The mechanisms underlying visual working memory have recently become controversial. One account proposes a small number of memory "slots," each capable of storing a single visual object with fixed precision. A contrary view holds that working memory is a shared resource, with no upper limit on the number of items stored; instead, the more items that are held in memory, the less precisely each can be recalled. Recent findings from a color report task have been taken as crucial new evidence in favor of the slot model. However, while this task has previously been thought of as a simple test of memory for color, here we show that performance also critically depends on memory for location. When errors in memory are considered for both color and location, performance on this task is in fact well explained by the resource model. These results demonstrate that visual working memory consists of a common resource distributed dynamically across the visual scene, with no need to invoke an upper limit on the number of objects represented.

Our ability to remember what we have seen is very limited. Most current views characterize this limit as a fixed number of items-only four objects-that can be held in visual working memory. We show that visual memory capacity is not fixed by the number of objects, but rather is a limited resource that is shared out dynamically between all items in the visual scene. This resource can be shifted flexibly between objects, with allocation biased by selective attention and toward targets of upcoming eye movements. The proportion of resources allocated to each item determines the precision with which it is remembered, a relation that we show is governed by a simple power law, allowing quantitative estimates of resource distribution in a scene.

Working memory is widely considered to be limited in capacity, holding a fixed, small number of items, such as Miller's ‘magical number’ seven or Cowan's four. It has recently been proposed that working memory might better be conceptualized as a limited resource that is distributed flexibly among all items to be maintained in memory. According to this view, the quality rather than the quantity of working memory representations determines performance. Here we consider behavioral and emerging neural evidence for this proposal.

Recent neurophysiological and imaging studies have investigated how neural representations underlying working memory (WM) are dynamically updated for objects presented sequentially. Although such studies implicate information encoded in oscillatory activity across distributed brain networks, interpretation of findings depends crucially on the underlying conceptual model of how memory resources are distributed.Here, we quantify the fidelity of human memory for sequences of colored stimuli of different orientation. The precision with which each orientation was recalled declined with increases in total memory load, but also depended on when in the sequence it appeared. When one item was prioritized, its recall was enhanced, but with corresponding decrements in precision for other objects. Comparison with the same number of items presented simultaneously revealed an additional performance cost for sequential display that could not be explained by temporal decay. Memory precision was lower for sequential compared with simultaneous presentation, even when each item in the sequence was presented at a different location.Importantly, stochastic modeling established this cost for sequential display was due to misbinding object features (color and orientation). These results support the view that WM resources can be dynamically and flexibly updated as new items have to be stored, but redistribution of resources with the addition of new items is associated with misbinding object features, providing important constraints and a framework for interpreting neural data.

Errors in short-term memory increase with the quantity of information stored, limiting the complexity of cognition and behavior. In visual memory, attempts to account for errors in terms of allocation of a limited pool of working memory resources have met with some success, but the biological basis for this cognitive architecture is unclear. An alternative perspective attributes recall errors to noise in tuned populations of neurons that encode stimulus features in spiking activity. I show that errors associated with decreasing signal strength in probabilistically spiking neurons reproduce the pattern of failures in human recall under increasing memory load. In particular, deviations from the normal distribution that are characteristic of working memory errors and have been attributed previously to guesses or variability in precision are shown to arise as a natural consequence of decoding populations of tuned neurons. Observers possess fine control over memory representations and prioritize accurate storage of behaviorally relevant information, at a cost to lower priority stimuli. I show that changing the input drive to neurons encoding a prioritized stimulus biases population activity in a manner that reproduces this empirical tradeoff in memory precision. In a task in which predictive cues indicate stimuli most probable for test, human observers use the cues in an optimal manner to maximize performance, within the constraints imposed by neural noise.

An influential conception of visual working memory is of a small number of discrete memory "slots", each storing an integrated representation of a single visual object, including all its component features. When a scene contains more objects than there are slots, visual attention controls which objects gain access to memory.A key prediction of such a model is that the absolute error in recalling multiple features of the same object will be correlated, because features belonging to an attended object are all stored, bound together. Here, we tested participants' ability to reproduce from memory both the color and orientation of an object indicated by a location cue. We observed strong independence of errors between feature dimensions even for large (6 item) memory arrays, inconsistent with an upper limit on the number of objects held in memory.Examining the pattern of responses in each dimension revealed a gaussian distribution of error centered on the target value that increased in width under higher memory loads. For large arrays, a subset of responses were not centered on the target but instead predominantly corresponded to mistakenly reproducing one of the other features held in memory. These misreporting responses again occurred independently in each feature dimension, consistent with 'misbinding' due to errors in maintaining the binding information that assigns features to objects.The results support a shared-resource model of working memory, in which increasing memory load incrementally degrades storage of visual information, reducing the fidelity with which both object features and feature bindings are maintained.

Recent studies have demonstrated that memory performance can be enhanced by a cue which indicates the item most likely to be subsequently probed, even when that cue is delivered seconds after a stimulus array is extinguished. Although such retro-cuing has attracted considerable interest, the mechanisms underlying it remain unclear. Here, we tested the hypothesis that retro-cues might protect an item from degradation over time. We employed two techniques that previously have not been deployed in retro-cuing tasks. First, we used a sensitive, continuous scale for reporting the orientation of a memorized item, rather than binary measures (change or no change) typically used in previous studies. Second, to investigate the stability of memory across time, we also systematically varied the duration between the retro-cue and report. Although accuracy of reporting uncued objects rapidly declined over short intervals, retro-cued items were significantly more stable, showing negligible decline in accuracy across time and protection from forgetting. Retro-cuing an object’s color was just as advantageous as spatial retro-cues. These findings demonstrate that during maintenance, even when items are no longer visible, attention resources can be selectively redeployed to protect the accuracy with which a cued item can be recalled over time, but with a corresponding cost in recall for uncued items.

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