标题： 探索基于偏心率的专用深度神经网络模型以模拟视觉注意 显示英文标题

标题： Exploring a Specialized Eccentricity-Based Deep Neural Network Model to Simulate Visual Attention

摘要： 虽然视觉注意理论众多，但神经发育研究仍受限于婴儿不可靠的反应和有限的注意力持续时间。 通过与Project Prakash的合作，我们接触到一个独特的群体：在生命后期获得视力的患者。 这一队列使我们能够对认知成熟且合作的参与者进行视觉过程发展的研究，而不是婴儿。 我们从术前患者、术后患者（纵向跟踪1、3、6和12个月）以及佩戴模糊护目镜的神经正常对照组收集数据，这些护目镜的清晰度与患者的术后视力相匹配。 所有参与者都执行了一个修改后的前注意突出视觉搜索任务。 我们实施了eccNET CNN模型（Gupta等，2021），以模拟视觉搜索不对称性，并将其置于与人类参与者相同的任务中。 反应时间比较揭示了人类和模型表现之间的收敛和发散模式。 这些发现使我们能够根据人类生理约束和患者群体中观察到的发展轨迹进行系统的模型消融。 关键的是，人类与模型之间的差异最为有用，将我们的重点引向需要进行特定架构修改的方面，以更好地近似人类视觉发展模式。 显示英文摘要

摘要： While visual attention theories abound, neurodevelopmental research remains constrained by infants' unreliable responses and limited attention spans. Through collaboration with Project Prakash, we accessed a unique population: patients gaining vision later in life. This cohort enables investigation of visual process development in cognitively mature, cooperative participants rather than infants. We collected data from pre-operation patients, post-operation patients tracked longitudinally (1, 3, 6, and 12 months), and neurotypical controls wearing blurred goggles matched to patients' post-surgical acuity. All participants performed a modified pre-attentive pop-out visual search task. We implemented the eccNET CNN model (Gupta et al., 2021) to simulate visual search asymmetry, subjecting it to identical tasks as human participants. Reaction time comparisons revealed both convergent and divergent patterns between human and model performance. These findings enabled systematic model ablations informed by human physiological constraints and developmental trajectories observed across patient groups. Critically, human-model divergences proved most informative, directing our focus toward specific architectural modifications needed to better approximate human visual development patterns.