In order to efficiently deploy our limited visual processing resources, we must decide what information is relevant and to be prioritized and what information should rather be ignored. To detect visual information that we know is relevant but that is not very salient, we need to set our system to prioritize and combine information from different visual dimensions (e.g., size, color, motion). Four experiments examined the allocation of processing resources across different visual dimensions when observers searched for a singleton target defined by a conjunction of size (primary dimension: the target was always large) with either color or motion (secondary dimension: variable across trials) within heterogeneously sized, colored, and moving distractors. The results revealed search reaction times to be substantially increased in a given trial in which the secondary target dimension was changed from the preceding trial—indicative of a suboptimal distribution of dimensional weights carried over from the previous trial and of attentional weight being bound by the (need to filter within the) primary dimension, thereby reducing the weight available for processing the secondary dimensions. Semantic precueing of the secondary dimension and visual marking of the search-irrelevant items in the primary dimension reduced these costs significantly. However, observers were limited in their ability to implement both top-down sets simultaneously. These findings argue in favor of a parallel distribution of dimensional processing resources across multiple visual dimensions and, furthermore, that visual marking releases attentional weight bound to the primary dimension, thus permitting more efficient (parallel) processing in the secondary dimensions.
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