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Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations
Cognition 206 (C):104465 (2021)
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Citations of this work
Meaning maps capture the density of local semantic features in scenes: A reply to Pedziwiatr, Kümmerer, Wallis, Bethge & Teufel (2021).John M. Henderson, Taylor R. Hayes, Candace E. Peacock & Gwendolyn Rehrig - 2021 - Cognition 214 (C):104742.
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.
References found in this work
Eye movements in natural behavior.Mary Hayhoe & Dana Ballard - 2005 - Trends in Cognitive Sciences 9 (4):188-194.
Computational modelling of visual attention.L. Itti & C. Koch - 2001 - Nature Reviews Neuroscience 2 (3):194-203.
Semantic override of low-level features in image viewing - both initially and overall.Marcus Nyström & Kenneth Holmqvist - 2008 - Journal of Eye Movement Research 2 (2):1-11.
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