Associative learning changes multivariate neural signatures of visual working memory

A hallmark of visual working memory is its sharp capacity limit, though this limit can be circumvented using learned knowledge. For example, when arrays of to-be-remembered items contain statistical regularities, people can learn the associations between items and recall more information overall (Brady et al., 2009; Ngiam et al., 2019). One proposed mechanism for how this recall benefit is achieved is through ‘memory compression’ – redundancies introduce a reduction of information per item, enabling more items to be stored online. Another proposed mechanism is that pointers are efficiently allocated to each ‘chunk’ with the benefit coming from long-term memory retrieval rather than changes to working memory itself. In an attempt to distinguish between these possibilities, we turned to an EEG measure that tracks the number of individuated items stored in working memory (mvLoad; Thyer et al., 2022). The memory compression account predicts an overall increase in the number of items stored online, whereas the long-term memory retrieval account predicts a reduction in working memory load. Subjects completed a training session where they learned specific color-color pairs. In a subsequent EEG session, subjects completed a recall task with 2 random colors, 4 random colors, or 2 learned color pairs. mvLoad analysis showed a reduction in working memory load for the 2 learned pairs condition (from 4 towards 2), consistent with the notion that an item-based pointer is assigned to each chunk. Moreover, multidimensional scaling shows an additional independent signal that distinguishes the 2 learned pairs condition from the other conditions. We propose that this additional signal reflects the involvement of long-term memory, consistent with the notion that the learned association is being relied upon to maintain the information.