Literature DB >> 33892912

Meaning maps capture the density of local semantic features in scenes: A reply to Pedziwiatr, Kümmerer, Wallis, Bethge & Teufel (2021).

John M Henderson1, Taylor R Hayes2, Candace E Peacock3, Gwendolyn Rehrig4.   

Abstract

Pedziwiatr, Kümmerer, Wallis, Bethge, & Teufel (2021) contend that Meaning Maps do not represent the spatial distribution of semantic features in scenes. We argue that Pesziwiatr et al. provide neither logical nor empirical support for that claim, and we conclude that Meaning Maps do what they were designed to do: represent the spatial distribution of meaning in scenes.
Copyright © 2021. Published by Elsevier B.V.

Keywords:  Attention; Eye movements; Scene perception

Year:  2021        PMID： 33892912     DOI： 10.1016/j.cognition.2021.104742

Source DB:  PubMed          Journal:  Cognition        ISSN： 0010-0277


  4 in total

1.  Look at what I can do: Object affordances guide visual attention while speakers describe potential actions.

Authors:  Gwendolyn Rehrig; Madison Barker; Candace E Peacock; Taylor R Hayes; John M Henderson; Fernanda Ferreira
Journal:  Atten Percept Psychophys       Date:  2022-04-28       Impact factor: 2.157

2.  Disrupted object-scene semantics boost scene recall but diminish object recall in drawings from memory.

Authors:  Wilma A Bainbridge; Wan Y Kwok; Chris I Baker
Journal:  Mem Cognit       Date:  2021-05-24

3.  Deep saliency models learn low-, mid-, and high-level features to predict scene attention.

Authors:  Taylor R Hayes; John M Henderson
Journal:  Sci Rep       Date:  2021-09-16       Impact factor: 4.379

4.  Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps.

Authors:  Marek A Pedziwiatr; Matthias Kümmerer; Thomas S A Wallis; Matthias Bethge; Christoph Teufel
Journal:  J Vis       Date:  2022-02-01       Impact factor: 2.240
  4 in total

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