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

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Abstract

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.
Articles
https://doi.org/10.1038/s41562-020-00985-7
1Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA. 2Department of Bioengineering, School of Engineering & Applied
Science, University of Pennsylvania, Philadelphia, PA, USA. 3Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, USA. 4Department of Philosophy, American University, Washington DC, USA. 5Department of Electrical & Systems Engineering, School
of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA. 6Department of Neurology, Perelman School of Medicine, University
of Pennsylvania, Philadelphia, PA, USA. 7Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA,
USA. 8Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 9Santa Fe Institute, Santa Fe, NM, USA.
e-mail: dsb@seas.upenn.edu
Curiosity is characterized by intrinsically motivated informa-
tion seeking13. The information sought while acting on one’s
curiosity often has no immediate, tangible benefit46. Despite
a lack of immediate benefits, the tendency to frequently experience
curiosity is associated with positive well-being79; curiosity facilitates
engagement with novel and challenging stimuli and, in the process,
the accrual of information and other resources that, although not
of immediate benefit, may have utility when encountering future
challenges1012. And irrespective of its immediate or potential utility,
curiosity may well be valuable in itself13.
Characterizing how individuals seek information when
internally driven is fundamental to understanding how curios-
ity leads to the shoring up of resources that impact well-being.
Historico-philosophical studies tracing the use of the word ‘curios-
ity’ have identified styles of information seeking that span millen-
nia, cultures and languages14. The styles include the busybody and
the hunter. The information seeking of the busybody is marked by
a preference for sampling diverse concepts, characterized by “dis-
traction” and “never-dwelling anywhere15 (p. 161). The busybody
will “frisk about, and rove about, at random, wherever they please16
(section 34). The information seeking of the hunter is characterized
by sampling closely connected concepts. The hunter does not “turn
aside and follow every scent”17 (p. 520e) in the manner of the busy-
body. The hunter instead “wishes [they] had a few hundred help-
ers and good, well-trained hounds that [they] could drive into the
history of the human soul to round up [their] game”18 (p. 59) in a
targeted information search. Both styles are considered expressions
of curiosity, but there are individual differences in the extent to
which each style is expressed19. Tendencies to exhibit one style over
another will lead to the accumulation of different types of resources
over time. The busybody’s store of information will be more diverse
than that of the hunter, but the hunter’s information store will
contain greater depth on fewer subjects.
The open-ended, internally driven nature of curiosity makes
characterizing diverse information-seeking styles a daunting
endeavour. Existing approaches include the examination of sac-
cadic exploration of visual scenes and responses to trivia ques-
tions designed to evoke curiosity20,21. Experimental paradigms are
shedding light on curiosity, but they have been met with calls to
consider more complex forms of information seeking that occur
over extended timescales2. We claim that styles of information seek-
ing identified through historico-philosophical methodologies can
be readily accommodated within a knowledge-network-building
framework22. From this perspective, network nodes represent dis-
tinct concepts, and network edges represent the manner in which
the concepts are related. While seeking information, an individual
traverses edges on knowledge networks, moving from one con-
cept to the next. Some of the edges they traverse may have large
weights, indicating that the two concepts joined by the edge are
very similar, and some edges may have very small weights, indicat-
ing that the two concepts are virtually unrelated. Casting curiosity
as a knowledge-network-building practice reflects the intercon-
nectedness of informational units23 and allows an application of
the mathematical language of graph theory24,25 to quantify complex
manifestations of curious behaviour. The easily distracted busy-
body will create loose knowledge networks of sparsely connected,
seemingly unrelated concepts. In the parlance of graph theory, their
networks will have small edge weights, low clustering and high
characteristic path length. The more targeted hunter, in contrast,
will create tight networks consisting of closely connected concepts,
and their networks will have large edge weights, high clustering and
low characteristic path length (Fig. 1).
Hunters, busybodies and the knowledge network
building associated with deprivation curiosity
David M. Lydon-Staley 1,2, Dale Zhou 3, Ann Sizemore Blevins 2, Perry Zurn 4 and
Danielle S. Bassett 2,5,6,7,8,9 ✉
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 similar-
ity 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 infor-
mation seeking associated with curiosity.
NATURE HUMAN BEHAVIOUR | VOL 5 | MARCH 2021 | 327–336 | www.nature.com/nathumbehav 327
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... Berlyne's notions of specific and diversive curiosity received recent empirical support. Lydon-Staley, Zhou, Blevins, Zurn, and Bassett (2021) used computational network science methods to capture styles of curiosity in the behavioral patterns of participants exploring Wikipedia. Knowledge networks were created in which nodes in the network represented unique Wikipedia pages visited and edges reflected the text similarity between the content of different pages. ...
... Constructs such as specific and diversive curiosity also apply to creativity. This relationship is reflected in the hunter and busybody typology proposed by Lydon-Staley et al. (2021). If hunters form tight knowledge networks made up of closely related concepts, these people would sample closely related ideas when engaging in creative problem solving. ...
... However, curiosity is currently receiving renewed attention in psychology, and recent studies are beginning to offer new and innovative ways of studying it experimentally. For example, the study by Lydon-Staley et al. (2021) offers an example of how curiosity can be studied empirically in an ecologically valid setting (browsing Wikipedia). In addition, the Curiosity Q&A study by Koutstaal et al. (2022) provides an innovative paradigm to investigate curiosity empirically that can be adapted to investigate the roles of question-asking and information-seeking in many domains. ...
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Curiosity, creativity, and aesthetics are typically studied separately. The extent to which they share psychological and neural mechanisms is not well understood, despite all being linked to broader personality characteristics like Openness to Experience and are driven by a desire for information and knowledge. Here, we review evidence and advance the hypothesis that creative and aesthetic experiences depend on curiosity as a driver of information-seeking and exploratory behavior because they are exemplars of situations that highlight gaps in knowledge or require problem finding and solving. At the psychological level, we link curiosity, creativity, and aesthetics to Openness to Experience and to ones’ semantic memory. We demonstrate how Openness is a critical personality trait in enhancing curious behaviors, as well as creative and aesthetic acts. Furthermore, we highlight the role of semantic memory in such information-seeking behavior, leading to knowledge acquisition. At the neural level, we examine the neurobiological underpinnings of these constructs in relation to the mesolimbic dopaminergic reward system, as related to information-seeking. Finally, we link creativity and aesthetic experience and discuss how stages of art viewing and making relate to curiosity. Thus, we argue that information-seeking, the key behavior attributed to curiosity, motivates both creative and aesthetic activities.
... Moreover, concept networks have proven to be powerful tools for probing questions about the exploration of knowledge across Wikipedia articles (Lydon-Staley et al., 2021). We perform these analyses on hyperlinked articles from Wikipedia, the largest online encyclopedia. ...
... We formalize this structured body of knowledge as a concept network whose nodes represent concepts and whose edges represent inter-concept relations (Siew et al., 2019). Concept networks have proven to be powerful tools for probing questions about topological structure and the exploration of knowledge (Christianson et al., 2020;Lydon-Staley et al., 2021). To begin to study the process of science, we build growing concept networks from Wikipedia, a free online encyclopedia (Figure 5.1A; Section 5.7). ...
... Network representations are intuitive models for concepts and conceptual relations. Such representations have previously proven useful in the study of concept networks from Wikipedia, and in the characterization of their topology using measures of centrality, shortest paths, and clustering (Bellomi et al., 2005;Matas et al., 2017;Lydon-Staley et al., 2021). Further, network representations can reveal patterns in data that are not quantifiable by observing each individual pairwise interaction-in this case, individual pages or hyperlinks-but that are only quantifiable by considering the entire network or subsection of a network. ...
Article
From brains to science itself, distributed representational systems store and process information about the world. In brains, complex cognitive functions emerge from the collective activity of billions of neurons, and in science, new knowledge is discovered by building on previous discoveries. In both systems, many small individual units—neurons and scientific concepts—interact to inform complex behaviors in the systems they comprise. The patterns in the interactions between units are telling; pairwise interactions not only trivially affect pairs of units, but they also form structural and dynamic patterns with more than just pairs, on a larger scale of the network. Recently, network science adapted methods from graph theory, statistical mechanics, information theory, algebraic topology, and dynamical systems theory to study such complex systems. In this dissertation, we use such cutting-edge methods in network science to study complex distributed representational systems in two domains: cascading neural networks in the domain of neuroscience and concept networks in the domain of science of science. In the domain of neuroscience, the brain is a system that supports complex behavior by storing and processing information from the environment on long time scales. Underlying such behavior is a network of millions of interacting neurons. Many recent studies measure neural activity on the scale of the whole brain with brain regions as units or on the scale of brain regions with individual neurons as units. While many studies have explored the neural correlates of behaviors on these scales, it is less explored how neural activity can be decomposed into low-level patterns. Network science has shown potential to advance our understanding of large-scale brain networks, and here, we apply network science to further our understanding of low-level patterns in small-scale neural networks. Specifically, we explore how the structure and dynamics of biological neural networks support information storage and computation in spontaneous neural activity in slice recordings of rodent brains. Our results illustrate the relationships between network structure, dynamics, and information processing in neural systems. In the domain of science of science, the practice of science itself is a system that discovers and curates information about the physical and social world. For centuries, philosophers, historians, and sociologists of science have theorized about the process and practice of scientific discovery. Recently, the field of science of science has emerged to use a more data-driven approach to quantify the process of science. However, it remains unclear how recent advances in science of science either support or refute the various theories from the philosophies of science. Here, we use a network science approach to operationalize theories from prominent philosophers of science, and we test those theories using networks of hyperlinked articles in Wikipedia, the largest online encyclopedia. Our results support a nuanced view of philosophies of science—that science does not grow outward, as many may intuit, but by filling in gaps in knowledge. In this dissertation, we examine cascading neural networks first in Chapters 2 through 4 and then concept networks in Chapter 5. The studies in Chapters 2 to 4 highlight the role of patterns in the connections of neural networks in storing information and performing computations. The study in Chapter 5 describes patterns in the historical growth of concept networks of scientific knowledge from Wikipedia. Together, these analyses aim to shed light on the network science of distributed representational systems that store and process information about the world.
... Although laboratory-based research shows that curiosity is one of the key drivers of information seeking, it is unknown whether these findings translate into information seeking in everyday life, where genuine knowledge acquisition takes place. An initial hint comes from a study showing that deprivation sensitivity, that is, a subtype of curiosity reflecting the tendency to seek information in order to close information gaps, was associated with the creation of knowledge networks during the exploration of Wikipedia articles (Lydon-Staley et al., 2021). Given that participants were able to explore individually chosen topics that they were interested in, it remains an open question whether curiosity drives real-life information seeking for a specific topic that is novel and personally relevant across all information seekers. ...
... Our finding adds to research showing that information-seeking motives remain stable over timeeven during such a major impact on our lives as the COVID-19 pandemic (Kelly and Sharot, 2021). In a previous study by Lydon-Staley et al., deprivation sensitivity-a subtype of curiosity that reflects the tendency to seek information in order to relieve the uncertainty created by information gaps-was associated with the creation of knowledge networks during information seeking (Lydon-Staley et al., 2021). In addition, the authors found that the subscale joyous exploration-the motivation to generally seek out novel information-was linked to the variety of visited information (Lydon-Staley et al., 2021). ...
... In a previous study by Lydon-Staley et al., deprivation sensitivity-a subtype of curiosity that reflects the tendency to seek information in order to relieve the uncertainty created by information gaps-was associated with the creation of knowledge networks during information seeking (Lydon-Staley et al., 2021). In addition, the authors found that the subscale joyous exploration-the motivation to generally seek out novel information-was linked to the variety of visited information (Lydon-Staley et al., 2021). In line with these findings, the present study also showed that both deprivation sensitivity and joyous exploration predicted the frequency of real-life information seeking, but when compared to each other, it was deprivation sensitivity that drove this relationship. ...
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Curiosity reflects an individual’s intrinsic motivation to seek information in order to close information gaps. In laboratory-based experiments, both curiosity and information seeking have been associated with enhanced neural dynamics in the mesolimbic dopaminergic circuit. However, it is unclear whether curiosity and dopaminergic dynamics drive information seeking in real life. We investigated (i) whether curiosity predicts different characteristics of real-life information seeking and (ii) whether functional connectivity within the mesolimbic dopaminergic circuit is associated with information seeking outside the laboratory. Up to 15 months before the COVID-19 pandemic, curiosity and anxiety questionnaires, and a 10-minute resting-state fMRI session were conducted. In a follow-up survey early during the COVID-19 pandemic, participants repeated the questionnaires and completed an additional questionnaire about their COVID-19-related information seeking. Individual differences in curiosity but not anxiety were positively associated with the frequency of information-seeking behaviour. Additionally, the frequency of information seeking was predicted by individual differences in resting-state functional connectivity between the ventral tegmental area and the nucleus accumbens. The present translational study paves the way for future studies on the role of curiosity in real-life information seeking by showing that both curiosity and mesolimbic dopaminergic functional network support real-life information-seeking behaviour.
... This field originated within psycholinguistics, where models of concept representations in the human mind employed network structures way before the advent of network science [88]. Overwhelming theoretical and empirical research over the years has shown that mental representations of knowledge are highly structured [58,88] and such structure influences a variety of phenomena related to knowledge acquisition [87,89] and processing [62,64,83,90]. For instance, the length of the shortest path between any two concepts in a network, that is, network distance [89], was shown to be predictive of normative language learning in young children [58,70,89], creativity levels and curiosity in healthy populations [61,[90][91][92], word production, picture naming and lexical access in semantic and working memory in healthy [59,88] or clinical populations [64,93,94]. ...
... Overwhelming theoretical and empirical research over the years has shown that mental representations of knowledge are highly structured [58,88] and such structure influences a variety of phenomena related to knowledge acquisition [87,89] and processing [62,64,83,90]. For instance, the length of the shortest path between any two concepts in a network, that is, network distance [89], was shown to be predictive of normative language learning in young children [58,70,89], creativity levels and curiosity in healthy populations [61,[90][91][92], word production, picture naming and lexical access in semantic and working memory in healthy [59,88] or clinical populations [64,93,94]. ...
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... Overwhelming empirical research [13], [33] has supported the importance of cognitive networks. Prior work showed that these structures of human perceptions and construction organization can influence different cognitive processes, such as early word learning [34], cognitive impairments [35], writing styles [36], individual creativity levels [37] and estimates of curiosity [38]. In education settings, recent studies showed that maps of conceptual associations can be informative of students' performance [29], [33], [39]. ...
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Significance It is more important than ever to seek information adaptively. While it is optimal to acquire information based solely on its instrumental benefit, humans also often acquire useless information because of psychological motives, such as curiosity and pleasure of anticipation. Here we show that instrumental and noninstrumental motives are multiplexed in subjective value of information (SVOI) signals in human brains. Subjects’ information seeking in an economic decision-making task was captured by a model of SVOI, which reflects not only information’s instrumental benefit but also utility of anticipation it provides. SVOI was represented in traditional value regions, sharing a common code with more basic reward value. This demonstrates that valuation system combines multiple motives to drive information-seeking behavior.