• Corpus ID: 247939932

Curiosity as filling, compressing, and reconfiguring knowledge networks

  title={Curiosity as filling, compressing, and reconfiguring knowledge networks},
  author={Shubhankar P. Patankar and Dale Zhou and Christopher W. Lynn and Jason Z. Kim and Mathieu Ouellet and Harang Ju and Perry Zurn and David M. Lydon-Staley and Danielle S Bassett},
Curiosity is an internally motivated search for information. It is enduring and open-ended, and may have evolved to help us build accurate mental representations of our ever-changing environments. Due to the significant role that curiosity plays in our lives, several theoretical constructs, such as the information gap theory and compression progress theory, have sought to explain how we engage in its practice. According to the former, curiosity is the drive to acquire information that is missing… 

Figures from this paper


The growth and form of knowledge networks by kinesthetic curiosity
Edgework: Viewing curiosity as fundamentally relational
Most theories of curiosity emphasize the acquisition of information. Such conceptualizations focus on the actions of the knower in seeking units of knowledge. Each unit is valued as an unknown and
Hunters, busybodies and the knowledge network building associated with deprivation curiosity.
A historico-philosophical taxonomy of information seeking coupled with a knowledge network building framework is used to capture styles of information-seeking in 149 participants as they explore Wikipedia for over 5 hours spanning 21 days.
Architecture and evolution of semantic networks in mathematics texts
This study studies the topological structure of semantic networks reflecting mathematical concepts and their relations in college-level linear algebra texts to find that they exhibit strong core–periphery architecture, and that the density of these gaps tracks negatively with community ratings of each textbook.
Knowledge gaps in the early growth of semantic feature networks
This work describes knowledge gaps, manifesting as topological cavities, in toddlers’ growing semantic network, and discusses the importance of semantic feature network topology in language learning and speculate that the progression through knowledge gaps may be a robust feature of knowledge acquisition.
Quantifying the compressibility of complex networks
Analyzing an array of real and model networks, it is demonstrated that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity, which indicates that hierarchical organization—which is characterized by modular structure and heterogeneous degrees—facilitates compression in complex networks.
The network structure of scientific revolutions
It is demonstrated that concept networks grow not by expanding from their core but rather by creating and filling knowledge gaps, a process which produces discoveries that are more frequently awarded Nobel prizes than others.
The Psychology and Neuroscience of Curiosity
The psychology of curiosity: A review and reinterpretation.
Research on curiosity has undergone 2 waves of intense activity. The 1st, in the 1960s, focused mainly on curiosity's psychological underpinnings. The 2nd, in the 1970s and 1980s, was characterized
Curiosity-Driven Exploration by Self-Supervised Prediction
This work forms curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model, which scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and ignores the aspects of the environment that cannot affect the agent.