Abstract
Attention can be directed at features and feature dimensions to facilitate perception. Here, we investigated whether feature-based-attention (FBA) can also dynamically weight feature-specific representations within multi-feature objects held in visual working memory (VWM). Across three experiments, participants retained coloured arrows in working memory and, during the delay, were cued to either the colour or the orientation dimension. We show that directing attention towards a feature dimension (1) improves the performance in the cued feature dimension at the expense of the uncued dimension, (2) is more efficient if directed to the same rather than to different dimensions for different objects, and (3) at least for colour, automatically spreads to the colour representation of non-attended objects in VWM. We conclude that FBA also continues to operate on VWM representations (with similar principles that govern FBA in the perceptual domain) and challenge the classical view that VWM representations are stored solely as integrated objects.
Highlights
IntroductionThat is, processing of an attended feature attribute[24,25] or dimension[26,27,28] is enhanced throughout the visual field, independent of the spatial focus of attention
An interesting characteristic of feature-based attention (FBA) in the perceptual realm is its global nature
The response-deviation density plots in Fig. 1d reveal that the uncued dimension was not dropped, but instead that the effects of FBA retro-cues in visual working memory (VWM) operate in a subtle manner on the fidelity of representations. These results indicate that FBA can enhance VWM representations in the more relevant feature dimension, and suggest that this occurs at the expense of the less relevant feature dimension
Summary
That is, processing of an attended feature attribute[24,25] or dimension[26,27,28] is enhanced throughout the visual field, independent of the spatial focus of attention. We hypothesized that the influence of FBA in VWM may be a global influence that will automatically spread to other (uncued) objects, with regard to their feature representations in the same dimension. FBA in VWM is most efficient if directed within the same dimension for different objects. Feature dimension weighting spreads automatically to non-attended objects in VWM. To this end, we employed a series of VWM tasks that deployed FBA retro-cues and required a continuous reproduction of the colour or orientation of probed objects
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