Access to WM can be restricted on the basis of goal-relevant properties such as spatial location. However, the extent of voluntary control over which features of an attended multi-feature object are encoded and maintained in WM is debated. Some evidence suggests that attending to an object leads to obligatory storage of all of its features, whereas other evidence suggests that access to WM can be restricted to only goal-relevant features. Another possibility is that all features are initially encoded, but irrelevant features are removed from WM over time. To address these various possibilities, we used pattern classification of EEG signals to track the temporal evolution of representations reflecting the encoding and storage of task-relevant and irrelevant features in WM. In different blocks, participants remembered the orientation, color or both orientation and color of a colored, oriented grating. The color and orientation of the grating was randomly drawn from two distinct feature bins on each trial. To examine trial-specific activity reflecting storage of the object's features, a support vector machine (SVM) classifier was trained to classify what bin the stimulus features came from. Importantly, for orientation, the classifier produced reliably above-chance classification across the delay when orientation was task-relevant but not when it was task-irrelevant. Interestingly, orientation could be accurately classified on trials for which both orientation and color were remembered. Moreover, a separate measure corresponding to the probability of a feature belonging to the correct bin was significantly higher when orientation was task-relevant compared to task-irrelevant during encoding. Above-chance classification for color was only present during the initial 500 ms across all conditions. Our results suggest that although information about all of an object's features is present in the initial stimulus-evoked neural response, information about the task-irrelevant features is attenuated during stimulus encoding and is largely absent throughout the delay.

Studies of change detection have shown that changing the task-irrelevant features of remembered objects impairs change detection for task-relevant features, a phenomenon known as the irrelevant change effect. Although this effect is pronounced at short study-test intervals, it is eliminated at longer delays. This has prompted the proposal that although all features of attended objects are initially stored together in visual working memory (VWM), top-down control can be used to suppress task-irrelevant features over time. The present study reports the results of three experiments aimed at testing the top-down suppression hypothesis. Experiments 1 and 2 tested whether the magnitude or time course of the irrelevant change effect was affected by the concurrent performance of a demanding executive load task (counting backwards by threes). Contrary to the top-down suppression view, the decreased availability of executive resources did not prolong the duration of the irrelevant change effect in either experiment, as would be expected if these resources were necessary to actively suppress task-irrelevant features. Experiment 3 showed that a visual pattern mask eliminates the irrelevant change effect and suggests that the source of the effect may lie in the use a high-resolution, sensory memory representation to match the memory and test displays when no task-irrelevant feature changes are present. These results suggest that the dissipation of the irrelevant change effect over time likely does not depend on the use of top-down control and raises questions about what can be inferred about the nature of storage in VWM from studies of this effect.

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The ability of prefrontal cortex to quickly encode novel associations is crucial for adaptive behavior and central to working memory. Fast Hebbian changes in synaptic strength permit forming new associations, but neuronal signatures of this have been elusive. We devised a trialwise index of pattern similarity to look for rapid changes in population codes. Based on a computational model of working memory, we hypothesized that synaptic strength—and consequently, the tuning of neurons—could change if features of a subsequent stimulus need to be “reassociated,” i.e., if bindings between features need to be broken to encode the new item. As a result, identical stimuli might elicit different neural responses. As predicted, neural response similarity dropped following rebinding, but only in prefrontal cortex. The history-dependent changes were expressed on top of traditional, fixed selectivity and were not explainable by carryover of previous firing into the current trial or by neural adaptation.

There is considerable debate regarding the ability to trade mnemonic precision for capacity in working memory (WM), with some studies reporting evidence consistent with such a trade-off and others suggesting it may not be possible. The majority of studies addressing this question have utilized a standard approach to analyzing continuous recall data in which individual-subject data from each experimental condition is fitted with a probabilistic model of choice. Estimated parameter values related to different aspects of WM (e.g., the capacity and precision of stored items) are then compared using statistical tests to determine the presence of hypothesized differences between experimental conditions. However, recent research has suggested that the standard approach is flawed in several respects. In this study, we adapted the methods of Roggeman et al. (2014) and analyzed the data using the standard analytical approach and a more rigorous Bayesian model comparison (BMC) approach. The second approach involved generating a set of probabilistic models whose priors reflect different hypotheses regarding the effect of our key experimental manipulations on behavior. Our results demonstrate that these two approaches can produce notably different results. More specifically, the standard analysis revealed that a high- versus a low-load cue resulted in higher capacity and lower precision parameter estimates, suggesting the presence of a trade-off between capacity and precision. However, the more rigorous BMC analysis revealed that it was very unlikely that participants employed a behavioral strategy in which they sacrificed mnemonic precision to achieve higher storage capacity. In light of these differences, we advocate for a more stringent approach to model selection and hypothesis testing in studies implementing mixture modeling.