A Connectionist Model of Visual Attention

选择性视觉注意使刺激的加工序列化 数据，以有效利用有限的处理资源， 人类视觉系统。 这项工作将发展一个连接主义者 网络来模拟各种注意力现象， Treisman、Wolford、邓肯和其他人都报道过。 作为 在初步工作中证明， 使用具有强横向抑制的特征阵列的网络 连接能够产生暗示性的反应， 与视觉注意力相关的一些关键行为。 要做的工作包括整合网络结构 将早期的作品整合到一个单一的有凝聚力的架构中， 详细说明该网络模型以支持附加功能， 并调整网络结构，以提供更定量的 与行为和神经数据吻合 工作的目标是 提供（1）一个实用的计算模型， 识别系统，（2）人类视觉注意力模型， 比现有的理论更全面，（3）网络 一种基于神经网络与数据联系的模型， 关注

Selective visual attention serializes the processing of stimulus data to make efficient use of limited processing resources in the human visual system. This work will develop a connectionist network to emulate a variety of attentional phenomena that have been reported by Treisman, Wolford, Duncan and others. As demonstrated in preliminary work, a hierarchical, multi-scale network that uses feature arrays with strong lateral inhibitory connections is capable of producing responses that are suggestive of a number of key behaviors associated with visual attention. The work to be done involves integrating the network structures of the earlier work into a single cohesive architecture, elaborating this network model to support additional function, and tuning the network structure to provide a more quantitative match with behavioral and neural data. The goals of the work are to provide (1) a practical computational model for machine recognition systems, (2) a model of human visual attention that is more comprehensive than current theories, and (3) a network model that makes contact with data on the neural basis of attention.