Abstract
Previous research suggested that filtering efficiency (the ability to ignore task irrelevant items) might explain individual differences in Visual Working Memory (VWM) capacity, such that high-capacity individuals have better control over VWM limited workspace by encoding and maintaining only task relevant information. Here, we investigated possible compensation mechanisms for improving filtering performance in VWM. Specifically, we investigated whether an identity cue that singled the upcoming appearance of distractors among targets, a spatial cue marking the locations of distractors (placeholders), or a combination of both cues can improve filtering performance in VWM. Participants performed a change-detection task in which they had to memorize the color of the targets with either three targets, six targets, or three targets and three distractors (the filtering condition). In Experiment 1 we used an identity cue, in Experiment 2 we used placeholders to mark the locations of the distractors and held these locations fixed throughout the experiment, and in Experiment 3 we used both cues. We found that each cue alone was not sufficient to improve filtering performance, but using both cues did improve filtering performance. In Experiment 4, manipulating the preparation interval prior to a filtering trial did not further improve filtering performance. In addition, in Experiment 5 a spatial cue alone was able to ameliorate filtering performance as long as the locations of the distractors were randomly changed throughout the experiment. Furthermore, in Experiment 6, removing the placeholders but keeping the distractors locations fixed was not sufficient for the identity cue to improve filtering performance. These findings suggest that when the spatial variability of the distractors is low, only interactions between high processes such as executive functions and low processes such as spatial separation between the distractors and the targets, can improve filtering ability in VWM.
Meeting abstract presented at VSS 2016