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

DOI:10.1038/s41562-020-00985-7
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David M. Lydon-Staley
David M. Lydon-Staley
David M. Lydon-Staley
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Dale Zhou at University of Pennsylvania
Dale Zhou
Ann Sizemore Blevins
Ann Sizemore Blevins
Ann Sizemore Blevins
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Perry Zurn
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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.
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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 seeking1–3. The information sought while acting on one’s
curiosity often has no immediate, tangible benefit4–6. Despite
a lack of immediate benefits, the tendency to frequently experience
curiosity is associated with positive well-being7–9; 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
challenges10–12. 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 anywhere”15 (p. 161). The busybody
will “frisk about, and rove about, at random, wherever they please”16
(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
Content courtesy of Springer Nature, terms of use apply. Rights reserved
... 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. ...
A Thirst for Knowledge: Grounding Curiosity, Creativity, and Aesthetics in Memory and Reward Neural Systems
Article
Full-text available
  • Jan 2023
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. ...
The Network Science Of Distributed Representational Systems
Article
  • Jan 2022
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. ...
Curiosity and Mesolimbic Functional Connectivity Drive Information Seeking in Real Life
Article
Full-text available
  • Aug 2022
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]. ...
Network psychometrics and cognitive network science open new ways for understanding math anxiety as a complex system
Article
Full-text available
  • Jun 2022
Math anxiety is a clinical pathology impairing cognitive processing in math-related contexts. Originally thought to affect only inexperienced, low-achieving students, recent investigations show how math anxiety is vastly diffused even among high-performing learners. This review of data-informed studies outlines math anxiety as a complex system that: (i) cripples well-being, self-confidence and information processing on both conscious and subconscious levels, (ii) can be transmitted by social interactions, like a pathogen, and worsened by distorted perceptions, (iii) affects roughly 20$\%$ of students in 63 out of 64 worldwide educational systems but correlates weakly with academic performance and (iv) poses a concrete threat to students’ well-being, computational literacy and career prospects in science. These patterns underline the crucial need to go beyond performance for estimating math anxiety. Recent advances in network psychometrics and cognitive network science provide ideal frameworks for detecting, interpreting and intervening upon such clinical condition. Merging education research, psychology and data science, the approaches reviewed here reconstruct psychological constructs as complex systems, represented either as multivariate correlation models (e.g. graph exploratory analysis) or as cognitive networks of semantic/emotional associations (e.g. free association networks or forma mentis networks). Not only can these interconnected networks detect otherwise hidden levels of math anxiety but—more crucially—they can unveil the specific layout of interacting factors, for example, key sources and targets, behind math anxiety in a given cohort. As discussed here, these network approaches open concrete ways for unveiling students’ perceptions, emotions and mental well-being, and can enable future powerful data-informed interventions untangling math anxiety.
... 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]. ...
Reviewing Theoretical and Generalizable Text Network Analysis: Forma Mentis Networks in Cognitive Science
Conference Paper
Full-text available
  • Nov 2022
Recommendations for network studies in learning analytics emphasize that network construction requires careful definitions of nodes, relationships between them, and network boundaries. Thus far, researchers in learning analytics have discussed how to operationalize interpersonal networks in learning settings. Analytical choices used in constructing networks of text have not been examined as much. By reviewing examples of text network analysis in learning analytics we demonstrate that convenience-based decisions for network construction are common, particularly when the ties in the text networks are defined as the co-occurrences of words or ideas. We argue that such an approach is limited in its potential to contribute to theory or generalize across studies. This submission presents an alternative approach to network representations of the text in learning settings, using the concept of Forma Mentis Networks. As reported in previous studies, Forma Mentis Networks are network representations either (1) elicited from individuals through free association tasks that capture valence or (2) constructed by analysts creating shared mental maps derived from text. Forma Mentis Networks is a theory-based and scalable approach complementary to the existing set of tools available for the analysis of teaching and learning.
... But the fact that laughter is a global and early developing phenomenon does not mean that everyone laughs in the same way or to the same degree. People likely vary in their tendency to laugh just as they vary along many other behavioural dimensions including how they explore information [5], empathize and recognize emotions [6], and connect socially [7]. Here we ask whether the frequency with which people laugh in conversation also comprises a stable individual difference and whether this individual difference predicts more personal (individual enjoyment) or social ( perceived similarity) outcomes. ...
Tendency to laugh is a stable trait: Findings from a round-robin conversation study
Article
People often laugh during conversation. Who is more responsible for the laughter, the person laughing or their partner for eliciting it? We used a round-robin design where participants ( N = 66) engaged in 10 different conversations with 10 same-gender strangers and counted the instances of laughter for each person in each conversation. After each conversation, participants rated their perceived similarity with their partner and how much they enjoyed the conversation. More than half the variability in the amount a person laughed was attributable to the person laughing—some people tend to laugh more than others. By contrast, less than 5% of the variability was attributable to the laugher's partner. We also found that the more a person laughed, the more their partners felt similar to them. Counterintuitively, laughter negatively predicted conversation enjoyment. These findings suggest that, in conversations between strangers, laughter may not be a straightforward signal of amusement, but rather a social tool. We did not find any personality predictors of how much a person laughs or elicits laughter. In summary, how much a person laughs in conversation appears to be a stable trait associated with being relatable, and is not necessarily reflective of enjoyment. This article is part of the theme issue ‘Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience’.
A Large-Scale Characterization of How Readers Browse Wikipedia
Article
Despite the importance and pervasiveness of Wikipedia as one of the largest platforms for open knowledge, surprisingly little is known about how people navigate its content when seeking information. To bridge this gap, we present the first systematic large-scale analysis of how readers browse Wikipedia. Using billions of page requests from Wikipedia’s server logs, we measure how readers reach articles, how they transition between articles, and how these patterns combine into more complex navigation paths. We find that navigation behavior is characterized by highly diverse structures. Although most navigation paths are shallow, comprising a single pageload, there is much variety, and the depth and shape of paths vary systematically with topic, device type, and time of day. We show that Wikipedia navigation paths commonly mesh with external pages as part of a larger online ecosystem, and we describe how naturally occurring navigation paths are distinct from targeted navigation in lab-based settings. Our results further suggest that navigation is abandoned when readers reach low-quality pages. Taken together, these insights contribute to a more systematic understanding of readers’ information needs and allow for improving their experience on Wikipedia and the Web in general.
Historical growth of concept networks in Wikipedia
Article
  • Dec 2022
Philosophers of science have long questioned how collective scientific knowledge grows. Although disparate answers have been posited, empirical validation has been challenging due to limitations in collecting and systematizing large historical records. Here, we introduce new methods to analyze scientific knowledge formulated as a growing network of articles on Wikipedia and their hyperlinks. We demonstrate that in Wikipedia, concept networks in subdisciplines of science do not grow by expanding from their central core to reach an ancillary periphery. Instead, science concept networks in Wikipedia grow by creating and filling knowledge gaps. Notably, the process of gap formation and closure may be valued by the scientific community, as evidenced by the fact that it produces discoveries that are more frequently awarded Nobel prizes than other processes. To determine whether and how the gap process is interrupted by paradigm shifts, we operationalize a paradigm as a particular subdivision of scientific concepts into network modules. Hence, paradigm shifts are reconfigurations of those modules. The approach allows us to identify a temporal signature in structural stability across scientific subjects in Wikipedia. In a network formulation of scientific discovery, our findings suggest that data-driven conditions underlying scientific breakthroughs depend as much on exploring uncharted gaps as on exploiting existing disciplines and support policies that encourage new interdisciplinary research.
Cognitive networks detect structural patterns and emotional complexity in suicide notes
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  • Dec 2022
Communicating one's mindset means transmitting complex relationships between concepts and emotions. Using network science and word co-occurrences, we reconstruct conceptual associations as communicated in 139 genuine suicide notes, i.e., notes left by individuals who took their lives. We find that, despite their negative context, suicide notes are surprisingly positively valenced. Through emotional profiling, their ending statements are found to be markedly more emotional than their main body: The ending sentences in suicide notes elicit deeper fear/sadness but also stronger joy/trust and anticipation than the main body. Furthermore, by using data from the Emotional Recall Task, we model emotional transitions within these notes as co-occurrence networks and compare their structure against emotional recalls from mentally healthy individuals. Supported by psychological literature, we introduce emotional complexity as an affective analog of structural balance theory, measuring how elementary cycles (closed triads) of emotion co-occurrences mix positive, negative and neutral states in narratives and recollections. At the group level, authors of suicide narratives display a higher complexity than healthy individuals, i.e., lower levels of coherently valenced emotional states in triads. An entropy measure identified a similar tendency for suicide notes to shift more frequently between contrasting emotional states. Both the groups of authors of suicide notes and healthy individuals exhibit less complexity than random expectation. Our results demonstrate that suicide notes possess highly structured and contrastive narratives of emotions, more complex than expected by null models and healthy populations.
Smokers' Curiosity Facilitates Recall of Tobacco-Related Health Information
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Curiosity promotes learning. Two open questions concern the extent to which tobacco smokers exhibit curiosity about smoking-related health information and whether this curiosity can facilitate recall of this information. Participants (n = 324 smokers; n = 280 nonsmokers) performed a Trivia Guessing Task wherein participants guessed the answers to general trivia and smoking-related trivia questions and provided ratings of their curiosity prior to viewing the answers to the questions. A subset of participants (n = 121 smokers; n = 97 nonsmokers) completed a surprise Trivia Memory Task one-week later and answered the previously-viewed questions. Results indicate that smokers are no less curious about smoking-related trivia than they are about general trivia and that curiosity about the answer to smoking-related trivia is associated with more accurate recall of smoking-related trivia answers one week later. Findings suggest that engendering states of curiosity for smoking-related information may facilitate retention of that information in smokers.
The seductive lure of curiosity: information as a motivationally salient reward
Article
Full-text available
  • Oct 2020
Humans are known to seek non-instrumental information, sometimes expending considerable effort or taking risks to receive it, for example, ‘curiosity killed the cat’. This suggests that information is highly motivationally salient. In the current article, we first review recent empirical studies that demonstrated the strong motivational lure of curiosity – people will pay and risk electric shocks for non-instrumental information; and request information that has negative emotional consequences. Then we suggest that this seductive lure of curiosity may reflect a motivational mechanism that has been discussed in the literature of reward learning: incentive salience. We present behavioral and neuroscientific evidence in support of this idea and propose two areas requiring further investigation – how incentive salience for information is instigated; and individual differences in motivational vigor.
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Curiosity is often portrayed as a desirable feature of human faculty. However, curiosity may come at a cost that sometimes puts people in harmful situations. Here, using a set of behavioural and neuroimaging experiments with stimuli that strongly trigger curiosity (for example, magic tricks), we examine the psychological and neural mechanisms underlying the motivational effect of curiosity. We consistently demonstrate that across different samples, people are indeed willing to gamble, subjecting themselves to electric shocks to satisfy their curiosity for trivial knowledge that carries no apparent instrumental value. Also, this influence of curiosity shares common neural mechanisms with that of hunger for food. In particular, we show that acceptance (compared to rejection) of curiosity-driven or incentive-driven gambles is accompanied by enhanced activity in the ventral striatum when curiosity or hunger was elicited, which extends into the dorsal striatum when participants made a decision.
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This review aims to clarify four perennial issues surrounding the concept of curiosity: its nature, conceptual distinction from situational interest, types, and educational implications. First, we argue that humans have evolved to be deeply curious to adapt to a world of uncertainty. Curiosity can be likened to an appetite for knowledge which can be satiated by specific information that fills a knowledge gap. Information-seeking behavior is determined by the expected availability of information using a cost–benefit analysis. Second, although curiosity and situational interest are often considered synonyms, we show that the two constructs differ in terms of their theoretical account, biological underpinnings, triggering factors, emotional valence, specificity of information searches, and relationship with individual interest. Unlike situational interest, which is the positive affect triggered by a wide variety of sources (e.g., autonomy, relatedness, competence), curiosity is an aversive cognitive state caused by an information gap. Situational interest follows the hedonic principle and is associated with opioid liking system in the brain. Curiosity, by contrast, is understood through drive theory and involves dopaminergic wanting system. Situational interest drives individuals to approach the stimulus while curiosity promotes the active seeking of missing information. Iterative cycles of curiosity resolution can lead to the development of individual interest. Third, we introduce two types of curiosity: curiosity for what (forward curiosity) is provoked by unpredictability, whereas curiosity for why (backward curiosity) arises from incongruity. Finally, driven by the unique characteristics of curiosity, we suggest ways to design learning environment that can nurture students’ curiosity.
Seizing an opportunity
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As the techniques available to neuroscientists to probe the inner workings of the brain become more powerful, the volume of data generated increases exponentially and the tools used to analyze these data become ever more sophisticated. Alongside this feverish press into the future comes a growing interest in the use of new data technologies to study old literary and philosophical texts. And as researchers develop new methods to unearth patterns hidden within complex data, it is natural to think of these old texts as simply more data reflecting the intricacies of the human mind, waiting to succumb to the powerful, objective, and reliable perception of machines. Here we argue that historical texts are more than prone booty to be exploited: rather, they offer researchers in neuroscience, philosophy, and literature the opportunity to work together, to learn from and enrich each other’s methodologies. Using the study of human curiosity as an illustration, we describe our vision for a collaborative approach to exploring the mysteries of the mind and human behavior.
Automatic Versus Human Navigation in Information Networks
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People regularly face tasks that can be understood as navigation in information networks, where the goal is to find a path between two given nodes. In many such situations, the navigator only gets local access to the node currently under inspection and its immediate neighbors. This lack of global information about the network notwithstanding, humans tend to be good at finding short paths, despite the fact that real-world networks are typically very large. One potential reason for this could be that humans possess vast amounts of background knowledge about the world, which they leverage to make good guesses about possible solutions. In this paper we ask the question: Are human-like high-level reasoning skills really necessary for finding short paths? To answer this question, we design a number of navigation agents without such skills, which use only simple numerical features. We evaluate the agents on the task of navigating Wikipedia, a domain for which we also possess large-scale human navigation data. We observe that the agents find shorter paths than humans on average and therefore conclude that, perhaps surprisingly, no sophisticated background knowledge or high-level reasoning is required for navigating the complex Wikipedia network.
Within-person variability in sensation-seeking during daily life: Positive associations with alcohol use and self-defined risky behaviors
Article
  • Dec 2019
Sensation-seeking is the seeking of varied, novel, and intense experiences and the willingness to take risks to engage in these experiences. Sensation-seeking is associated with engagement in risky behaviors but questions remain concerning the role of within-person variability in sensation-seeking. We use data from a 21-day daily diary from 167 participants (mean age = 25.37, SD = 7.34) to test within-person associations between sensation-seeking and both alcohol use and self-reported risk-taking. Participants also reported the riskiest behavior they engaged in each day, allowing insight into the perceived risky behaviors that participants take during daily life. Results indicate those days of higher than usual sensation-seeking are more likely to be days on which alcohol is consumed relative to days of no alcohol use. The association between day's sensation-seeking and alcohol use does not extend to the quantity of alcohol consumed. Risk-taking is higher than usual on days of higher than usual sensation-seeking. Using network science tools, we reduce 2,490 self-reports of the day's riskiest behavior to 20 communities reflecting a wide range of risk domains, including social, school, work, and drug use risks. Creating a risk-taking diversity score based on the identified domains of risk behaviors, we find that trait sensation-seeking is positively associated with greater diversity in the types of risks reported. In summary, we observe that sensation-seeking and both alcohol use and other risky behaviors are associated at the within-person level, and provide insight into the types of risks taken during the course of daily life. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Within‐Person Variability in Curiosity During Daily Life and Associations with Well‐Being
Article
  • Sep 2019
Objective: Curiosity promotes engagement in novel situations and the accruement of resources that promote well-being. An open question is the extent to which curiosity lability, the degree to which curiosity fluctuates over short timescales, impacts well-being. Method: We use data from a 21-day daily diary as well as trait measures in 167 participants (mean age = 25.37 years, SD = 7.34) to test (i) the importance of curiosity lability for depression, flourishing, and life satisfaction, (ii) day-to-day associations among curiosity and happiness, depressed mood, anxiety, and physical activity, and (iii) the role of day's mood as a mediator between physical activity and curiosity. Results: We observe positive associations among curiosity lability and depression, as well as negative associations among curiosity lability and both life satisfaction and flourishing. Curiosity is higher on days of greater happiness and physical activity, and lower on days of greater depressed mood. We find evidence consistent with day's depressed mood and happiness being mediators between physical activity and curiosity. Conclusions: Greater consistency in curiosity is associated with well-being. We identify several potential sources of augmentation and blunting of curiosity in daily life and provide support for purported mechanisms linking physical activity to curiosity via mood.
Common neural code for reward and information value
Article
  • Jun 2019
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.