We further hypothesized that the direction and rate of change of neural activity at the time of the go cue (the “neural velocity”) also relates to that trial’s RT. We investigated this possibility using a similar analysis to that above, but now correlating the neural velocity at the time of the go cue (vgo) projected onto the mean neural trajectory with RT ( Figure 4A). In order to isolate the effects of neural velocity from position, we grouped trials together that had similar neural positions, which was done by further segregating our data by delay period into 100 ms bins (justified by results in Figure S1G). As shown in Figure 4B, for

both RO4929097 supplier monkeys the histograms have medians significantly less Fasudil cell line than zero (p < 0.01; Wilcoxon signed-rank test). This is consistent with the hypothesis that the greater the rate of change of neural activity in the direction of the mean neural trajectory at the time of the go cue, the shorter the RT. We again performed

control analyses to rule out alternative hypotheses, as described in Figure S2. Specifically, we found that the overall neural speed (i.e., magnitude of velocity) did not provide a stronger correlate with RT and that the observed correlations did not derive solely from the correlation of neural position and neural velocity to each other (Figures S2A and S2B). We combined both neural position and velocity along the mean neural trajectory at the time of the go cue to construct a multivariate predictor of trial-by-trial RT. Since the mean neural trajectory changes direction around the time of the go cue (see Figure 3B), we projected both position and velocity onto two vectors each, defined by the mean neural trajectory at times both before

and after the go cue. The vector representing the mean trajectory prior to the go cue, p¯go−Δt’, was based on an offset of Δt′ chosen to maximize the average correlation as before (see Figure S1B). The four resulting already covariates (each of neural position and velocity projected onto each of the pre- and post-“go” directions) were used as inputs to a multivariate linear regression for RT. This model was compared with other RT predictors in the literature: the rise-to-threshold hypothesis (the best performing of three different definitions of the rise-to-threshold process is shown); the optimal subspace hypothesis; and an independent linear decoding method (see Experimental Procedures). The percentage of total data variance explained is shown in the bar graph in Figure 5. This method explained more variance for each data set, had the most targets with significant correlations, and explained approximately 4-fold more variance than the next best model overall.