Direct access to motor cortical information now enables tetraplegic patients to precisely control neuroprostheses and recover some autonomy. In contrast, explicit access to higher cortical cognitive functions, such as covert attention, has been missing. Indeed, this cognitive information, known only to the subject, can solely be inferred by an observer from the subject's overt behavior. Here, we present direct two-dimensional real-time access to where monkeys are covertly paying attention, using machine-learning decoding methods applied to their ongoing prefrontal cortical activity. Decoded attention was highly predictive of overt behavior in a cued target-detection task. Indeed, monkeys had a higher probability of detecting a visual stimulus as the distance between decoded attention and stimulus location decreased. This was true whether the visual stimulus was presented at the cued target location or at another distractor location. In error trials, in which the animals failed to detect the cued target stimulus, both the locations of attention and visual cue were misencoded. This misencoding coincided with a specific state of the prefrontal cortical population in which the shared variability between its different neurons (or noise correlations) was high, even before trial onset. This observation strongly suggests a functional link between high noise-correlation states and attentional failure. Overall, this real-time access to the attentional spotlight, as well as the identification of a neural signature of attentional lapses, open new perspectives both to the study of the neural bases of attention and to the remediation or enhancement of the attentional function using neurofeedback.

Despite an ever growing knowledge on how parietal and prefrontal neurons encode low-level spatial and color information or higher-level information, such as spatial attention, an understanding of how these cortical regions process neuronal information at the population level is still missing. A simple assumption would be that the function and temporal response profiles of these neuronal populations match that of its constituting individual cells. However, several recent studies suggest that this is not necessarily the case and that the single-cell approach overlooks dynamic changes in how information is distributed over the neuronal population. Here, we use a time-resolved population pattern analysis to explore how spatial position, spatial attention and color information are differentially encoded and maintained in the macaque monkey prefrontal (frontal eye fields) and parietal cortex (lateral intraparietal area). Overall, our work brings about three novel observations. First, we show that parietal and prefrontal populations operate in two distinct population regimens for the encoding of sensory and cognitive information: a stationary mode and a dynamic mode. Second, we show that the temporal dynamics of a heterogeneous neuronal population brings about complementary information to that of its functional subpopulations. Thus, both need to be investigated in parallel. Last, we show that identifying the neuronal configuration in which a neuronal population encodes given information can serve to reveal this same information in a different context. All together, this work challenges common views on neural coding in the parietofrontal network.