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.
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All data used in the manuscript are available upon request from the corresponding author.
The analyses in the manuscript used code available through R, MATLAB and the Brain Connectivity Toolbox. The code associated with the generative model is available at https://github.com/dalejn/kinestheticCuriosity.
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We thank J. Dworkin, C. W. Lynn and S. Patankar for feedback on earlier versions of the manuscript. D.S.B., D.M.L.-S., D.Z. and A.S.B. acknowledge support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the ISI Foundation, the Paul Allen Foundation, the Army Research Laboratory (grant no. W911NF-10-2-0022), the Army Research Office (grant nos Bassett-W911NF-14-1-0679, Grafton-W911NF-16-1-0474 and DCIST-W911NF-17-2-0181), the Office of Naval Research, the National Institute of Mental Health (grant nos 2-R01-DC-009209-11, R01-MH112847, R01-MH107235 and R21-M MH-106799), the National Institute of Child Health and Human Development (grant no. 1R01HD086888-01), the National Institute of Neurological Disorders and Stroke (grant no. R01 NS099348), the National Science Foundation (grant nos PHY-1554488 and BCS-1631550) and the National Institute on Drug Abuse (grant no. 1K01DA047417). All authors acknowledge support from the Center for Curiosity. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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Supplementary Figs. 1–5, Supplementary Tables 1–23, methods, results, discussion and references.
An illustration of the effects of varying levels of reinforcement and regularity parameters on network exploration. We modelled different styles of knowledge network growth using a generative model consisting of two growth rules: reinforcement and regularity. Reinforcement is the value an agent places on similar and previously sought information while traversing a knowledge network. Regularity reflects the preference to take short versus long topological steps while exploring a knowledge network. The four panels in the video illustrate how varying levels of reinforcement and regularity influence knowledge network growth. High values of reinforcement are associated with a greater likelihood of returning to previously visited concepts, resulting in networks akin to the hunter style of curious information seeking. High regularity values are associated with a preference to take shorter topological leaps when walking on the knowledge network, resulting in tight networks akin to the style of the hunter. Loose networks associated with the busybody style of information seeking are reflected in low values of reinforcement and low values of regularity.
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Lydon-Staley, D.M., Zhou, D., Blevins, A.S. et al. Hunters, busybodies and the knowledge network building associated with deprivation curiosity. Nat Hum Behav 5, 327–336 (2021). https://doi.org/10.1038/s41562-020-00985-7
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