Thus, far there has been only a single study to investigate individual differences in sensitivity to interference during sentence processing which takes into account variation in language knowledge. Van Dyke et al. (2014) utilized a dual-task paradigm to assess participants' ability to suppress proactive interference from distractors that appeared in a 3-word memory list (e.g., TABLE, SINK, TRUCK) prior to reading the critical sentence. The critical contrast was between the sentences where the verb was manipulated as follows: It was boat that the guy who drank coffee FIXED/SAILED for 2 sunny days. A previous study (Van Dyke and McElree, 2006) with university-level participants demonstrated that when the verb appeared as fixed, participants experienced retrieval interference from the items in the memory list (which are all fixable items). The Van Dyke et al. (2014) study sampled from a broader range of ability levels and administered a comprehensive battery of 24 individual differences measures. After partialling out the variance that each measure shared with a composite measure of IQ (combining the vocabulary and matrix reasoning subtests of the Weschler Abbreviated Scale of Intelligence; Psychological Corp.; Wechsler, 1994/1999), they observed that WM capacity no longer interacted with individuals' sensitivity to interference whereas a receptive vocabulary measure (Peabody Picture Vocabulary Test-Revised; Dunn and Dunn, 1997) did, such that the comprehension for individuals with low vocabulary was more affected by interference. Van Dyke et al. interpreted this result as most consistent with the view that the quality of to-be-retrieved representations (assumed to be reflected in the receptive vocabulary measure) is a critical determinant of sensitivity to interference. In addition, Van Dyke et al. also observed a significant interaction of IQ with sensitivity to interference, which mirrored the effect found with vocabulary: individuals with lower IQ were more affected by interference. This interaction with IQ is difficult to fully interpret in light of the findings suggesting that IQ shares significant variance with WMC and that this shared variance is itself multi-faceted (Engle et al., 1999a,b; Kane and Engle, 2002; Hambrick et al., 2005; Kane et al., 2007; Shipstead et al., 2014; Harrison et al., 2015). Due to the collinearity of fluid intelligence and WM, we included the WAIS vocabulary measure (Wechsler, 1997; WAIS-III, 2002) as a control variable, because this task is generally viewed as a measure of crystallized intelligence, which has less shared variance with WM capacity (Kane et al., 2007). The inclusion of this task provides a means of assessing the role of WM capacity independent of lexical processing ability.