Page 85 - Designing Sociable Robots

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66 Chapter 6

selectively enhances or suppresses the contribution of certain features, but does not alter
the underlying raw saliency of a stimulus (Niedenthal & Kityama, 1994). To implement
this, the bottom-up results of each feature map are each passed through a filter (effectively a
gain). The value of each gain is determined by the active behavior. These modulated feature
maps are then summed to compute the overall attention activation map.
This serves to bias attention in a way that facilitates achieving the goal of the active
behavior. For example, if the robot is searching for social stimuli, it becomes sensitive to
skin tone and less sensitive to color. Behaviorally, the robot may encounter toys in its search,
but will continue until a skin-toned stimulus is found (often a person’s face). Figure 6.3
illustrates how gain adjustment biases what the robot finds to be more salient.
As shown in figure 6.4, the skin-tone gain is enhanced when the seek-people behavior
is active, and is suppressed when the avoid-people behavior is active. Similarly, the
color gain is enhanced when the seek-toys behavior is active, and suppressed when the
avoid-toys behavior is active. Whenever the engage-people or engage-toys behaviors
are active, the face and color gains are restored to slightly favor the desired stimulus. Weight
adjustments are constrained such that the total sum of the weights remains constant at all
times.

Figure 6.3
Effect of gain adjustment on looking preference. Circles correspond to fixation points, sampled at one-second
intervals. On the left, the gain of the skin tone filter is higher. The robot spends more time looking at the face in
the scene (86% face, 14% block). This bias occurs despite the fact that the face is dwarfed by the block in the
visual scene. On the right, the gain of the color saliency filter is higher. The robot now spends more time looking
at the brightly colored block (28% face, 72% block).