The study by Cohen and Maunsell investigates how visual attention improves behavioral performance by examining the effects of attention on the responses of neurons in visual area V4. They found that attention primarily improves performance by reducing interneuronal correlations rather than increasing firing rates or decreasing the Fano factor (a measure of variability). Over 80% of the attentional improvement in population sensitivity was attributed to decreases in the correlations between trial-to-trial fluctuations in the responses of pairs of neurons. This suggests that the representation of sensory information in populations of neurons and the way attention affects population sensitivity may be better understood by considering the interactions between neurons. The results highlight the importance of studying the interactions between neurons for understanding population coding and the mechanisms by which attention affects sensory processing.The study by Cohen and Maunsell investigates how visual attention improves behavioral performance by examining the effects of attention on the responses of neurons in visual area V4. They found that attention primarily improves performance by reducing interneuronal correlations rather than increasing firing rates or decreasing the Fano factor (a measure of variability). Over 80% of the attentional improvement in population sensitivity was attributed to decreases in the correlations between trial-to-trial fluctuations in the responses of pairs of neurons. This suggests that the representation of sensory information in populations of neurons and the way attention affects population sensitivity may be better understood by considering the interactions between neurons. The results highlight the importance of studying the interactions between neurons for understanding population coding and the mechanisms by which attention affects sensory processing.