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Recent studies have shown that instead of a dichotomy between parallel and serial search strategies, in many instances we see a combination of both search strategies utilized. Consequently, computational models and theoretical accounts of visual search processing have evolved from traditional parallel/serial descriptions to labels of “efficient” and “inefficient.” In the first experiment, we replicate previous findings with a between-subjects design, where incremental spoken language comprehension influences the efficiency of visual search processing. Next, we utilize a localist attractor network to simulate the results from the first experiment, and then employ the network to make quantitative predictions about the influence of subtle timing differences of real-time language processing on visual search. These predictions are then tested and confirmed in our second experiment. The results provide further evidence toward understanding linguistically mediated influences on real-time visual search processing and support an interactive processing account of visual search and language comprehension.