No trade-off between the use of space and time for working memory
de Vries, E.; Fejer, G.; van Ede, F.
Show abstract
Space and time can each act as scaffolds for the individuation and selection of visual objects in working memory. Here we ask whether there is a trade-off between the use of space and time for visual working memory: whether observers will rely less on space, when memoranda can additionally be individuated through time. We tracked the use of space through directional biases in microsaccades after attention was directed to memory contents that had been encoded simultaneously or sequentially to the left and right of fixation. We found that spatial gaze biases were preserved when participants could (Experiment 1) and even when they had to (Experiment 2) additionally rely on time for object individuation. Thus, space remains a profound organizing medium for working memory even when other organizing sources are available and utilised, with no evidence for a trade-off between the use of space and time. SIGNIFICANCE STATEMENTSpace and time provide two foundational dimensions that govern not only our sensations and actions, but also the organisation of internal representations in working memory. Space and time have each been shown to provide an automatic organising principle - or scaffold - for memory retention. We uniquely address whether there is a trade-off between the use of space and time for working memory. We show that the profound and automatic reliance on memorised space is preserved not only when time can, but even when time has to be used for individuation and selection of memory contents. This shows there is no trade-off between spatial and temporal codes available for memory organisation, advancing our understanding of the spatial-temporal architecture of mind.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.