September 2011
Volume 11, Issue 11
Vision Sciences Society Annual Meeting Abstract  |   September 2011
A Biased-Competition Account of Visual Working Memory Performance
Author Affiliations
  • Stephen M. Emrich
    Department of Psychology, University of Toronto
  • Susanne Ferber
    Department of Psychology, University of Toronto
    Rotman Research Institute, Baycrest
Journal of Vision September 2011, Vol.11, 1245. doi:
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      Stephen M. Emrich, Susanne Ferber; A Biased-Competition Account of Visual Working Memory Performance. Journal of Vision 2011;11(11):1245. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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One of the most successful models of how visual information is processed by the human brain is the biased-competition model of attention (Desimone & Duncan, 1995) which posits that multiple objects within a receptive field compete for representation, and that attention serves to bias neural responses towards some objects over others. To date, few studies have investigated how visual working memory (VWM) performance is affected by these competitive interactions. Here, we presented participants with memory displays in low- or high-competition configurations by manipulating the distance between objects. Participants responded by selecting from a colour-wheel the colour of the probed location. Using a three-component model described by Bays and colleagues (Bays, Catalao, & Husain, 2009), we describe the effects of competition on the number and precision of VWM representations. That is, in addition to examining correct responses, the proportion of non-target errors was examined. The results demonstrate that the fidelity of VWM responses is negatively affected by competitive interactions, as response precision decreases when the competition between sample items increases (i.e., when they are presented close together). Specifically, competitive interactions increase the number of non-target errors, but without affecting the number of targets correctly reported. Thus, increasing the competition between items appears to increase the amount of response error by biasing responses towards non-target items, without affecting the number of items stored in VWM. Furthermore, we demonstrate that target responses can benefit from attentional cues, indicating that bias signals can support VWM encoding and maintenance by resolving competition. Interestingly, the precision of reporting a single object is strongly correlated with VWM capacity, suggesting that VWM capacity may be related to the quality of perceptual representations. These results provide a novel framework for understanding performance limitations in VWM.


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