@inproceedings{c4722e4f78684531a3d2fc5a51a2a42e,
title = "Can Humans and Machines Classify Photographs as Depicting Negation?",
abstract = "How logical concepts such as negation can be visually represented is of central importance in the study of diagrammatic reasoning. To explore various ways in which negation can be visually represented, this study focuses on photographs as instances of purely visual representations. We use real-world photographic image data and study how well humans can classify those images as depicting negation. We also compare the human performance with a state-of-the-art machine (deep) learning model on this classification task. The present paper gives some preliminary results on our data-driven analyses.",
keywords = "Cognitive science, Machine learning, Negation, Photograph",
author = "Yuri Sato and Koji Mineshima",
note = "Funding Information: This work was supported by JSPS KAKENHI Grant Number JP20K12782 to the first author. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 12th International Conference on the Theory and Application of Diagrams, Diagrams 2021 ; Conference date: 28-09-2021 Through 30-09-2021",
year = "2021",
doi = "10.1007/978-3-030-86062-2_35",
language = "English",
isbn = "9783030860615",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "348--352",
editor = "Amrita Basu and Gem Stapleton and Sven Linker and Catherine Legg and Emmanuel Manalo and Petrucio Viana",
booktitle = "Diagrammatic Representation and Inference - 12th International Conference, Diagrams 2021, Proceedings",
}