Problem/Research Questions Visual working memory (VWM) allows us to temporarily store relevant information from the visual world despite frequent interruptions such as saccades. Despite the importance of VWM in a variety of cognitive tasks, this process is capacity limited. Behavioral estimates of capacity converge on a storage limit of ~3-4 items (Cowan, 2001). Converging neural evidence from neuroimaging techniques supports these behavioral estimates. For example, in functional magnetic resonance imaging (fMRI) experiments, delay-related activity in regions of posterior parietal cortex increases according to the number of items held within VWM (Todd & Marois, 2004; Xu & Chun, 2006). When capacity is reached the signal asymptotes, indicating that no additional neural resources are available to store any remaining items. Additionally, an event-related potential (ERP) known as the contralateral delay activity (CDA) has been used to measure storage capacity by recording from posterior scalp sites during the delay period during VWM tasks. The CDA amplitude increases as additional items are added, reaching asymptote when individual item limits are reached (Vogel & Machizawa, 2004; Vogel, McCollough, & Machizawa, 2005). Importantly, in these previous studies the neural correlates of VWM capacity reflect the aggregate processing of all of the presented stimuli. As such, the neural-correlate signal associated with each individual item is obscured within this cumulative activity. Additionally, the majority of these studies have focused almost exclusively on maintenance processes, creating uncertainty regarding the influence of encoding processes on capacity limitations. This leaves a fundamental but important question regarding basic VWM processes unanswered. Can cumulative neural activity during encoding be used to understand the neural fate of individual items presented in VWM tasks? Here we present evidence that cumulative activity during VWM encoding can be used to identify and quantify the neural-correlate signals associated with individual stimuli. Additionally, we describe novel frequency tagging, steady-state visual evoked potential (SSVEP) techniques used to isolate and examine these neural-correlate signals.