(a) Schematic representation of the one-choice drift diffusion model. Noisy error evidence accumulates over time at mean drift rate v until a threshold a is reached (light red traces) or a deadline on detection expires (gray traces). Drift rate is normally distributed across trials with standard deviation η, and non-decision-related processing time is captured by tnd. Within-trial noise s is fixed at 0.1 (not shown). (b) Group-level model fit. Points from left to right represent the 0.1, 0.3, 0.5, 0.7 and 0.9 RTd quantiles, estimated from the data (black) and generated by the model fit (gray). (c) Observed versus fitted error detection accuracy. Diagonal line is the identity line. (d) Group-average decision variables reflecting the accumulation of error evidence, simulated using the best-fitting model parameters for each subject and overlaid on the grand-average second-order CPP signals. Detected error traces are sorted by RTd into three equal-sized bins. CPPs are baselined to the 50 ms preceding error. (e) Between-subjects relationship between FCθ power and model-estimated drift ratio (v/η). Bar graph (inset) shows standardized regression coefficients from a multiple regression that included CPP amplitude as an additional predictor. (f, g) Scatterplots and bar graphs highlighting between-subjects relationships between drift ratio and signal build-up rates (f) and peak latencies (g). Although one extreme data point is apparent in f, this did not exert disproportionate influence over the fitted regression line (studentized deleted residual < 1) and the correlation remained marginally significant when it was removed (r = 0.37, p = 0.06). Error bars = s.e.m. *p < 0.05; **p < 0.01.