Model Comparison A
Model	logLik	deviance	Chisq	df	p-value
Detection Latency ~ 1 + (Error Type | Subject)	−1051.3	2102.6
Detection Latency ~ Error Latency + (Error Type | Subject)	−1051.2	2102.4	0.19	1	0.66
Detection Latency ~ Error Latency + Detection Type+(Error Type | Subject)	−1007.0	2014.1	88.37	1	<.001*
Detection Latency ~ Error Latency + Detection Type + Error Type + (Error Type | Subject)	−1001.9	2003.8	10.32	1	<.01*
Detection Latency ~ Error Latency + Detection Type*Error Type + (Error Type | Subject)	−989.7	1979.4	24.36	1	<.001*

Model Comparison B
Model	logLik	deviance	Chisq	df	p-value
Detection Latency ~ 1 + (Error Type | Subject)	−737.5	1475.0
Detection Latency ~ Error Latency + (Error Type | Subject)	−735.2	1470.3	4.72	1	0.03*
Detection Latency ~ Error Latency + Phonological Overlap + (Error Type | Subject)	−716.8	1433.5	36.79	1	<.001*
Detection Latency ~ Error Latency + Phonological Overlap + Repair Accuracy + (Error Type | Subject)	−712.9	1425.7	7.77	1	<.01*
Detection Latency ~ Error Latency + Phonological Overlap + Repair Accuracy + Error Type + (Error Type | Subject)	−709.5	1419.1	6.67	1	<.01*
Detection Latency ~ Error Latency + Phonological Overlap + Repair Accuracy*Error Type + (Error Type | Subject)	−702.6	1405.2	13.92	1	<.001*

Note. The base model includes the intercept and random effects represented as (Error Type | Subject). The subsequent comparison models show the individually added fixed effects in bold, with “*” representing the complete set of main effects and interactions. Improvements in model fit were evaluated using the change in the deviance statistic, which is distributed as chi-squared with degrees of freedom equal to the number of parameters added.