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.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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.
Gottlieb, J., Oudeyer, P. Y., Lopes, M. & Baranes, A. Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends Cogn. Sci. 17, 585–593 (2013).
Gottlieb, J. & Oudeyer, P. Y. Towards a neuroscience of active sampling and curiosity. Nat. Rev. Neurosci. 19, 758–770 (2018).
Kidd, C. & Hayden, B. Y. The psychology and neuroscience of curiosity. Neuron 88, 449–460 (2015).
Blanchard, T. C., Hayden, B. Y. & Bromberg-Martin, E. S. Orbitofrontal cortex uses distinct codes for different choice attributes in decisions motivated by curiosity. Neuron 85, 602–614 (2015).
Brydevall, M., Bennett, D., Murawski, C. & Bode, S. The neural encoding of information prediction errors during non-instrumental information seeking. Sci. Rep. 8, 6134 (2018).
Daddaoua, N., Lopes, M. & Gottlieb, J. Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned reinforcement in non-human primates. Sci. Rep. 6, 20202 (2016).
Lydon-Staley, D. M., Zurn, P. & Bassett, D. S. Within-person variability in curiosity during daily life and associations with well-being. J. Pers. 88, 625–641 (2020).
Park, S., Kim, M.-S. & Chun, M. M. Concurrent working memory load can facilitate selective attention: evidence for specialized load. J. Exp. Psychol. Hum. Percept. Perform. 33, 1062–1075 (2007).
Peterson, C., Ruch, W., Beermann, U., Park, N. & Seligman, M. E. Strengths of character, orientations to happiness, and life satisfaction. J. Posit. Psychol. 2, 149–156 (2007).
Fredrickson, B. L. in Advances in Experimental Social Psychology (eds Plant, E. A. & Devine, P. G.) 1–53 (Academic Press, 2013).
Fredrickson, B. L. The role of positive emotions in positive psychology. Am. Psychol. 56, 218–226 (2001).
Kashdan, T. B., Rose, P. & Fincham, F. D. Curiosity and exploration: facilitating positive subjective experiences and personal growth opportunities. J. Pers. Assess. 82, 291–305 (2004).
Nowotny, H. Insatiable Curiosity: Innovation in a Fragile Future (MIT Press, 2010).
Zurn, P. in Toward New Philosophical Explorations of the Epistemic Desire to Know: Just Curious about Curiosity (ed. Papastefanou, M.) 27–49 (Cambridge Scholars Press, 2019).
Heidegger, M. Being and Time (trans. Stambaugh, J.) (State Univ. of New York Press, 1996).
Yonge, C. D. (trans.) The Works of Philo: Complete and Unabridged (Hendrickson, 1993).
Helmbold, W. C. et al. Plutarch’s Moralia Vol. 1 (Harvard Univ. Press, 1960).
Nietzsche, F. Beyond Good and Evil (trans. Kaufmann, W.) (Vintage Books, 1996).
Zurn, P. & Bassett, D. S. On curiosity: a fundamental aspect of personality, a practice of network growth. Pers. Neurosci. 1, e13 (2018).
Baranes, A., Oudeyer, P. Y. & Gottlieb, J. Eye movements reveal epistemic curiosity in human observers. Vis. Res. 117, 81–90 (2015).
Risko, E. F., Anderson, N. C., Lanthier, S. & Kingstone, A. Curious eyes: individual differences in personality predict eye movement behavior in scene-viewing. Cognition 122, 86–90 (2012).
Bassett, D. S. in Curiosity Studies: Toward a New Ecology of Knowledge (eds Zurn, P. & Shankar, A.) 57–74 (Univ. of Minnesota Press, 2019).
West, R. & Leskovec, J. Human wayfinding in information networks. Proc. 21st Int. Conf. World Wide Web 117, 619–628 (2012).
Gross, J. L. & Yellen, J. Handbook of Graph Theory (CRC, 2004).
Newman, M. Networks (Oxford Univ. Press, 2018).
Loewenstein, G. The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116, 75–98 (1994).
Litman, J. A. & Mussel, P. Validity of the interest- and deprivation-type epistemic curiosity model in Germany. J. Individ. Differ. 34, 59–68 (2013).
Litman, J. A. Interest and deprivation factors of epistemic curiosity. Pers. Individ. Differ. 44, 1585–1595 (2008).
Litman, J. A. & Jimerson, T. L. The measurement of curiosity as a feeling of deprivation. J. Pers. Assess. 82, 147–157 (2004).
Kashdan, T. B. et al. The five-dimensional curiosity scale: capturing the bandwidth of curiosity and identifying four unique subgroups of curious people. J. Res. Pers. 73, 130–149 (2018).
Litman, J. A. Relationships between measures of I- and D-type curiosity, ambiguity tolerance, and need for closure: an initial test of the wanting–liking model of information-seeking. Pers. Individ. Differ. 48, 397–402 (2010).
Golman, R. & Loewenstein, G. Information gaps: a theory of preferences regarding the presence and absence of information. Decision 5, 143–164 (2018).
Litman, J. in The Cambridge Handbook on Motivation and Learning (eds Renniger, K. A. & Hidi, S. E.) 418–422 (Cambridge Univ. Press, 2019).
Lauriola, M. et al. Epistemic curiosity and self-regulation. Pers. Individ. Differ. 83, 202–207 (2015).
FitzGibbon, L., Lau, J. K. L. & Murayama, K. The seductive lure of curiosity: information as a motivationally salient reward. Curr. Opin. Behav. Sci. 35, 21–27 (2020).
Berridge, K. C. From prediction error to incentive salience: mesolimbic computation of reward motivation. Eur. J. Neurosci. 35, 1124–1143 (2012).
Berridge, K. C. The debate over dopamine’s role in reward: the case for incentive salience. Psychopharmacology 191, 391–431 (2007).
Robinson, T. E. & Berridge, K. C. Incentive-sensitization and drug ‘wanting’. Psychopharmacology 171, 352–353 (2004).
Berridge, K. C. & Robinson, T. E. What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res. Rev. 28, 309–369 (1998).
Lau, J. K. L., Ozono, H., Kuratomi, K., Komiya, A. & Murayama, K. Shared striatal activity in decisions to satisfy curiosity and hunger at the risk of electric shocks. Nat. Hum. Behav. 4, 531–543 (2020).
Kobayashi, K. & Hsu, M. Common neural code for reward and information value. Proc. Natl Acad. Sci. USA 116, 13061–13066 (2019).
Gruber, M. J., Gelman, B. D. & Ranganath, C. States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron 84, 486–496 (2014).
Shin, D. D. & Kim, S.-i. Homo curious: curious or interested? Educ. Psychol. Rev. 31, 853–874 (2019).
Scholtes, I. When is a network a network? Multi-order graphical model selection in pathways and temporal networks. Preprint at arXiv https://arxiv.org/abs/1702.05499 (2017).
West, R. & Leskovec, J. Automatic versus human navigation in information networks. In Proc. 6th International AAAI Conference on Weblogs and Social Media (ed. Breslin, J.) 362–369 (AAAI, 2012).
Lamprecht, D., Lerman, K., Helic, D. & Strohmaier, M. How the structure of Wikipedia articles influences user navigation. New Rev. Hypermedia Multimed. 23, 29–50 (2017).
Lemmerich, F., Sáez-Trumper, D., West, R. & Zia, L. Why the world reads Wikipedia: beyond English speakers. In Proc. 12th ACM International Conference on Web Search and Data Mining (eds. Culpepper, J. S. & Moffat, A.) 618–626 (ACM, 2019).
Singer, P. et al. Why we read Wikipedia. In Proc. 26th International Conference on World Wide Web (eds. Barrett, R. & Cummings, R.) 1591–1600 (International World Wide Web Conferences Steering Committee, 2017).
Kashdan, T. B. & Steger, M. F. Curiosity and pathways to well-being and meaning in life: traits, states, and everyday behaviors. Motiv. Emot. 31, 159–173 (2007).
Zuckerman, M. Behavioral Expressions and Biosocial Bases of Sensation Seeking (Cambridge Univ. Press, 1994).
Lydon-Staley, D. & Bassett, D. Within-person variability in sensation-seeking during daily life: positive associations with alcohol use and self-defined risky behaviors. Psychol. Addict. Behav. 34, 257–268 (2020).
Kleiman, E. E. MAtools: Data management tools for real-time monitoring/ecological momentary assessment data. R package version 0.1.3 (2017).
Onnela, J. P., Saramäki, J., Kertész, J. & Kaski, K. Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E 71, 065103 (2005).
Latora, V. & Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701 (2001).
Van Wijk, B. C., Stam, C. J. & Daffertshofer, A. Comparing brain networks of different size and connectivity density using graph theory. PLoS ONE 5, e13701 (2010).
Viswanathan, G. M. et al. Optimizing the success of random searches. Nature 401, 911–914 (1999).
Viswanathan, G. et al. Lévy flights in random searches. Physica A 282, 208–213 (2000).
Sims, D. W. et al. Scaling laws of marine predator search behaviour. Nature 451, 1098–1102 (2008).
Hills, T. T., Maouene, M., Maouene, J., Sheya, A. & Smith, L. Longitudinal analysis of early semantic networks: preferential attachment or preferential acquisition? Psychol. Sci. 20, 729–739 (2009).
Santos, M., Viswanathan, G., Raposo, E. & da Luz, M. Optimization of random searches on regular lattices. Phys. Rev. E 72, 046143 (2005).
Wosniack, M., Santos, M., Raposo, E., Viswanathan, G. & Da Luz, M. Robustness of optimal random searches in fragmented environments. Phys. Rev. E 91, 052119 (2015).
Raposo, E. P. et al. How landscape heterogeneity frames optimal diffusivity in searching processes. PLoS Comput. Biol. 7, e1002233 (2011).
Harris, T. H. et al. Generalized Lévy walks and the role of chemokines in migration of effector CD8+ T cells. Nature 486, 545–548 (2012).
Rhee, I. et al. On the Levy-walk nature of human mobility. IEEE ACM Trans. Netw. 19, 630–643 (2011).
Rhodes, T. & Turvey, M. T. Human memory retrieval as Lévy foraging. Physica A 385, 255–260 (2007).
Bartumeus, F. Lévy processes in animal movement: an evolutionary hypothesis. Fractals 15, 151–162 (2007).
Humphries, N. E. et al. Environmental context explains Lévy and Brownian movement patterns of marine predators. Nature 465, 1066–1069 (2010).
Viswanathan, G. M., Da Luz, M. G., Raposo, E. P. & Stanley, H. E. The Physics of Foraging: An Introduction to Random Searches and Biological Encounters (Cambridge Univ. Press, 2011).
Wosniack, M. E., Santos, M. C., Raposo, E. P., Viswanathan, G. M. & da Luz, M. G. The evolutionary origins of Lévy walk foraging. PLoS Comput. Biol. 13, e1005774 (2017).
Hills, T. T. Animal foraging and the evolution of goal-directed cognition. Cogn. Sci. 30, 3–41 (2006).
Todd, P. M., Hills, T. T. & Robbins, T. W. Cognitive Search: Evolution, Algorithms, and the Brain Vol. 9 (MIT Press, 2012).
Berlyne, D. E. Conflict, Arousal, and Curiosity (McGraw-Hill, 1960).
Day, H. An Instrument for the Measurement of Intrinsic Motivation: An Interim Report to the Department of Manpower and Immigration (York University, 1969).
Leherissey, B. L. The Development of a Measure of State Epistemic Curiosity (ERIC, 1971).
Litman, J. A. & Spielberger, C. D. Measuring epistemic curiosity and its diversive and specific components. J. Pers. Assess. 80, 75–86 (2003).
Zurn, P. & Bassett, D. S. Philosophy of biology: seizing an opportunity. eLife 8, e48336 (2019).
Johnson, K. T. in Curiosity Studies: Toward a New Ecology of Knowledge (eds Zurn, P. & Shankar, A.) 129–146 (Univ. of Minnesota Press, 2020).
Kenett, Y. N., Anaki, D. & Faust, M. Investigating the structure of semantic networks in low and high creative persons. Front. Hum. Neurosci. 8, 407 (2014).
Kenett, Y. N. & Faust, M. A semantic network cartography of the creative mind. Trends Cogn. Sci. 23, 271–274 (2019).
Fosco, G. M. & Lydon-Staley, D. M. A within-family examination of interparental conflict, cognitive appraisals, and adolescent mood and well-being. Child Dev. 90, e421–e436 (2019).
Carver, C. S. & White, T. L. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: the bis/bas scales. J. Pers. Soc. Psychol. 67, 319–333 (1994).
Costa, P. T. & McCrae, R. R. Normal personality assessment in clinical practice: the neo personality inventory. Psychol. Assess. 4, 5–13 (1992).
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.
Peer review information Primary handling editor: Aisha Bradshaw.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
About this article
Cite this article
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
Journal of Happiness Studies (2021)