, 2008, 2010; Ivanoff et al., 2008; van Veen et al., 2008; Mansfield et al., 2011; van Maanen et al., 2011). However, the neurophysiological mechanisms accomplishing SAT are unknown, as no test of SAT adjustments in nonhuman primates has been reported. Only neurophysiology provides the spatial and temporal resolution Transmembrane Transporters inhibitor necessary to decisively test the implementation of computational decision models. Multiple laboratories have demonstrated how the stochastic accumulation process is instantiated through the activity of specific neurons in the frontal eye field (FEF; Hanes and Schall, 1996; Boucher et al., 2007; Woodman et al., 2008;

Purcell et al., 2010, 2012; Ding and Gold, 2012), lateral intraparietal area (LIP; Roitman and Shadlen, 2002; Wong et al., selleckchem 2007), superior colliculus (SC; Ratcliff et al., 2003; 2007), and basal ganglia (Ding and Gold, 2010). However, no study has investigated whether single neurons accomplish SAT as predicted by the models. We addressed this by training macaque monkeys to perform voluntary, cued adjustments of SAT during visual search while recording from single neurons in the FEF. Monkeys exhibited proactive and immediate changes

in behavior when SAT cues changed. As observed in human SAT, an accumulator model described their behavioral data with systematic variation of just one parameter between SAT conditions—decision threshold. However, the neural correlates of SAT were much more diverse, affecting preperceptual, perceptual, categorical, and premovement activity in distinct functional types of neurons. Moreover, although the accumulator models Ribonucleotide reductase exhibit greater excursions from baseline to threshold when accuracy is stressed relative to speed, the neurons

that have been identified most clearly with stochastic accumulation exhibited smaller excursions. Thus, these results demonstrate that the simple stochastic accumulator model framework provides an incomplete description of the brain processes mediating SAT. These discrepancies were reconciled by recognizing constraints of the brainstem circuitry generating the saccades, which had invariant dynamics across all SAT conditions. These constraints require that the final net influence of FEF movement neurons is equivalent across SAT conditions. Our data were consistent with this; we discovered that leaky integration of FEF movement neuron activity terminated at the same level across SAT conditions. These relationships led naturally to an integrated accumulator model that reconciles the key features of stochastic accumulator models with the variety of neural adjustments we observed during SAT. Two Macaca radiata (Q and S) performed a visual search task to locate a target item presented among distractor items (T or L among Ls or Ts; Figure 1A).