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  1.  29
    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 - 2021 - Cognition 206 (C):104465.
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    Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision.James J. DiCarlo, Daniel L. K. Yamins, Michael E. Ferguson, Evelina Fedorenko, Matthias Bethge, Tyler Bonnen & Martin Schrimpf - 2023 - Behavioral and Brain Sciences 46:e390.
    In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without producing alternatives. They eschew the use of public benchmarking platforms to compare vision models with the brain and behavior, and they advocate for a fragmented, phenomenon-specific modeling approach. These are unconstructive to scientific progress. We outline how the Brain-Score community is moving forward to add new model-to-human comparisons to its community-transparent suite of benchmarks.
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    There is no evidence that meaning maps capture semantic information relevant to gaze guidance: Reply to Henderson, Hayes, Peacock, and Rehrig (2021).Marek A. Pedziwiatr, Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge & Christoph Teufel - 2021 - Cognition 214 (C):104741.
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    Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision – CORRIGENDUM.James J. DiCarlo, Daniel L. K. Yamins, Michael E. Ferguson, Evelina Fedorenko, Matthias Bethge, Tyler Bonnen & Martin Schrimpf - 2024 - Behavioral and Brain Sciences 47:e66.
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