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Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations

View ORCID ProfileMarek A. Pedziwiatr, View ORCID ProfileMatthias Kümmerer, View ORCID ProfileThomas S.A. Wallis, Matthias Bethge, View ORCID ProfileChristoph Teufel
doi: https://doi.org/10.1101/840256
Marek A. Pedziwiatr
Cardiff University, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology Cardiff, United Kingdom
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  • For correspondence: marek.pedziwi@gmail.com
Matthias Kümmerer
University of Tübingen, Center for Integrative Neuroscience, Tübingen, Germany
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Thomas S.A. Wallis
University of Tübingen, Center for Integrative Neuroscience, Tübingen, GermanyBernstein Center for Computational Neuroscience, Tübingen, Germany
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Matthias Bethge
University of Tübingen, Center for Integrative Neuroscience, Tübingen, Germany
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Christoph Teufel
Cardiff University, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology Cardiff, United Kingdom
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Abstract

Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic importance across an image, have recently been proposed to support the hypothesis that meaning rather than image features guide human gaze. MMs have the potential to be an important tool far beyond eye-movements research. Here, we examine central assumptions underlying MMs. First, we compared the performance of MMs in predicting fixations to saliency models, showing that DeepGaze II – a deep neural network trained to predict fixations based on high-level features rather than meaning – outperforms MMs. Second, we show that whereas human observers respond to changes in meaning induced by manipulating object-context relationships, MMs and DeepGaze II do not. Together, these findings challenge central assumptions underlying the use of MMs to measure the distribution of meaning in images.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 14, 2019.
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Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations
Marek A. Pedziwiatr, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge, Christoph Teufel
bioRxiv 840256; doi: https://doi.org/10.1101/840256
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Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations
Marek A. Pedziwiatr, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge, Christoph Teufel
bioRxiv 840256; doi: https://doi.org/10.1101/840256

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