Henry Jones¹ (), Dawei Bai², Brian Scholl², Edward Awh¹; ¹University of Chicago, ²Yale University

Recent work leveraging multivariate decoding of EEG data has identified a signal that scales with the number of items in working memory (WM). This signal appears to be content-independent, generalizing across distinct visual features, and between single-feature and multi-feature items. One explanation for this content independence is that this signal reflects an abstract indexing process that binds items to their context in space and time for maintenance and access. To explore this possibility, we examined whether a similar load signal exists for “featureless objects”, which have no enduring properties from moment to moment. On each trial, participants viewed a dense grid of crosses of random orientations. A moving object was implemented by having a cross change from one random orientation to another, with these changes propagating through space and time. These transients yield a persisting trackable object, even though (a) there is no surface feature that is constant from one frame to the next, and (b) it is impossible even in principle to identify an object in any static frame. In the actual experiment, participants viewed a set of such moving featureless objects at once, and were cued to track 1 or 2 of them. After tracking the cued item(s), there was a brief delay, and then participants had to discriminate between the true final location of an object, and a nearby alternative location. EEG decoding found a signal that scaled with the number of featureless objects and generalized between all 3 phases of the trial: cueing, tracking, and the pre-test delay. In next steps, this neural signature will be compared directly to the previously identified WM load signal. Generalization of these load signals across task contexts would provide support for the theory that spatiotemporal indexing plays a role in WM maintenance.