Maintenance and transformation of representational formats during working memory prioritization

Visual working memory depends on both material-specific brain areas in the ventral visual stream (VVS) that support the maintenance of stimulus representations and on regions in the prefrontal cortex (PFC) that control these representations. How executive control prioritizes working memory contents and whether this affects their representational formats remains an open question, however. Here, we analyzed intracranial EEG (iEEG) recordings in epilepsy patients with electrodes in VVS and PFC who performed a multi-item working memory task involving a retro-cue. We employed Representational Similarity Analysis (RSA) with various Deep Neural Network (DNN) architectures to investigate the representational format of prioritized VWM content. While recurrent DNN representations matched PFC representations in the beta band (15-29 Hz) following the retro-cue, they corresponded to VVS representations in a lower frequency range (3-14 Hz) towards the end of the maintenance period. Our findings highlight the distinct coding schemes and representational formats of prioritized content in VVS and PFC.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2024-09-19
DOI
https://doi.org/10.1038/s41467-024-52541-w
Citations
13
Primary Topic
Neural and Behavioral Psychology Studies
Type
article
Field-Weighted Citation Impact
4.11

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Maintenance and transformation of representational formats during working memory prioritization

Gui Xue, Daniel Pacheco, Armin Brandt, Shuang Wang et al.
13 citations
Nature Communications
Neural and Behavioral Psychology Studies
4.11
article

Maintenance and transformation of representational formats during working memory prioritization

Gui Xue, Daniel Pacheco, Armin Brandt, Shuang Wang, Charlotte Roy, Marie-Christin Fellner, Jing Liu, Peter Christoph Reinacher, Nikolai Axmacher, Linglin Yang, Andreas Schulze‐Bonhage, Lukas Kunz, Hui Zhang
article en
13 citations

Abstract

Visual working memory depends on both material-specific brain areas in the ventral visual stream (VVS) that support the maintenance of stimulus representations and on regions in the prefrontal cortex (PFC) that control these representations. How executive control prioritizes working memory contents and whether this affects their representational formats remains an open question, however. Here, we analyzed intracranial EEG (iEEG) recordings in epilepsy patients with electrodes in VVS and PFC who performed a multi-item working memory task involving a retro-cue. We employed Representational Similarity Analysis (RSA) with various Deep Neural Network (DNN) architectures to investigate the representational format of prioritized VWM content. While recurrent DNN representations matched PFC representations in the beta band (15-29 Hz) following the retro-cue, they corresponded to VVS representations in a lower frequency range (3-14 Hz) towards the end of the maintenance period. Our findings highlight the distinct coding schemes and representational formats of prioritized content in VVS and PFC.

Nature CommunicationsVol. 15(1)
Hong Kong Polytechnic University (HK), McGovern Institute for Brain Research (US), University Hospital Bonn (DE), University Medical Center Freiburg (DE), Chinese Institute for Brain Research (CN), Fraunhofer Institute for Laser Technology (DE), Second Affiliated Hospital of Zhejiang University (CN), Ruhr University Bochum (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 5%
Neural and Behavioral Psychology Studies
4.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.