The contralateral delay activity (CDA) is a negative slow wave sensitive to the number of objects maintained in visual working memory (VWM). In recent years, a growing number of labs started to use the CDA in order to investigate VWM, leading to many fascinating discoveries. Here, we discuss the recent developments and contribution of the CDA in various research fields. Importantly, we report two meta-analyses that unequivocally validate the relationship between the set-size increase in the CDA amplitude and the individual VWM capacity, and between the CDA and filtering efficiency. We further discuss how the CDA was used to study the role of VWM in visual search, multiple object tracking, grouping, binding, and whether VWM capacity allocation is determined by the items’ resolution or instead by the number of objects regardless of their complexity. In addition, we report how the CDA has been used to characterize specific VWM deficits in special populations.
What makes an integrated object in visual working memory (WM)? Past evidence suggested that WM holds all features of multidimensional objects together, but struggles to integrate color-color conjunctions. This difficulty was previously attributed to a challenge in same-dimension integration, but here we argue that it arises from the integration of 2 distinct objects. To test this, we examined the integration of distinct different-dimension features (a colored square and a tilted bar). We monitored the contralateral delay activity, an event-related potential component sensitive to the number of objects in WM. The results indicated that color and orientation belonging to distinct objects in a shared location were not integrated in WM (Experiment 1), even following a common fate Gestalt cue (Experiment 2). These conjunctions were better integrated in a less demanding task (Experiment 3), and in the original WM task, but with a less individuating version of the original stimuli (Experiment 4). Our results identify the critical factor in WM integration at same- versus separate-objects, rather than at same- versus different-dimensions. Compared with the perfect integration of an object's features, the integration of several objects is demanding, and depends on an interaction between the grouping cues and task demands, among other factors.
In the present study, we examined how real-world objects are represented in long-term memory. Two contrasting views exist with regard to this question: one argues that real-world objects are represented as a set of independent features, and the other argues that they form bound integrate representations. In 5 experiments, we tested the different predictions of each view, namely whether the different features of real-world items are remembered and forgotten independently from each other, in a feature-based manner, or conversely are stored and lost in an object-based manner, with all features depending upon each other. Across various stimuli, learning tasks (incidental or explicit), experimental setups (within- or between-subjects design), feature-dimensions, and encoding times, we consistently found that information is forgotten in an object-based manner. When an object ceases to be fully remembered, all of its features are lost, instead of only some of the object’s features being lost whereas other features are still remembered. Furthermore, we found support for a strong form of dependency among the different features, namely a hierarchical structure. We conclude that visual long-term memory is object-based, challenging previous findings.
Visual working memory (VWM) guides behavior by holding a set of active representations and modifying them according to changes in the environment. This updating process relies on a unique mapping between each VWM representation and an actual object in the environment. Here, we destroyed this mapping by either presenting a coherent object but then breaking it into independent parts or presenting an object but then abruptly replacing it with a different object. This allowed us to introduce the neural marker and behavioral consequence of an online resetting process in humans' VWM. Across seven experiments, we demonstrate that this resetting process involves abandoning the old VWM contents because they no longer correspond to the objects in the environment. Then, VWM encodes the novel information and reestablishes the correspondence between the new representations and the objects. The resetting process was marked by a unique neural signature: a sharp drop in the amplitude of the electrophysiological index of VWM contents (the contralateral delay activity), presumably indicating the loss of the existent object-to-representation mappings. This marker was missing when an updating process occurred. Moreover, when tracking moving items, VWM failed to detect salient changes in the object's shape when these changes occurred during the resetting process. This happened despite the object being fully visible, presumably because the mapping between the object and a VWM representation was lost. Importantly, we show that resetting, its neural marker, and the behavioral cost it entails, are specific to situations that involve a destruction of the objects-to-representations correspondence.
In three experiments we manipulated the resolution of novel complex objects in visual working memory (WM) by changing task demands. Previous studies that investigated the trade-off between quantity and resolution in visual WM yielded mixed results for simple familiar stimuli. We used the contralateral delay activity as an electrophysiological marker to directly track the deployment of visual WM resources while participants preformed a change-detection task. Across three experiments we presented the same novel complex items but changed the task demands. In Experiment 1 we induced a medium resolution task by using change trials in which a random polygon changed to a different type of polygon and replicated previous findings showing that novel complex objects are represented with higher resolution relative to simple familiar objects. In Experiment 2 we induced a low resolution task that required distinguishing between polygons and other types of stimulus categories, but we failed in finding a corresponding decrease in the resolution of the represented item. Finally, in Experiment 3 we induced a high resolution task that required discriminating between highly similar polygons with somewhat different contours. This time, we observed an increase in the item’s resolution. Our findings indicate that the resolution for novel complex objects can be increased but not decreased according to task demands, suggesting that minimal resolution is required in order to maintain these items in visual WM. These findings support studies claiming that capacity and resolution in visual WM reflect different mechanisms.
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