Feature-Independent Neural Coding of Target Detection during Search of Natural Scenes

Abstract

Visual search requires humans to detect a great variety of target objects in scenes cluttered by other objects or the natural environment. It is unknown whether there is a general purpose neural detection mechanism in the brain that codes the presence of a wide variety of categories of objects embedded in natural scenes. We provide evidence for a feature-independent coding mechanism for detecting behaviorally relevant targets in natural scenes in the dorsal frontoparietal network. Pattern classifiers using single-trial fMRI responses in the dorsal frontoparietal network reliably predicted the presence of 368 different target objects and also the observer's choices. Other vision-related areas such as the primary visual cortex, lateral occipital complex, the parahippocampal, and the fusiform gyri did not predict target presence, while high-level association areas related to general purpose decision making, including the dorsolateral prefrontal cortex and anterior cingulate, did. Activity in the intraparietal sulcus, a main area in the dorsal frontoparietal network, correlated with observers' decision confidence and with the task difficulty of individual images. These results cannot be explained by physical differences across images or eye movements. Thus, the dorsal frontoparietal network detects behaviorally relevant targets in natural scenes independent of their defining visual features and may be the human analog of the priority map in monkey lateral intraparietal cortex.