The development of diagrammatic logics is strongly motivated by the desire to make formal reasoning accessible to broad audiences. One major research problem, for which surprisingly little progress has been made, is to understand how to choose between semantically equivalent diagrams from the perspective of human cognition. The particular focus of this paper is on choosing between diagrams that represent either the presence or absence of individuals. To understand how to best make this choice, we conducted an empirical study. We (...) found that representing the presence of individuals supported task performance either significantly better than, or no worse than, representing the absence of individuals. The particularly striking feature of our results was that representing the absence of individuals in a way that makes the diagram highly cluttered is detrimental to human cognition. As a result, diagrams with this feature should be avoided, but diagrams using presence or low-cluttered absence can be used to support cognition in the context of the tasks performed in our study. (shrink)

In this study, Knauff and Johnson-Laird's visual impedance hypothesis is applied to the domain of external representations and diagrammatic reasoning. We show that the use of real objects and augmented real objects can control human interpretation and reasoning about conditionals. As participants made inferences, they also moved objects corresponding to premises. Participants who moved real objects made more invalid inferences than those who moved AR objects and those who did not manipulate objects. Our results showed that real objects impeded conditional (...) reasoning, but AR objects did not. These findings are explained by the fact that real objects may over-specify a single state that exists, while AR objects suggest multiple possibilities. (shrink)

There is a widely held view that visual representations (images) do not depict negation, for example, as expressed by the sentence, “the train is not coming.” The present study focuses on the real-world visual representations of photographs and comic (manga) illustrations and empirically challenges the question of whether humans and machines, that is, modern deep neural networks, can recognize visual representations as expressing negation. By collecting data on the captions humans gave to images and analyzing the occurrences of negation phrases, (...) we show some evidence that humans recognize certain images as expressing negation. Furthermore, based on this finding, we examined whether or not humans and machines can classify novel images as expressing negation. The humans were able to correctly classify images to some extent, as expected from the analysis of the image captions. On the other hand, the machine learning model of image processing was only able to perform this classification at about the chance level, not at the same level of performance as the human. Based on these results, we discuss what makes humans capable of recognizing negation in visual representations, highlighting the role of the background commonsense knowledge that humans can exploit. Comparing human and machine learning performances suggests new ways to understand human cognitive abilities and to build artificial intelligence systems with more human-like abilities to understand logical concepts. (shrink)