, 1996 and Parker and Newsome, 1998). Our metric differs in that it is based on population projections onto an attention axis rather than spike counts from single neurons and in that it relies on responses to stimuli before the stimulus selleck kinase inhibitor change. We refer to our metric as DPAA to emphasize that this calculation is done on projections onto the attention axis (AA) (Cohen and Maunsell, 2010). As Figure 5A suggests, both feature and spatial attention predict performance, although spatial attention was

more predictive. The average DPAA for feature attention was 0.63, and DPAA for spatial attention was 0.68. This measure was significantly greater than 0.5 for both types of attention (t tests; p < 10−3). We assessed the dependence of DPAA on the number of neurons from which the attention axis projections this website were calculated (Figure 5B). For each recording session, we randomly selected (without replacement) subsets of neurons, calculated projections onto an attention axis constructed for just those neurons, computed the area under the ROC curve comparing the distributions of projections for correct and missed trials, and repeated the process

1000 times. For the combined feature and spatial attention axes, we calculated the percent correct classifications of the ideal linear discriminator between the two-dimensional distributions of projections

for correct and missed trials. DPAA increases with population size, and Dichloromethane dehalogenase appears to approach asymptote at population sizes only slightly larger than our mean of 83 neurons. We used this metric to test the possibility that some of the variability along the attention axis arose from variability in global factors such as arousal or alertness rather than variability in attention. This possibility seems unlikely, because both attention axes should be orthogonal to global axes. About half the neurons increase their rates and half decrease their rates in each attention condition. For spatial attention, neurons with receptive fields in the left hemifield tend to have higher firing rates in the attend-left than the attend-right condition, and the opposite is true for neurons whose receptive fields are in the right hemifield. For feature attention, about half of the neurons in each hemisphere respond more strongly in the orientation change than the spatial frequency change detection task. In contrast, global factors should comodulate all neurons. To directly test the possibility that global factors can predict behavior, we computed projections onto a response axis (from the origin to the mean response to the repeated stimulus).