A Connectionist Model of Visual Attention

选择性视觉注意将刺激数据的处理串行化，以有效利用人类视觉系统中有限的处理资源。这项工作将开发一个连接主义网络，以模仿特雷斯曼、沃尔福德、邓肯和其他人所报道的各种注意现象。如前期工作中所证明的，使用具有强横向抑制连接的特征阵列的分层、多尺度网络能够产生暗示与视觉注意相关的许多关键行为的反应。要做的工作包括将早期工作的网络结构集成到一个单一的内聚体系结构中，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与行为和神经数据更定量的匹配。这项工作的目标是提供(1)机器识别系统的实用计算模型，(2)比现有理论更全面的人类视觉注意力模型，(3)基于注意力的神经基础与数据联系的网络模型。

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.