A Model of the Superior Colliculus Predicts Fixation Locations during Scene Viewing and Visual Search
A Model of the Superior Colliculus Predicts Fixation Locations during Scene Viewing and Visual Search
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DOI：
10.1523/jneurosci.0825-16.2016
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发表时间：
2017-02-08
影响因子：
5.3
通讯作者：
Zelinsky, Gregory J.
中科院分区：
文献类型：
--
作者：
Adeli, Hossein;Vitu, Francoise;Zelinsky, Gregory J.
现代注意力计算模型使用显著性图和目标图来预测注视，它们分别基于特征对比和目标目标来确定注视的优先位置。但是，尽管许多这样的模型在生物学上是合理的，但没有一个模型考虑到动眼肌系统的设计约束或参数规范。相反，尽管大多数扫视编程模型都与潜在的神经生理学紧密相关，但没有一个模型是用现实世界的刺激和任务进行测试的。我们在MASC中结合了这两种方法的优势，MASC是上丘（SC）的注意力模型，捕获了已知的扫视编程的神经生理约束。我们发现，MASC预测了人类自由观看自然场景和执行范例和分类搜索任务的注视位置，这是其他现有模型无法实现的广度。此外，它在这方面做得和那些更专业、更先进的竞争对手一样好，甚至更好。MASC的预见性成功源于它包含了SC组织的高层核心原则：中央凹信息的过度表示、大小不变的人口编码、在扭曲的视觉和运动地图上的级联人口平均、以及运动点图像之间的竞争。所有这些都会在突出性和目标映射投射到SC后导致优先级（注意力）的进一步调节。只有将这些组织大脑原理纳入我们的模型中，我们才能充分理解复杂视觉信息转化为潜在的扫视程序。有了MASC，现在有了一个理论基础，可以在视觉复杂的现实环境中生成和测试计算明确的行为和神经反应预测。
Modern computational models of attention predict fixations using saliency maps and target maps, which prioritize locations for fixation based on feature contrast and target goals, respectively. But whereas many such models are biologically plausible, none have looked to the oculomotor system for design constraints or parameter specification. Conversely, although most models of saccade programming are tightly coupled to underlying neurophysiology, none have been tested using real-world stimuli and tasks. We combined the strengths of these two approaches in MASC, a model of attention in the superior colliculus (SC) that captures known neurophysiological constraints on saccade programming. We show that MASC predicted the fixation locations of humans freely viewing naturalistic scenes and performing exemplar and categorical search tasks, a breadth achieved by no other existing model. Moreover, it did this as well or better than its more specialized state-of-the-art competitors. MASC's predictive success stems from its inclusion of high-level but core principles of SC organization: an over-representation of foveal information, size-invariant population codes, cascaded population averaging over distorted visual and motor maps, and competition between motor point images for saccade programming, all of which cause further modulation of priority (attention) after projection of saliency and target maps to the SC. Only by incorporating these organizing brain principles into our models can we fully understand the transformation of complex visual information into the saccade programs underlying movements of overt attention. With MASC, a theoretical footing now exists to generate and test computationally explicit predictions of behavioral and neural responses in visually complex real-world contexts.