Dynamic updating of working memory resources for visual objects.

Recent neurophysiological and imaging studies have investigated how neural representations underlying working memory (WM) are dynamically updated for objects presented sequentially. Although such studies implicate information encoded in oscillatory activity across distributed brain networks, interpr...

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Autors principals: Gorgoraptis, N, Catalao, R, Bays, P, Husain, M
Format: Journal article
Idioma:English
Publicat: 2011
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Sumari:Recent neurophysiological and imaging studies have investigated how neural representations underlying working memory (WM) are dynamically updated for objects presented sequentially. Although such studies implicate information encoded in oscillatory activity across distributed brain networks, interpretation of findings depends crucially on the underlying conceptual model of how memory resources are distributed. Here, we quantify the fidelity of human memory for sequences of colored stimuli of different orientation. The precision with which each orientation was recalled declined with increases in total memory load, but also depended on when in the sequence it appeared. When one item was prioritized, its recall was enhanced, but with corresponding decrements in precision for other objects. Comparison with the same number of items presented simultaneously revealed an additional performance cost for sequential display that could not be explained by temporal decay. Memory precision was lower for sequential compared with simultaneous presentation, even when each item in the sequence was presented at a different location. Importantly, stochastic modeling established this cost for sequential display was due to misbinding object features (color and orientation). These results support the view that WM resources can be dynamically and flexibly updated as new items have to be stored, but redistribution of resources with the addition of new items is associated with misbinding object features, providing important constraints and a framework for interpreting neural data.