Seizing an opportunity

Validation of the I- and D-type epistemic curiosity scale among young Chinese children and implications on early curiosity nurture

Curiosity, an innate and intrinsic motivation to explore, makes vital contributions to learning in individuals of various ages. Epistemic curiosity centers on the drive to close information gaps and can be classified into joyous exploration and interest (I) and deprivation sensitivity (D) types. Each subtype is associated with different academic achievements, personality traits, emotions, and aspects of creativity. Building on the concept of epistemic curiosity in adults, the I- and D-type Epistemic Curiosity in Young Children (I/D-YC) scale was developed. The purpose of the present study was to validate the Chinese I/D-YC scale for preschoolers. Both exploratory factor analyses of data from 111 parents (Sample 1) and confirmatory factor analyses of data from 389 parents (Sample 2) indicated that the Chinese I/D-YC replicated the two-factor structure of the original scale. The scales' convergent validity was examined with data from 189 parents (Sample 3) and 129 teachers (Sample 4), as its reliability and test-retest reliability was respectively examined with data from 389 to 82 parents (from sample 2). The results established the Chinese I/D-YC scale as a valid and reliable measure of epistemic curious behaviors in young Chinese children. Moreover, the cultivation of epistemic curiosity should weaken inhibition and this might enhance well-being and creativity and learning.

Hunters, busybodies and the knowledge network building associated with deprivation curiosity

The open-ended and internally driven nature of curiosity makes characterizing the information seeking that accompanies it a daunting endeavour. We use a historico-philosophical taxonomy of information seeking coupled with a knowledge network building framework to capture styles of information-seeking in 149 participants as they explore Wikipedia for over 5 hours spanning 21 days. We create knowledge networks in which nodes represent distinct concepts and edges represent the similarity between concepts. We quantify the tightness of knowledge networks using graph theoretical indices and use a generative model of network growth to explore mechanisms underlying information-seeking. Deprivation curiosity (the tendency to seek information that eliminates knowledge gaps) is associated with the creation of relatively tight networks and a relatively greater tendency to return to previously visited concepts. With this framework in hand, future research can readily quantify the information seeking associated with curiosity.

The growth and form of knowledge networks by kinesthetic curiosity

Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification. The practice of curiosity can be viewed as an extended and open-ended search for valuable information with hidden identity and location in a complex space of interconnected information. Despite its importance, curiosity has been challenging to computationally model because the practice of curiosity often flourishes without specific goals, external reward, or immediate feedback. Here, we show how network science, statistical physics, and philosophy can be integrated into an approach that coheres with and expands the psychological taxonomies of specific-diversive and perceptual-epistemic curiosity. Using this interdisciplinary approach, we distill functional modes of curious information seeking as searching movements in information space. The kinesthetic model of curiosity offers a vibrant counterpart to the deliberative predictions of model-based reinforcement learning. In doing so, this model unearths new computational opportunities for identifying what makes curiosity curious.