Abstract
How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.
Highlights
Covert visual attention—applied according to spatial location or visual features—has been shown repeatedly to enhance performance on challenging visual tasks (Carrasco, 2011)
A ’retinotopic’ layout is built into the structure of the network, and the same visual features are represented across that retinotopy
This network was explored in (Gucluand van Gerven, 2015), where it was shown that early convolutional layers of this convolutional neural networks (CNNs) are best at predicting activity of voxels in V1, while late convolutional layers are best at predicting activity of voxels in the object-selective lateral occipital area (LO)
Summary
Covert visual attention—applied according to spatial location or visual features—has been shown repeatedly to enhance performance on challenging visual tasks (Carrasco, 2011). To explore the neural mechanisms behind this enhancement, neural responses to the same visual input are compared under different task conditions Such experiments have identified numerous neural modulations associated with attention, including changes in firing rates, noise levels, and correlated activity (Treue, 2001; Cohen and Maunsell, 2009; Fries et al, 2001; Maunsell and Cook, 2002). How do these neural activity changes impact performance? We use a deep convolutional neural network trained to perform object classification to test effects of the feature similarity gain model of attention (Treue and Martınez Trujillo, 1999)
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