A Neural Network Model of Spatial and Feature-Based Attention
This work addresses the challenge of understanding human cognition through neural network models, but it is incremental as it builds on existing attention mechanisms without major breakthroughs.
The researchers tackled the problem of modeling human visual attention by designing a neural network with two components that guide attention for complex tasks, resulting in emergent patterns that correspond to spatial and feature-based attention.
Visual attention is a mechanism closely intertwined with vision and memory. Top-down information influences visual processing through attention. We designed a neural network model inspired by aspects of human visual attention. This model consists of two networks: one serves as a basic processor performing a simple task, while the other processes contextual information and guides the first network through attention to adapt to more complex tasks. After training the model and visualizing the learned attention response, we discovered that the model's emergent attention patterns corresponded to spatial and feature-based attention. This similarity between human visual attention and attention in computer vision suggests a promising direction for studying human cognition using neural network models.