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Introduction: Paper folding and punched hole tests are used to measure spatial abilities in humans. These abilities are relevant since they are associated with success in STEM (Science, Technology, Engineering, and Mathematics). This study addresses the challenge of teaching spatial reasoning skills using an educational videogame, the Paper Folding Reasoning Game. Methods: The Paper Folding Reasoning Game is an interactive game which presents activities intended to help users train and understand how to fold a paper to get a specific shape (Part I) and the consequence of punching a hole on a folded paper (Part II). This educational videogame can automatically generate paper-folding-and-punched-hole questions with varying degrees of difficulty depending on the number of folds and holes made, thus producing additional levels for training due to its embedded reasoning mechanisms (Part III). Results: This manuscript presents the results of analyzing the gameplay data gathered by the Paper Folding Reasoning Game in its three parts. For Parts I and II, the data provided by 225 anonymous unique players are analyzed. For Part III (Mastermode), the data obtained from 894 gameplays by 311 anonymous unique players are analyzed. In our analysis, we found out a significant difference in performance regarding the players who trained (i.e., played Parts I and II) before playing the Mastermode (Part III) vs. the group of players who did not train. We also found a significant difference in players' performance who used the visual help (i.e., re-watch the animated sequence of paper folds) vs. the group of players who did not use it, confirming the effectiveness of the Paper Folding Reasoning Game to train paper-folding-and-punched-hole reasoning skills. Statistically significant gender differences in performance were also found.

This paper presents the art painting style explainable classifier named ANYXI. The classifier is based on art specialists’ knowledge of art styles and human-understandable color traits. ANYXI overcomes the principal flaws in the few art painting style classifiers in the literature. In this way, we first propose, using the art specialists’ studies, categorizations of the Baroque, Impressionism, and Post-Impressionism. Second, we carry out a human survey with the aim of validating the appropriateness of the color features used in the categorizations for human understanding. Then, we analyze and discuss the accuracy and interpretability of the ANYXI classifier. The study ends with an evaluation of the rationality of explanations automatically generated by ANYXI. We enrich the discussion and empirical validation of ANYXI by considering a quantitative and qualitative comparison versus other explainable classifiers. The reported results show how ANYXI is outstanding from the point of viewof interpretability while keeping high accuracy (comparable to non-explainable classifiers). Moreover, automated generations are endowed with a good level of rationality.

Everyday life takes place in space and time, and spatial experience lies at the heart of our existence. Understanding how we conceive spatial relations, and how we solve spatio-temporal problems, is therefore key to understanding human cognition. Spatial cognition research has advanced considerably over the past decades, with major successes particularly in computational implementations of knowledge representation and reasoning methods. Still, a range of key issues continue to pose major challenges. The goal of this report is to discuss the various options for the formalisation, implementation and automated solution of spatial problems including the following issues: the identification and specification of relevant concepts as expressed in human language; modules for automated understanding of domain descriptions; the use of spatial structures and affordances for direct spatial problem solving; and, the development of efficient planning systems capable of providing feasible solutions to spatial problems. This report documents the program and the outcomes of Dagstuhl Seminar 21492 "Representing and Solving Spatial Problems".

This paper presents an approach for analysing food-porn images and their related comments published by the cooking school Getcookingcanada Instagram account. Our approach processes the published images to extract colour parameters, counts the number of likes, and also analyses the comments related to each publication. A dataset containing all these was built, and methods were applied to study correlations among the data: a regression analysis, an ANOVA and a sentiment analysis of the comments on the dataset to explain the relation between the quantity of likes and the sentiment obtained from the food images. Our results show a correlation between the number of likes and the sentiment analysis of the comments. Images that evoke a positive sentiment have a higher number of likes and comments. Users’ experience on creating posts is also analysed and confirms a positive correlation between the number of likes and the publisher’s experience

Within the realm of new robotics, researchers have placed a great amount of effort into learning, understanding, and representing knowledge for task execution by robots. The goal is to develop robots that can help humans with daily tasks. Cognitive robots need to explore and understand their environment, choose a safe and human-aware course of action, and learn—not only from experience, but also through interaction.

This paper presents a logical Style painting classifier based on evaluated Horn clauses, qualitative colour descriptors and Explanations (ℓ-SHE). Three versions of ℓ-SHE are defined, using rational Pavelka logic (RPL), and expansions of Gödel logic and product logic with rational constants: RPL, G(Q) and ∩ (Q), respectively. We introduce a fuzzy representation of the more representative colour traits for the Baroque, the Impressionism and the Post-Impressionism art styles. The ℓ-SHE algorithm has been implemented in Swi-Prolog and tested on 90 paintings of the QArt-Dataset and on 247 paintings of the Paintings-91-PIB dataset. The percentages of accuracy obtained in the QArt-Dataset for each ℓ-SHE version are 73.3% (RPL), 65.6% (G(Q)) and 68.9% (∩ (Q)). Regarding the Paintings-91-PIB dataset, the percentages of accuracy obtained for each ℓ-SHE version are 60.2% (RPL), 48.2% (G(Q)) and 57.0% (∩ (Q)). Our logic definition for the Baroque style has obtained the highest accuracy in both datasets, for all the ℓ-SHE versions (the lowest Baroque case gets 85.6% of accuracy). An important feature of the classifier is that it provides reasons regarding why a painting belongs to a certain style. The classifier also provides reasons about why outliers of one art style may belong to another art style, giving a second classification option depending on its membership degrees to these styles.

This special issue proposes a space for researchers to discuss the challenges of bridging pattern recognition and cognitive assistants. This bridge was travelled in both sides. From one side, how pattern recognition methods can help intelligent systems to become more cognitive. And from the other side, how cognitive models can improve pattern recognition methods so that the final system can better assist users. So, this special issue presents research work on these topics, aiming to observe their interrelations in order to create theoretical approaches, methodologies and computational tools to advance work on cognitive assistants and pattern recognition.

Computational models of novel concept understanding and creativity are addressed in this paper from the viewpoint of conceptual blending theory (CBT). In our approach, a novel, unknown concept is addressed in a communication setting, where this novel concept, cre- ated as a blend by an emitter agent, sends a communicative object (words, or in this paper, a visual representation of that concept) to another agent. When received by a computational agent, a novel concept for that communicative object can only be understood by blend- ing concepts already known by that agent. In this paper, we first posit that understanding new concepts via blending is also a creative process. Albeit different from generating con- ceptual blends, understanding a novel concept via blending involves the disintegration and decompression of that novel concept, in such a way that the receiver of that concept is able to re-create the conceptual network supposedly intended by the emitter of the novel con- cept. Secondly, we also propose image schemas as a tool that agents can use to interpret the spatial information obtained when disintegrating/unpacking novel concepts and then re- create the new blend. This process is studied in a communication setting where semiotics and meaning are conveyed by visual and spatial signs (instead of the usual setting of natural language or text). In this case study, qualitative spatial descriptors are applied for obtaining a formal description of an icon or pictogram, which is later assigned a meaning by a process of conceptual blending using image schemas.

© 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0, This special issue proposes a space for researchers to discuss the challenges of bridging pattern recognition and cognitive assistants. This bridge was travelled in both sides. From one side, how pattern recognition methods can help intelligent systems to become more cognitive. And from the other side, how cognitive models can improve pattern recognition methods so that the final system can better assist users. So, this special issue presents research work on these topics, aiming to observe their interrelations in order to create theoretical approaches, methodologies and computational tools to advance work on cognitive assistants and pattern recognition., Peer Reviewed, Postprint (author's final draft)

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