Error trials due to breaks in fixation, blinks, and releases of the lever before the offset of the stimulus (in the delayed match-to-sample task) were excluded. There were two types of error trials in

the reaction-time task: miss trials in which the target was present (and should have been Go trials) but the monkeys did not release the lever, and false alarms in which the target was absent (and should have been NoGo trials) but the monkeys released the lever. We computed the choice probabilities for these error types separately: (i) correct detection of target in Go trials vs. miss trials and (ii) false detection of target (false alarm) vs. correct rejection in NoGo trials. The choice probabilities were computed in the same fashion, based on 0.3 s of the fixation period or 0.3 s of the cue period, in the reaction-time task. Choice probabilities were computed for each neuron and distributions PD0325901 of values across neurons were then compared for neurons recorded from PPC and dlPFC. The variability of a neuron’s firing rate across trials was expressed as the Fano factor, defined as the variance of spike counts divided by the mean. The Fano factor was computed based on the algorithm developed by Churchland et al. (2010). First,

the variance and mean of the spike count were computed in each trial type, and then a regression of the variance to the mean was performed. The Fano factor reported here was the slope of this regression. Spike counts were computed Epacadostat in vitro in a 150-ms sliding window moving in 10-ms steps. The Fano factor was computed in three separate task periods in the delayed match-to-sample task, the fixation period (0.5 s), the cue period (0.5 s) and the delay period (1.0 s). We computed the Fano factor for correct and error trials separately for target in the receptive

field and target outside the receptive field conditions. Neurons with at least five trials per condition were used for this analysis. To evaluate the relationship between the trial-to-trial neuronal activity and behavioral reaction time, we computed a correlation coefficient between firing rate and reaction time using data from the standard version of the reaction-time task (Fig. 1C). DOK2 Firing rate when the stimulus appeared at the best location for each neuron was calculated for each 100-ms window, sliding in 20-ms intervals for each trial. A correlation coefficient was computed for each bin between the firing rates and corresponding reaction times. A correlation coefficient was also calculated for the fixation period (0.3 s) or the cue period (0.3 s). A correlation value was determined thus for each neuron. The distributions of correlation values were then compared across areas. Neurophysiological data were collected from areas 8 and 46 of the dlPFC and LIP of the PPC in two monkeys (Fig.