In the mixed-effects models, fixed effects included the main effects and interaction of semantic and syntactic interference manipulations, as well as the main effects of all the individual differences and their interactions with semantic/syntactic interference. In addition to these fixed effects, all the mixed-effects models included maximal random-effects structures to provide the most conservative assessment of the significance of results (Barr et al., 2013). Thus, by-subject adjustments to the intercept as well as by-subject adjustments to the random slope of interaction between semantic × syntactic interference were included in the models. Similarly, by-item adjustments to the intercept and to the random slope of interaction between semantic × syntactic interference were included. In addition, word length was included as a control factor in all the models for the critical and spillover regions. There was no convergence problem for any of the models reported in this study. Throughout, we present coefficient estimates, standard errors (SE), and t- or z-scores (when the dependent measures is a dichotomous variable, i.e., accuracy) derived from 50,000 Monte Carlo Markov Chain (MCMC) runs. For the RT data, the degrees of freedom are not reported because they can only be approximated in LMEMs, and consequently p-values are not reported. The t- or z-score based on MCMC sampling and t- or z-score based on the upper bound of the degrees of freedom tend to be very close for a relatively large sample (Baayen et al., 2008). Hence, we adopted a standard in which an absolute t- equal to or >2.0 was considered to be significant at the α = 0.05 level. For the mixed logistic regression analysis of errors, degrees of freedom can be calculated and thus, p-values are reported in the results for these analyses.