February 24, 2021

An image-computable model of attention and texture segmentation

Attention can facilitate or impair texture segmentation, altering whether objects are isolated from their surroundings in visual scenes. We simultaneously explain several empirical phenomena of texture segmentation and its attentional modulation with a single image-computable model. At the model’s core, segmentation relies on the interaction between sensory processing and attention, with different operating regimes for involuntary and voluntary attention systems. Model comparisons were used to identify computations critical for texture segmentation and attentional modulation. The model reproduced (i) the central performance drop, which is the parafoveal advantage for segmentation over the fovea, (ii) the peripheral improvements and central impairments induced by involuntary attention and (iii) the uniform improvements across eccentricity by voluntary attention. The proposed model reveals distinct functional roles for involuntary and voluntary attention and provides a generalizable quantitative framework for predicting the perceptual impact of attention across the visual field.

 bioRxiv Subject Collection: Neuroscience

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