ArticlePLoS computational biology2022
Priority-based transformations of stimulus representation in visual working memory.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Past and present goals are represented concurrently during visual search.PLoS biology · 2026Article
- Long-term effects of working memory retrieval from prioritized and deprioritized states.Communications psychology · 2026Article
- Sensory reformatting for a working visual memory.Trends in cognitive sciences · 2025Review
- Article
- Neural codes track prior events in a narrative and predict subsequent memory for details.Communications psychology · 2025Article
- The representation of abstract goals in working memory is supported by task-congruent neural geometry.PLoS biology · 2024Article
- The human posterior parietal cortices orthogonalize the representation of different streams of information concurrently coded in visual working memory.PLoS biology · 2024Article
- A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection.Nature communications · 2024Article
- Maintenance and transformation of representational formats during working memory prioritization.Nature communications · 2024Article
- Single-pulse Transcranial Magnetic Stimulation Affects Working-memory Performance via Posterior Beta-band Oscillations.Journal of cognitive neuroscience · 2024Article
- A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection.bioRxiv : the preprint server for biology · 2024Article
- Representation and computation in visual working memory.Nature human behaviour · 2024Review
- Representing Context and Priority in Working Memory.Journal of cognitive neuroscience · 2024Article
- Internal Representations Are Prioritized by Frontoparietal Theta Connectivity and Suppressed by alpha Oscillation Dynamics: Evidence from Concurrent Transcranial Magnetic Stimulation EEG and Invasive EEG.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Article
- Neural population dynamics of human working memory.Current biology : CB · 2023Article
- Strategic control of location and ordinal context in visual working memory.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Predicted utility modulates working memory fidelity in the brain.Cortex; a journal devoted to the study of the nervous system and behavior · 2023Article
Corrections and comments
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3 authors.
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Abstract
How does the brain prioritize among the contents of working memory (WM) to appropriately guide behavior? Previous work, employing inverted encoding modeling (IEM) of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) datasets, has shown that unprioritized memory items (UMI) are actively represented in the brain, but in a "flipped", or opposite, format compared to prioritized memory items (PMI). To acquire independent evidence for such a priority-based representational transformation, and to explore underlying mechanisms, we trained recurrent neural networks (RNNs) with a long short-term memory (LSTM) architecture to perform a 2-back WM task. Visualization of LSTM hidden layer activity using Principal Component Analysis (PCA) confirmed that stimulus representations undergo a representational transformation-consistent with a flip-while transitioning from the functional status of UMI to PMI. Demixed (d)PCA of the same data identified two representational trajectories, one each within a UMI subspace and a PMI subspace, both undergoing a reversal of stimulus coding axes. dPCA of data from an EEG dataset also provided evidence for priority-based transformations of the representational code, albeit with some differences. This type of transformation could allow for retention of unprioritized information in WM while preventing it from interfering with concurrent behavior. The results from this initial exploration suggest that the algorithmic details of how this transformation is carried out by RNNs, versus by the human brain, may differ.
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