Network visualizations are understudied in graphical perception. As a result, most network visualization designs still largely rely on designer intuition and algorithm optimizations rather than being guided by knowledge of human perception. The lack of perceptual understanding of network visualizations also limits the generalizability of past empirical evaluations, given their focus on performance over causal interpretation. To bridge this gap between perception and network visualization, we introduce a framework highlighting five key perceptual mechanisms used in node-link diagrams and adjacency matrices: attention, visual search, perceptual organization, ensemble coding, and object recognition. Our framework describes the role these perceptual mechanisms play in common network analytical tasks. We use the framework to revisit four past empirical investigations and outline future design experiments that can help produce more perceptually effective network visualizations. We anticipate this connection will afford translational understanding to guide more effective network visualization design and offer hypotheses for perception-aware network visualizations.

For a humanoid robot to interact easily with a person, the robot should have human-like sensory capabilities and attentional mechanisms. Particularly useful is an active vision head controlled by a visual attention system that selects viewpoints in the environment as a function of the robot's task. This paper describes a model of human visual attention called FeatureGate, which is a locally connected, pyramidal, artificial neural network that operates on 2D feature maps of the environment. Given a set of feature maps, and the description of a specific target, FeatureGate finds the location whose features most closely match those of the target. The paper describes the network, its implementation, a series of tests that characterize its performance with respect to a person's performance on a similar task, and its use in the control of an active vision system.