Table 3.

Percentage of variance across subjects explained by in-sample prediction ($R_{\mathrm{pred}}^2$) for summary statistics of known subjects' accuracy-RT distributions. All three models fit accuracy and correct-RT t₁ data very well, explaining over 92% of median correct-RT and over 90% of accuracy in each condition. However none of the models explain incorrect-RT t0 distributions well, a known problem for simple diffusion models that can be overcome by including variable drift rates directly in the likelihood function (Ratcliff, 1978; Ratcliff and McKoon, 2008).

Prediction of training data from known subjects
		Model 1 Comparison	Model 2 EEG-δ,τ	Model 3 EEG-δ,τ,ς
Low	25th t1 Percentile	96.5%	97.1%	97.8%
	t1 Median	96.6%	96.5%	97.3%
	75th t1 Percentile	91.4%	93.4%	94.5%
	Accuracy	95.1%	95.2%	97.3%
	t0 Median	–118.8%	–108.3%	–111.6%
Medium	25th t1 Percentile	86.0%	87.5%	88.6%
	t1 Median	95.9%	95.6%	96.3%
	75th t1 Percentile	84.7%	89.4%	90.1%
	Accuracy	90.7%	94.1%	95.3%
	t0 Median	–163.9%	–158.6%	–163.9%
High	25th t1 Percentile	85.4%	87.3%	86.7%
	t1 Median	93.1%	92.5%	92.9%
	75th t1 Percentile	79.1%	83.8%	84.0%
	Accuracy	95.9%	97.4%	95.9%
	t0 Median	–73.4%	–71.2%	–76.4%