How Are Curiosity and Interest Different? Naive Bayes Classification of People's Beliefs

Researchers studying curiosity and interest note a lack of consensus in whether and how these important motivations for learning are distinct. Empirical attempts to distinguish them are impeded by this lack of conceptual clarity. Following a recent proposal that curiosity and interest are folk concepts, we sought to determine a non-expert consensus view on their distinction using machine learning methods. In Study 1, we demonstrate that there is a consensus in how they are distinguished, by training a Naïve Bayes classification algorithm to distinguish between free-text definitions of curiosity and interest (n = 396 definitions) and using cross-validation to test the classifier on two sets of data (main n = 196; additional n = 218). In Study 2, we demonstrate that the non-expert consensus is shared by experts and can plausibly underscore future empirical work, as the classifier accurately distinguished definitions provided by experts who study curiosity and interest (n = 92). Our results suggest a shared consensus on the distinction between curiosity and interest, providing a basis for much-needed conceptual clarity facilitating future empirical work. This consensus distinguishes curiosity as more active information seeking directed towards specific and previously unknown information. In contrast, interest is more pleasurable, in-depth, less momentary information seeking towards information in domains where people already have knowledge. However, we note that there are similarities between the concepts, as they are both motivating, involve feelings of wanting, and relate to knowledge acquisition

Process account of curiosity and interest: a reward-learning perspective

Previous studies suggested roles for curiosity and interest in knowledge acquisition and exploration, but there has been a long-standing debate about how to define these concepts and whether they are related or different. In this paper, we address the definition issue by arguing that there is inherent difficulty in defining curiosity and interest, because both curiosity and interest are naïve concepts, which are not supposed to have a priori scientific definitions. We present a reward-learning framework of autonomous knowledge acquisition and use this framework to illustrate the importance of process account as an alternative to advance our understanding of curiosity and interest without being troubled by their definitions. The framework centers on the role of rewarding experience associated with knowledge acquisition and learning and posits that the acquisition of new knowledge strengthens the value of further information. Critically, we argue that curiosity and interest are the concepts that they subjectively construe through this knowledge-acquisition process. Finally, we discuss the implications of the reward-learning framework for education and empirical research in educational psychology

Magic Curiosity Arousing Tricks (MagicCATs): a novel stimulus collection to induce epistemic emotions

There has been considerable interest in empirical research on epistemic emotions, i.e. emotions related to knowledge-generating qualities of cognitive tasks and activities such as curiosity, interest, and surprise. One big challenge when studying epistemic emotions is systematically inducting these emotions in restricted experimental settings. The current study created a novel stimulus set called Magic Curiosity Arousing Tricks (MagicCATs): a collection of 166 short magic trick video clips that aim to induce a variety of epistemic emotions. MagicCATs are available for research, and can be used in a variety of ways to examine epistemic emotions. Rating data also supports that the magic tricks elicit a variety of epistemic emotions with sufficient inter-stimulus variability, demonstrating good psychometric properties for their use in psychological experiments