In recent times, researchers across a variety of disciplines in the humanities and social sciences have been interested in the human ability to process embedded mindstates, also known as 'multiple-order intentionality' (MOI): A believes that B thinks that C intends (etc.). This task is considered increasingly cognitively demanding with every order of embedding added. However, we argue that the way in which the information relevant to the task is represented in language (in particular, using a narrative) greatly influences how well people are able to deal with MOI cognitively. This effect can be illustrated by paraphrasing situations presented by a play such as Shakespeare's Othello: by the end of Act II the audience has to understand that Iago intends that Cassio believes that Desdemona intends that Othello believes that Cassio did not intend to disturb the peace. Formulated this way, using sentence embedding to express the intentional relationships, this is highly opaque. At the same time, we know that Othello has been understood and appreciated by innumerable different audiences for ages. What is it that the play's text does to make the audience understand all these embedded mindstates without undue cognitive strain? In this article we discuss six 'expository strategies' relevant to the representation of MOI and illustrate their working with examples from Shakespeare's Othello.
Emoji, colourful pictographs showing faces, creatures and objects, have seen a surge in popularity and number in recent years. This exploratory study strives to answer the following question: how and why are emoji used on Twitter in the Netherlands and England? By combining quantitative and qualitative methods, we identified three important factors explaining emoji usage: the individual’s purpose on Twitter, the perceived functionality of emoji and the individual’s selection criteria for emoji. Overall, emoji play an important role in online communication and their use is more complex than their light-hearted appearance may suggest.
Choosing a suitable visualization for data is a difficult task. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such as low accessibility and indecisiveness. In this study, we first define a step-by-step guide on how to build a data visualization recommender system. We then use this guide to create a model for a data visualization recommender system for non-experts that aims to resolve the issues of current solutions. The result is a questionbased model that uses a decision tree and a data visualization classification hierarchy in order to recommend a visualization. Furthermore, it incorporates both task-driven and data characteristicsdriven perspectives, whereas existing solutions seem to either convolute these or focus on one of the two exclusively. Based on testing against existing solutions, it is shown that the new model reaches similar results while being simpler, clearer, more versatile, extendable and transparent. The presented guide can be used as a manual for anyone building a data visualization recommender system. The resulting model can be applied in the development of new data visualization software or as part of a learning tool. Figure 1: The data science process [3]and also elaborate on ways of evaluating and implementing it. Section 2 places data visualization recommender systems in the context of data science. Section 3 introduces our step-by-step guide to building a data visualization recommender system. In Sections 4-10 we go through the individual steps and build our very own data visualization recommender system while taking measures to make it well suited for non-expert users. We define a 'non-expert user' as someone without professional or specialized knowledge of data visualization. We thus include both complete beginners and users who have general knowledge of data visualization types (e.g. bar charts, pie charts, scatter plots) but have no professional experience in the fields of data science and data communication. We want to see if we can make adjustments that make a system more suitable for non-expert users while maintaining effectiveness (still clearly distinguishing the data visualizations from each other) and performance (recommending the most suitable visualization type). We draw conclusions in Section 11 and set an agenda for future work in Section 12. Context Data scienceData science plays an important role in scientific research, as it aids us in collecting, organizing, and interpreting data, so that it can be transformed into valuable knowledge. Figure 1 shows a simplified diagram of the data science process as described by O‚Neil and Schutt [3]. This diagram is helpful in demarcating the research objectives of this paper. According to O‚Neil and Schutt, first, real world raw data is collected, processed and cleaned through a process called data munging. Then exploratory data analysis (EDA) follows, during which we might find that we need to collect more data or dedicate more time to cleaning and organizing the cu...
Curiosity is a strong motivator for human action, but the circumstances under which one becomes curious are not clear. This paper builds on the assumption that video games can be used as a stimulus for the experimental study of curiosity, and forms a basis in examining the type of curiosity motivated by spatial exploration. A video game was created that incorporates five proposed 'game design patterns' that may induce curiosity in players. The game, Shinobi Valley, was tested in a pilot study with 24 participants. Participants responded positively to the game and exhibited exploratory behaviour while playing without specifically being prompted to do so. The presented results suggest which of the patterns are most promising in inducing curiosity, and show that the game is of sufficient quality to be used in larger studies. CCS Concepts•Applied computing → Computer games; •Humancentered computing → Empirical studies in interaction design; Empirical studies in HCI;
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