The storage limitations of WM have been the subject of intense research interest for several decades (see Luck & Vogel,
2013), but although several studies have reported a reduction in WM capacity with high memory load (e.g., Xu,
2007; Chee & Chuah,
2007), WM overload has only been the focus of a handful of behavioral experiments (Cusack et al.,
2009; Linke et al.,
2011; Matsuyoshi et al.,
2014; Fukuda, Woodman, & Vogel,
2015). We investigated the neural basis of overload with the PPC and PPC-PFC models, finding that overload could be reduced in both models by strong competitive dynamics during the stimulus interval of simulated WM tasks. The PPC-only model, however, showed a positive correlation between peak capacity and overload (
Figure 5), in opposition to available data (
Figure 3). The PPC-PFC model accounted for these data in a parameter regime where selective encoding was supported by strong competitive dynamics in
PFC, persistent activity was supported by interareal projections, and weak dynamics in
PPC limited competition during the memory delay (
Figure 7). As such, the model implemented hierarchical recruitment of competition during stimulus encoding and identified a set of computational principles for WM storage in distributed circuitry. Under these principles, all WM items were encoded by
PPC (
Figure 7B), consistent with single-cell electrophysiological recordings from PPC (Thomas & Paré,
2007); simulated EEG amplitude was bilinear over memory load during the delay period (
Figure 12A, black curves), consistent with EEG recordings over parieto-occipital cortex (Vogel & Machizawa,
2004; Fukuda, Woodman, & Vogel,
2015); and peak capacity was around three items, consistent with behavioral data from numerous WM tasks (
Figure 7B; see Cowan,
2001; Luck & Vogel,
2013). When we violated the identified principles (increased competition in
PPC, decreased strength of feedback projections and decreased competition in
PFC), peak capacity was reduced to just over two items (
Figure 11A), overload was greater than 50% of peak capacity (
Figure 11C), and the “second line” of the bilinearity of simulated EEG amplitude showed a negative slope (
Figure 12A, gray curves). These results are strikingly consistent with behavioral and EEG data from low-capacity subjects in the study by Fukuda, Woodman, and Vogel (
2015). To our surprise, the model implemented selective encoding in this low-capacity regime (
Figure 11B). Thus, while it captured a strategy for WM storage under high load and offered a set of neural mechanisms for its implementation in hierarchical circuitry, it predicted that low-capacity subjects are indeed attempting this strategy and that their performance reflects poor control of frontoparietal processing. Our hypothesis is testable by the prediction that EEG amplitude over memory load will show greater concavity over lateral PFC than over PPC during the stimulus interval of WM tasks (
Figure 13), providing a neural signature of early selection.