UC Davis

The concept of target templates is a core construct in all models of attention, guiding attention during search and acting as a “template” for making target match decisions. Previous work has generally focused on how the identity of a known target is stored in the target template and the consequences of failures to maintain that representation in memory. Few, if any, have tested how the target template adapts to uncertainty in target features and makes predictions about how the target will look in the future. In this doctoral thesis, I present a series of four experiments that address how predictive uncertainty shapes representations of target templates, on both cognitive and neural levels. I show that observers readily learn statistical knowledge about targets and use this knowledge to make predictions about the likelihood of various target features. These predictions are then used to set priority for features which minimize uncertainty about the upcoming target, biasing eye-movements and reducing decision times when participants are certain about how the target will look. At the neural level, I show that feature uncertainty is coded in dorsolateral prefrontal cortex and inferior frontal junction, supporting search behavior for uncertain targets. These findings contribute to our understanding of how attentional templates adapt to statistical knowledge about uncertain target features and provide insights into theories of attention and perceptual learning.