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Attention allows us to select relevant and ignore irrelevant information from our complex environments. What happens when attention shifts from one item to another? To answer this question, it is critical to have tools that accurately recover neural representations of both feature and location information with high temporal resolution. In the present study, we used human electroencephalography (EEG) and machine learning to explore how neural representations of object features and locations update across dynamic shifts of attention. We demonstrate that EEG can be used to create simultaneous time courses of neural representations of attended features (time point-by-time point inverted encoding model reconstructions) and attended location (time point-by-time point decoding) during both stable periods and across dynamic shifts of attention. Each trial presented two oriented gratings that flickered at the same frequency but had different orientations; participants were cued to attend one of them and on half of trials received a shift cue midtrial. We trained models on a stable period from Hold attention trials and then reconstructed/decoded the attended orientation/location at each time point on Shift attention trials. Our results showed that both feature reconstruction and location decoding dynamically track the shift of attention and that there may be time points during the shifting of attention when 1) feature and location representations become uncoupled and 2) both the previously attended and currently attended orientations are represented with roughly equal strength. The results offer insight into our understanding of attentional shifts, and the noninvasive techniques developed in the present study lend themselves well to a wide variety of future applications. NEW & NOTEWORTHY We used human EEG and machine learning to reconstruct neural response profiles during dynamic shifts of attention. Specifically, we demonstrated that we could simultaneously read out both location and feature information from an attended item in a multistimulus display. Moreover, we examined how that readout evolves over time during the dynamic process of attentional shifts. These results provide insight into our understanding of attention, and this technique carries substantial potential for versatile extensions and applications.
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Cost of maintaining attentional templates for multiple colors revealed by EEG decoding
Abstract Attention to a feature enhances the sensory representation of that feature. However, it is less clear whether attentional modulation is limited when needing to attend to multiple features. Here, we studied both the behavioral and neural correlates of the attentional limit by examining the effectiveness of attentional enhancement of one versus two color features. We recorded electroencephalography (EEG) while observers completed a color-coherence detection task in which they detected a weak coherence signal, an over-representation of a target color. Before stimulus onset, we presented either one or two valid color cues. We found that, on the one-cue trials compared with the two-cue trials, observers were faster and more accurate, indicating that observers could more effectively attend to a single color at a time. Similar behavioral deficits associated with attending to multiple colors were observed in a pre-EEG practice session with one-, two-, three-, and no-cue trials. Further, we were able to decode the target color using the EEG signals measured from the posterior electrodes. Notably, we found that decoding accuracy was greater on the one-cue than on two-cue trials, indicating a stronger color signal on one-cue trials likely due to stronger attentional enhancement. Lastly, we observed a positive correlation between the decoding effect and the behavioral effect comparing one-cue and two-cue trials, suggesting that the decoded neural signals are functionally associated with behavior. Overall, these results provide behavioral and neural evidence pointing to a strong limit in the attentional enhancement of multiple features and suggest that there is a cost in maintaining multiple attentional templates in an active state.
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- Award ID(s):
- 2019995
- PAR ID:
- 10587350
- Publisher / Repository:
- DOI PREFIX: 10.1162
- Date Published:
- Journal Name:
- Imaging Neuroscience
- Volume:
- 3
- ISSN:
- 2837-6056
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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