Generating predictions for task-relevant goals is a fundamental requirement of human information processing, as it ensures adaptive success in our complex natural environment. Clark (in press) proposed a model of hierarchical predictive processing, in which perception, attention, and learning are unified within a coherent framework. In this view, incoming sensory signals are constantly matched with top-down expectations or predictions, with the aim of minimizing the prediction error to generate adaptive behavior. For example, in a natural environment such as a kitchen, search for a given target object (e.g., a pan) might be guided by a variety of predictive cues generated by previously acquired knowledge, such as the target’s typical appearance (e.g., its color, size, and shape as defined by a top-down implemented search template). In addition, predictions can also be derived from contextual factors, such as the most probable location of the target (e.g., on the stove), and its typical co-occurrence with other objects (e.g., pan and kettle; see Oliva and Torralba, 2007; Wolfe et al., 2011, for reviews).
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