Increasing Working Memory Load Reduces Processing of Cross-Modal Task-Irrelevant Stimuli Even after Controlling for Task Difficulty and Executive Capacity

The classic account of the load theory (LT) of attention suggests that increasing cognitive load leads to greater processing of task-irrelevant stimuli due to competition for limited executive resource that reduces the ability to actively maintain current processing priorities. Studies testing this hypothesis have yielded widely divergent outcomes. The inconsistent results may, in part, be related to variability in executive capacity (EC) and task difficulty across subjects in different studies. Here, we used a cross-modal paradigm to investigate whether augmented working memory (WM) load leads to increased early distracter processing, and controlled for the potential confounders of EC and task difficulty. Twenty-three young subjects were engaged in a primary visual WM task, under high and low load conditions, while instructed to ignore irrelevant auditory stimuli. Demands of the high load condition were individually titrated to make task difficulty comparable across subjects with differing EC. Event-related potentials (ERPs) were used to measure neural activity in response to stimuli presented in both the task relevant modality (visual) and task-irrelevant modality (auditory). Behavioral results indicate that the load manipulation and titration procedure of the primary visual task were successful. ERPs demonstrated that in response to visual target stimuli, there was a load-related increase in the posterior slow wave, an index of sustained attention and effort. Importantly, under high load, there was a decrease of the auditory N1 in response to distracters, a marker of early auditory processing. These results suggest that increased WM load is associated with enhanced attentional engagement and protection from distraction in a cross-modal setting, even after controlling for task difficulty and EC. Our findings challenge the classic LT and offer support for alternative models.