Visual working memory (VWM), the brief retention of past visual information, supports a range of cognitive functions. One of the defining, and largely studied, characteristics of VWM is how resource-limited it is, raising questions about how this resource is shared or split across memoranda. Since objects are rarely equally important in the real world, we ask how people split this resource in settings where objects have different levels of importance. In a psychophysical experiment, participants remembered the location of four targets with different probabilities of being tested after a delay. We then measured their memory accuracy of one of the targets. We found that participants allocated more resource to memoranda with higher priority, but underallocated resource to high- and overallocated to low-priority targets relative to the true probability of being tested. These results are well explained by a computational model in which resource is allocated to minimize expected estimation error. We replicated this finding in a second experiment in which participants bet on their memory fidelity after making the location estimate. The results of this experiment show that people have access to and utilize the quality of their memory when making decisions. Furthermore, people again allocate resource in a way that minimizes memory errors, even in a context in which an alternative strategy was incentivized. Our study not only shows that people are allocating resource according to behavioral relevance, but suggests that they are doing so with the aim of maximizing memory accuracy.

Visual working memory is a system used to hold information actively in mind for a limited time. The number of items and the precision with which we can store information has limits that define its capacity. How much control do we have over the precision with which we store information when faced with these severe capacity limitations? Here, we tested the hypothesis that rank-ordered attentional priority determines the precision of multiple working memory representations. We conducted two psychophysical experiments that manipulated the priority of multiple items in a two-alternative forced choice task (2AFC) with distance discrimination. In Experiment 1, we varied the probabilities with which memorized items were likely to be tested. To generalize the effects of priority beyond simple cueing, in Experiment 2, we manipulated priority by varying monetary incentives contingent upon successful memory for items tested. Moreover, we illustrate our hypothesis using a simple model that distributed attentional resources across items with rank-ordered priorities. Indeed, we found evidence in both experiments that priority affects the precision of working memory in a monotonic fashion. Our results demonstrate that representations of priority may provide a mechanism by which resources can be allocated to increase the precision with which we encode and briefly store information.

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Visual working memory (VWM), the brief retention of past visual information, supports a range of cognitive functions (Fukuda, Vogel, Mayr, & Awh, 2010; Johnson et al., 2013).The resource that supports VWM is limited, raising the question of how the brain allocates this limited resource to different objects. This question is even more interesting in ecological settings, in which objects are not equally important. In a psychophysical experiment, participants remembered the location of four targets with different probabilities of being tested after a delay. We then measured their memory accuracy of one of the targets. We found that participants allocated more resource to memoranda with higher priority, but underallocated resource to high-and overallocated to low-priority targets relative to the true probe probabilities. These results are well explained by a computational model in which resource is allocated to minimize expected estimation error. We replicated this finding in a second experiment in which participants bet on their memory fidelity after making the location estimate. The results of this experiment show that people 1) use information about memory quality and 2) minimize error even with an incentivized, alternative resource allocation strategy. Humans may mitigate the behavioral effects of a limited VWM through knowledge of memory fidelity and strategic resource allocation.