[en] Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine-learning approach to determine more directly the extent to which common neural patterns characterize retention in verbal WM and visual WM. Verbal WM was assessed via a standard delayed probe recognition task for letter sequences of variable length. Visual WM was assessed via a visual array WM task involving the maintenance of variable amounts of visual information in the focus of attention. We trained a classifier to distinguish neural activation patterns associated with high- and low-visual WM load and tested the ability of this classifier to predict verbal WM load (high–low) from their associated neural activation patterns, and vice versa. We observed significant between-task prediction of load effects during WM maintenance, in posterior parietal and superior frontal regions of the dorsal attention network; in contrast, between-task prediction in sensory processing cortices was restricted to the encoding stage. Furthermore, between-task prediction of load effects was strongest in those participants presenting the highest capacity for the visual WM task. This study provides novel evidence for common, attention-based neural patterns supporting verbal and visual WM.
Disciplines :
Neurosciences & behavior
Author, co-author :
Majerus, Steve ; Université de Liège - ULiège > Département de Psychologie : cognition et comportement > Psychopathologie cognitive
Cowan, Nelson
Peters, Frédéric ; Université de Liège - ULiège > Département de Psychologie : cognition et comportement > Psychopathologie cognitive
Van Calster, Laurens ; Université de Liège - ULiège > Département de Psychologie : cognition et comportement > Psychopathologie cognitive
Phillips, Christophe ; Université de Liège - ULiège > Centre de recherches du cyclotron
Schrouff, Jessica ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Cross-Modal Decoding of Neural Patterns Associated with Working Memory: Evidence for Attention-Based Accounts of Working Memory
Publication date :
January 2016
Journal title :
Cerebral Cortex
ISSN :
1047-3211
eISSN :
1460-2199
Publisher :
Oxford University Press, Cary, United States - North Carolina
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