Visual spatial attention

Résumé

Visual spatial attention is a form of visual attention that involves directing attention to a location in space. Similar to its temporal counterpart visual temporal attention, these attention modules have been widely implemented in video analytics in computer vision to provide enhanced performance and human interpretable explanation of deep learning models. Spatial attention allows humans to selectively process visual information through prioritization of an area within the visual field. A region of space within the visual field is selected for attention and the information within this region then receives further processing. Research shows that when spatial attention is evoked, an observer is typically faster and more accurate at detecting a target that appears in an expected location compared to an unexpected location. Attention is guided even more quickly to unexpected locations, when these locations are made salient by external visual inputs (such as a sudden fl

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https://en.wikipedia.org/wiki/Visual_spatial_attention

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Leveraging Spiking Deep Neural Networks to Understand the Neural Mechanisms Underlying Selective Attention

Davide Zambrano

Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of precision (internal noise suppression) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neuron's input or on its outgoing connection. We show that modulating the connection is most effective in selectively enhancing information processing by redistributing spiking activity and by introducing additional task-relevant information, as shown by representational similarity analyses. Precision only produced minor attentional effects in performance. Our results, which mirror empirical findings, show that it is possible to adjudicate between attention mechanisms using more biologically realistic models and natural stimuli.

MIT PRESS2022

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Attentional shifts precede most of the perceptual processes, and therefore their recognition may constitute an interesting process for both the study of human perception as well as the design of EEG-based Brain Computer Interfaces (BCI). Several studies have focused on EEG modulations of spatial attention, pointing out to changes in both Alpha and Gamma bands (Thut et al. 2006 J. Neurosci 26:9494; Tallon-Baudry and Bertrand 1999 T Cogn Sci 3:151; Jensen et al. 2007 TINS 30:317). This work aims at further characterization of EEG correlates of attention shifts to evaluate their potential use in BCI applications. We recorded 32-channel scalp EEG while subjects covertly shift their attention towards one out of several spatial targets. Consistently with previous studies, characterization of the spatio-temporal patterns using time-frequency analysis (Morlet wavelet transformation), shows main modulations in the alpha and gamma frequency bands. Moreover, it is also shown that these modulations are characterized by intermittent episodes of activity (Palix et al. 2006 Perception 25:234). Furthermore, we assess the EEG-based recognition of spatial attention -time and target location of the shift- using a machine learning approach based on the detection of informative, episodic activity. The proposed approach is based on the hypothesis that recognition may be improved by identifying the moments where the underlying neural phenomena is more salient (e. g. the appearance of episodic oscillations related to attentional shifts), and performing the classification based mainly -if not exclusively- on the activity during these periods. Our results show accurate recognition of attentional shifts (>75%) based on the scalp EEG signal. Moreover, the proposed approach allow us to efficiently detect endogenous processes loosely synchronized to external events. These results provides evidence supporting the feasibility of using attentional processes in Brain-Computer Interfaces.

2008

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Modelling human visual attention is of great importance in the field of computer vision and has been widely explored for 3D imaging. Yet, in the absence of ground truth data, it is unclear whether such predictions are in alignment with the actual human viewing behavior in virtual reality environments. In this study, we work towards solving this problem by conducting an eye-tracking experiment in an immersive 3D scene that offers 6 degrees of freedom. A wide range of static point cloud models is inspected by human subjects, while their gaze is captured in real-time. The visual attention information is used to extract fixation density maps, that can be further exploited for saliency modelling. To obtain high quality fixation points, we devise a scheme that utilizes every recorded gaze measurement from the two eye-cameras of our set-up. The obtained fixation density maps together with the recorded gaze and head trajectories are made publicly available, to enrich visual saliency datasets for 3D models.

IEEE

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