Representations of Predicted Uncertainty in Prefrontal and Sensory Cortex Prior to Search

Abstract

Theories of attention often focus on how known, definitive visual features are represented prior to the onset of a task. However, objects in the real-world are dynamic and have features that change overtime, creating uncertainty in their future appearance. This means that observers must make predictions about how the target could look. Representing the uncertainty of target features is essential for predictive processes, yet how the brain represents the uncertainty of upcoming visual stimuli is unclear. In this study, we investigate the brain regions that encode the distribution of possible target features prior to the onset of a visual search task. In each trial, participants (N=7) heard a cue-tone (high- or low-pitch) which corresponded to the central tendency of a target color (pink-orange, or blue-green, respectively). However, the true target color was drawn from a Gaussian distribution centered on the predicted color with either high or low variability, depending on the block. In each trial, there was one distractor object with a randomly selected color. Thus, to optimally represent the target object participants had to know both the central tendency of the target color, as well as the distribution of possible colors the target could have (i.e., the uncertainty). Using a combination of linear decoders and representational similarity analysis, we show the regions of early visual cortex and rostro-lateral prefrontal cortex (RLPFC) carry representations of the predicted target and the uncertainty of those predictions. Further, univariate analyses at the time of search found that RLPFC, medial frontal gyrus, and intraparietal cortex also coded prediction errors for the target object which depended on the variability of the target distribution. These results suggest that a network of sensory and frontal regions carry joint codes for both the mean and uncertainty of the distribution of possible upcoming search targets.