For each condition and decay, the value of the integral 20–10 ms before saccade initiation was recorded as the trigger threshold ( Figure S5B). We found that the trigger threshold was invariant with respect to task conditions (Fast/Neutral/Accurate condition) and made or missed deadline (premature Accurate/late Fast) when the selleck compound decay constant was in the range of plausible values (7.1 ms < τ < 166.7; McCormick et al., 1985). What differed between SAT conditions was

the amount of time needed for this integration to reach a single, constant threshold ( Figures 5 and S5B). We also computed the time course of integration for each RT quantile, separated by made/missed deadline and SAT condition. Remarkably, the trigger thresholds remained constant for both movement and visuomovement neurons ( Figures S5B and S5C). For each of 5,000 simulated trials per SAT condition, a start point (A) was drawn from a uniform distribution, and a drift rate (v) was drawn from a normal distribution with standard deviation s. The drift rate for distractor items was set to 1 − v. Activation functions that increased linearly with rate v were integrated with leak τ in the same manner as the movement activity described above. The values for A, v, and nondecision time T0 were allowed to vary between SAT conditions.

Selleckchem Osimertinib Leakage τ was not fixed but was shared across SAT conditions because cognitive state is unlikely to influence brainstem saccade-triggering mechanisms. The distribution of simulated RTs and proportions correct were compared against Vincentized behavioral data using

χ2. Outliers were removed from the behavioral and simulated data by eliminating values beyond median ± 1.5 × the interquartile range for each condition separately. Data are presented as defective CDFs, normalized to the mean accuracy rate. Minimization was carried out in several steps, first using multiple runs of the genetic algorithm in MATLAB with different random number seeds and values for s. The best fitting of these were minimized again with bounded simplex algorithms. This work was supported by F32-EY019851 to R.P.H. and by R01-EY08890, P30-EY08126, P30-HD015052, and the E. Bronson Ingram Chair in Neuroscience. We would like to thank S. Cediranib (AZD2171) Brown, J. Cohen, R. Desimone, P. Holmes, G. Logan, A. Maier, P. Middlebrooks, T. Palmeri, M. Paré, B. Purcell, R. Ramachandran, R. Ratcliff, F. Tong, M. Wallace, X.J. Wang, and B. Zandbelt for comments. R.P.H. designed the study, collected the data, and analyzed the results. R.P.H. and J.D.S. wrote the paper. “
“Despite the widespread use of functional magnetic resonance imaging (fMRI), the relative contributions of processes like feedforward, feedback, excitation, and inhibition to the blood oxygenation level-dependent (BOLD) signal remain unknown.