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Sutterer, D. W.

Publications and source records attributed to Sutterer, D. W..

2 recordsLinked to original sources

Item-specific delay activity demonstrates concurrent storage of multiple items in working memory

AbstractA longstanding view holds that information is maintained in working memory (WM) via persistent neural activity that encodes the content of WM. Recent work, however, has challenged the view that all items stored in WM are actively maintained. Instead, \"activity-silent\" models propose that items can be maintained in WM without the need for persistent neural activity, raising the possibility that only a subset of items - perhaps just a single item - may be actively represented at a given time. While past studies have successfully decoded multiple items stored in WM, these studies cannot rule out an active switching account in which only a single item is actively represented at a time. Here, we directly tested whether multiple representations can be held concurrently in an active state. We tracked spatial representations in WM using alpha-band (8-12 Hz) activity, which encodes spatial positions held in WM. Human observers (male and female) remembered one or two positions over a short delay while we recorded EEG. Using a spatial encoding model, we reconstructed stimulus-specific working memory representations (channel tuning functions, CTFs) from the scalp distribution of alphaband power. Consistent with past work, we found the selectivity of spatial CTFs was lower when two items were stored than when one item was stored. Critically, data-driven simulations revealed that the selectivity of spatial representations in the two-item condition could not be explained by models restricting storage to a single item at a time. Thus, our findings provide robust evidence for the concurrent storage of multiple items in visual working memory.\n\nAuthor SummaryWorking memory (WM) is a mental workspace where we temporarily hold information \"online\" in pursuit of our current goals. However, recent activity-silent models of WM have challenged the view that all items are held in an \"online\" state, instead proposing that only a subset of representations in WM - perhaps just one item - are represented by persistent activity at a time. To directly test a single-item model of persistent activity, we used a spatial encoding model to read out the strength of two representations from alpha-band (8-12 Hz) power in the human EEG signal. We provide direct evidence that both locations were maintained concurrently, ruling out the possibility that declines in stimulus-specific activity are due to storing one of two items in an activity-silent state.

neuroscience

Alpha-band oscillations track the retrieval of precise spatial representations from long-term memory

A hallmark of episodic memory is the phenomenon of mentally re-experiencing the details of past events, and a well-established concept is that the neuronal activity that mediates encoding is reinstated at retrieval. Evidence for reinstatement has come from multiple modalities, including functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG). These EEG studies have shed light on the time-course of reinstatement, but have been limited to distinguishing between a few categories and/or limited measures of memory strength. The goal of this work was to investigate whether recently developed experimental and technical approaches, namely an inverted encoding model applied to alpha oscillatory power in conjunction with sensitive tests of memory retrieval in a continuous space, can track and reconstruct memory retrieval of specific spatial locations. In Experiment 1, we establish that an inverted encoding model applied to multivariate alpha topography can track retrieval of precise spatial memories. In Experiment 2, we demonstrate that the pattern of multivariate alpha activity at study is similar to the pattern observed during retrieval. Finally, we observe that these encoding models predict memory retrieval behavior, including the accuracy and latency of recall. These findings highlight the broad potential for using encoding models to characterize long-term memory retrieval.

neuroscience