Working memory is a mental storage system that keeps task-relevant information accessible for a brief span of time, and it is strikingly limited. Its limits differ substantially across people but are assumed to be fixed for a given person. Here we show that there is substantial variability in the quality of working memory representations within an individual. This variability can be explained neither by fluctuations in attention or arousal over time, nor by uneven distribution of a limited mental commodity. Variability of this sort is inconsistent with the assumptions of the standard cognitive models of working memory capacity, including both slot- and resource-based models, and so we propose a new framework for understanding the limitations of working memory: a stochastic process of degradation that plays out independently across memories.

The MemToolbox is a collection of MATLAB functions for modeling visual working memory. In support of its goal to provide a full suite of data analysis tools, the toolbox includes implementations of popular models of visual working memory, real and simulated data sets, Bayesian and maximum likelihood estimation procedures for fitting models to data, visualizations of data and fit, validation routines, model comparison metrics, and experiment scripts. The MemToolbox is released under the permissive BSD license and is available at http://memtoolbox.org.

The world is composed of features and objects and this structure may influence what is stored in working memory. It is widely believed that the content of memory is object-based: Memory stores integrated objects, not independent features. We asked participants to report the color and orientation of an object and found that memory errors were largely independent: Even when one of the object’s features was entirely forgotten, the other feature was often reported. This finding contradicts object-based models and challenges fundamental assumptions about the organization of information in working memory. We propose an alternative framework involving independent self-sustaining representations that may fail probabilistically and independently for each feature. This account predicts that the degree of independence in feature storage is determined by the degree of overlap in neural coding during perception. Consistent with this prediction, we found that errors for jointly encoded dimensions were less independent than errors for independently encoded dimensions.

An influential theory suggests that integrated objects, rather than individual features, are the fundamental units that limit our capacity to temporarily store visual information (S. J. Luck & E. K. Vogel, 1997). Using a paradigm that independently estimates the number and precision of items stored in working memory (W. Zhang & S. J. Luck, 2008), here we show that the storage of features is not cost-free. The precision and number of objects held in working memory was estimated when observers had to remember either the color, the orientation, or both the color and orientation of simple objects. We found that while the quantity of stored objects was largely unaffected by increasing the number of features, the precision of these representations dramatically decreased. Moreover, this selective deterioration in object precision depended on the multiple features being contained within the same objects. Such fidelity costs were even observed with change detection paradigms when those paradigms placed demands on the precision of the stored visual representations. Taken together, these findings not only demonstrate that the maintenance of integrated features is costly; they also suggest that objects and features affect visual working memory capacity differently.