Profiles of visual perceptual learning in feature space

• Published in: iScience Vol. 27; no. 3; p. 109128
• Main Authors: Shen, Shiqi, Sun, Yueling, Lu, Jiachen, Li, Chu, Chen, Qinglin, Mo, Ce, Fang, Fang, Zhang, Xilin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.03.2024; Elsevier
• Subjects: Cognitive neuroscience; Computer science; Neuroscience; Computer science; Cognitive neuroscience; Neuroscience
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2024.109128

Visual perceptual learning (VPL), experience-induced gains in discriminating visual features, has been studied extensively and intensively for many years, its profile in feature space, however, remains unclear. Here, human subjects were trained to perform either a simple low-level feature (grating orientation) or a complex high-level object (face view) discrimination task over a long-time course. During, immediately after, and one month after training, all results showed that in feature space VPL in grating orientation discrimination was a center-surround profile; VPL in face view discrimination, however, was a monotonic gradient profile. Importantly, these two profiles can be emerged by a deep convolutional neural network with a modified AlexNet consisted of 7 and 12 layers, respectively. Altogether, our study reveals for the first time a feature hierarchy-dependent profile of VPL in feature space, placing a necessary constraint on our understanding of the neural computation of VPL. [Display omitted] •Profiles of VPL in feature space depend on the visual feature hierarchy•VPL in grating orientation discrimination displays a center-surround profile•VPL in face view discrimination displays a monotonic gradient profile Neuroscience; Cognitive neuroscience; Computer science