DEEP LEARNING NEURAL NETWORKS AS A MODEL OF SACCADIC GENERATION
Sofia Krasovskaya (),
Georgiy Zhulikov () and
Joseph MacInnes ()
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Sofia Krasovskaya: National Research University Higher School of Economics
Georgiy Zhulikov: National Research University Higher School of Economics
Joseph MacInnes: National Research University Higher School of Economics
No WP BRP 93/PSY/2018, HSE Working papers from National Research University Higher School of Economics
Approximately twenty years ago, Laurent Itti and Christof Koch created a model of saliency in visual attention in an attempt to recreate the work of biological pyramidal neurons by mimicking neurons with centre-surround receptive fields. The Saliency Model has launched many studies that contributed to the understanding of layers of vision and the sphere of visual attention. The aim of the current study is to improve this model by using an artificial neural network that generates saccades similar to how humans make saccadic eye movements. The proposed model uses a Leaky Integrate-and-Fire layer for temporal predictions, and replaces parallel feature maps with a deep learning neural network in order to create a generative model that is precise for both spatial and temporal predictions. Our deep neural network was able to predict eye movements based on unsupervised learning from raw image input, as well as supervised learning from fixation maps retrieved during an eye-tracking experiment conducted with 35 participants at later stages in order to train a 2D softmax layer. The results imply that it is possible to match the spatial and temporal distributions of the model to spatial and temporal human distributions.
Keywords: saccade generation; salience model; deep learning neural network; visual search; leaky integrate and fire (search for similar items in EconPapers)
JEL-codes: Z (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big and nep-cmp
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Published in WP BRP Series: Science, Psychology / PSY, October 2018, pages 1-14
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Persistent link: https://EconPapers.repec.org/RePEc:hig:wpaper:93psy2018
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