom F (2024) Task-dependent contribution to edge-based versus region-based texture perception. Scientific Reports, 14, Art. No.: 17953. https://doi.org/10.1038/s41598-024-68976-6

Abstract
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 (OM), orientation variance modulation (OVM), luminance modulation (LM), contrast modulation (CM) 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.

Keywords
Texture; Edge; Region; Segmentation; Contrast; Orientation; Luminance

Notes
All data are available online in DataSTORRE at: http://hdl.handle.net/11667/231.

Journal
Scientific Reports: Volume 14