Can Humans and Machines Classify Photographs as Depicting Negation?

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.