Role of Metacognitive Confidence Judgments in Curiosity: Different Effects of Confidence on Curiosity Across Epistemic and Perceptual Domains.

Previous research suggests that curiosity is sometimes induced by novel information one has no relevant knowledge about, but it is sometimes induced by new information about something that one is familiar with and has prior knowledge about. However, the conditions under which novelty or familiarity triggers curiosity remain unclear. Using metacognitive confidence judgments as a proxy to quantify the amount of knowledge, this study evaluates the relationship between the amount of relevant knowledge and curiosity. We reviewed previous studies on the relationship between subjective curiosity and confidence and reanalyzed existing large-sample data. The findings indicate that the relationship between curiosity and confidence differs depending on the nature of the stimuli: epistemic versus perceptual. Regarding perceptual stimuli, curiosity is stronger when individuals have lower confidence levels. By contrast, for epistemic stimuli, curiosity is stronger when individuals have higher confidence levels. These results suggest that curiosity triggered by perceptual stimuli is based on perceived novelty, whereas that triggered by epistemic stimuli is based on familiarity with prior knowledge.

Humans monitor learning progress in curiosity-driven exploration.

Curiosity-driven learning is foundational to human cognition. By enabling humans to autonomously decide when and what to learn, curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms driving people to set intrinsic goals, when they are free to explore multiple learning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures (e.g., percent correct) and learning progress, that could be used as intrinsic utility functions to efficiently organize exploration. Such intrinsic utilities constitute computationally cheap but smart heuristics to prevent people from laboring in vain on unlearnable activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a free-choice experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include a learning-progress component provide the best fit to task selection data. These results bridge the research in artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and introduce tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.

A structured open dataset of government interventions in response to COVID-19.

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.