scholarly journals The growth and form of knowledge networks by kinesthetic curiosity

2020 ◽  
Author(s):  
Dale Zhou ◽  
David M. Lydon-Staley ◽  
Perry Zurn ◽  
Danielle S Bassett
Keyword(s):  
Information Seeking ◽  
Statistical Physics ◽  
Network Science ◽  
Complex Space ◽  
Interdisciplinary Approach ◽  
Knowledge Networks ◽  
Immediate Feedback ◽  
Epistemic Curiosity ◽  
Self Knowledge ◽  
External Reward

Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification. The practice of curiosity can be viewed as an extended and open-ended search for valuable information with hidden identity and location in a complex space of interconnected information. Despite its importance, curiosity has been challenging to computationally model because the practice of curiosity often flourishes without specific goals, external reward, or immediate feedback. Here, we show how network science, statistical physics, and philosophy can be integrated into an approach that coheres with and expands the psychological taxonomies of specific-diversive and perceptual-epistemic curiosity. Using this interdisciplinary approach, we distill functional modes of curious information seeking as searching movements in information space. The kinesthetic model of curiosity offers a vibrant counterpart to the deliberative predictions of model-based reinforcement learning. In doing so, this model unearths new computational opportunities for identifying what makes curiosity curious.

Pattern Recognition of Travel Mobility in a City Destination: Application of Network Motif Analytics

2021 ◽  
pp. 004728752110247
Author(s):  
Sangwon Park ◽  
Ren Ridge Zhong
Keyword(s):  
Network Science ◽  
Network Motif ◽  
Interdisciplinary Approach ◽  
Movement Patterns ◽  
Urban Tourism ◽  
Lengths Of Stay ◽  
Sustainable Planning ◽  
Total Movement ◽  
City Network ◽  
Travel Behaviors

Urban tourism is considered a complex system. Tourists who visit cities have diverse purposes, leading to multifaceted travel behaviors. Understanding travel movement patterns is crucial in developing sustainable planning for urban tourism. Built on network science, this article discusses 12 key topologies of travel patterns/flow occurring in a city network by applying network motif analytics. The 12 significant types of travel mobility can account for approximately 50% of the total movement patterns. In addition, this study presents variations in travel movement patterns depending on not only different lengths of stay in topological structures of travel mobility, but also relative proportions of each type. As a result, this article suggests an interdisciplinary approach that adopts the network science method to better understand city travel behaviors. Important methodological and practical implications that could be useful for city destination planners are suggested.

scholarly journals On Curiosity: A Fundamental Aspect of Personality, a Practice of Network Growth

2018 ◽  
Vol 1 ◽  
Author(s):  
Perry Zurn ◽  
Danielle S. Bassett
Keyword(s):  
Information Seeking ◽  
Network Science ◽  
Theory Development ◽  
Mathematical Object ◽  
Knowledge Network ◽  
Fundamental Aspect ◽  
Architectural Style ◽  
Network Growth ◽  
Network Building ◽  
Definition Of

AbstractHuman personality is reflected in patterns—or networks—of behavior, either in thought or action. Curiosity is an oft-treasured component of one’s personality, commonly associated with information-seeking proclivities with distinct neurophysiological correlates. The markers of curiosity can differ substantially across people, suggesting the possibility that personality also determines the architectural style of one’s curiosity. Yet progress in defining those styles, and marking their neurophysiological basis, has been hampered by fairly fundamental difficulties in defining curiosity itself. Here, we offer and exercise a definition of the practice of curiosity as knowledge network building, one particular pattern of thought behavior. To unpack this definition and motivate its utility, we begin with a short primer on network science and describe how the mathematical object of a network can be used to map items and relations that are characteristic of bodies of knowledge. Next, we turn to a discussion of how networks grow, how their growth can be modeled, and how the practice of curiosity can be formalized as a process of network growth. We pay particular attention to how individuals may differ in how they build their knowledge networks, and discuss how the sort, manner, and action of building can be modulated by experience. We discuss how this definition of the practice of curiosity motivates new experiments and theory development at the interdisciplinary intersection of network science, personality neuroscience, education, and curiosity studies. We close with a note on the potential of network science to inform studies of other domains of personality, and the patterns of thought– or action–behavior characteristic thereof.

LinkIt: A Tool for Assessing and Providing Formative Feedback for Structural Knowledge

2021 ◽  
Vol 10 (1) ◽  
pp. 109-113
Author(s):  
Russell J. Branaghan ◽  
Roger W. Schvaneveldt ◽  
Jennifer L. Winner
Keyword(s):  
Concept Mapping ◽  
Multiple Choice ◽  
Formative Feedback ◽  
Knowledge Networks ◽  
Structural Knowledge ◽  
Immediate Feedback ◽  
Knowledge Domain ◽  
Student Knowledge ◽  
Expert Networks ◽  
And Training

It is challenging to assess the effectiveness of learning and training. Most evaluators rely on multiple choice, fill-in-the-blank, and essay questions. These are time-consuming, provide little formative value, and provide no visualization of the knowledge domain. LinkIt uses constrained concept mapping to elicit, score, represent visually, and provide immediate feedback on student knowledge networks by comparing them to expert networks.

scholarly journals The control of epistemic curiosity in the human brain

2017 ◽  
Author(s):  
Romain Ligneul ◽  
Martial Mermillod ◽  
Tiffany Morisseau
Keyword(s):  
Prefrontal Cortex ◽  
Information Seeking ◽  
Ventral Striatum ◽  
Temporal Evolution ◽  
Predictive Coding ◽  
Individual Variability ◽  
Integrative Model ◽  
Human Cognition ◽  
Fundamental Relationship ◽  
Epistemic Curiosity

AbstractEpistemic curiosity (EC) is a cornerstone of human cognition that contributes to the actualization of our cognitive potential by stimulating a myriad of information-seeking behaviours. Yet, its fundamental relationship with uncertainty remains poorly understood, which limits our ability to predict within- and between-individual variability in the willingness to acquire knowledge. Here, a two-step stochastic trivia quiz designed to induce curiosity and manipulate answer uncertainty provided behavioural and neural evidence for an integrative model of EC inspired from predictive coding. More precisely, our behavioural data indicated an inverse relationship between average surprise and EC levels, which depended upon hemodynamic activity in the rostrolateral prefrontal cortex from one trial to another and from one individual to another. Complementary, the elicitation of epistemic surprise and the relief of acute curiosity states were respectively related to ventromedial prefrontal cortex and ventral striatum activity. Taken together, our results account for the temporal evolution of EC over time, as well as for the interplay of EC, prior knowledge and surprise in controlling memory gain.

scholarly journals The Statistical Physics of Learning Revisited: Typical Learning Curves in Model Scenarios

2021 ◽  
pp. 128-142
Author(s):  
Michael Biehl
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Statistical Learning ◽  
Statistical Physics ◽  
Interdisciplinary Approach ◽  
Learning Curves ◽  
Basic Concepts ◽  
Model Scenarios

AbstractThe exchange of ideas between computer science and statistical physics has advanced the understanding of machine learning and inference significantly. This interdisciplinary approach is currently regaining momentum due to the revived interest in neural networks and deep learning. Methods borrowed from statistical mechanics complement other approaches to the theory of computational and statistical learning. In this brief review, we outline and illustrate some of the basic concepts. We exemplify the role of the statistical physics approach in terms of a particularly important contribution: the computation of typical learning curves in student teacher scenarios of supervised learning. Two, by now classical examples from the literature illustrate the approach: the learning of a linearly separable rule by a perceptron with continuous and with discrete weights, respectively. We address these prototypical problems in terms of the simplifying limit of stochastic training at high formal temperature and obtain the corresponding learning curves.

scholarly journals Statistical physics of network structure and information dynamics

2021 ◽  
Author(s):  
Arsham Ghavasieh ◽  
Manlio De Domenico
Keyword(s):  
Machine Learning ◽  
Statistical Physics ◽  
Quantum Physics ◽  
Network Science ◽  
Information Science ◽  
Wide Spectrum ◽  
Quantum Statistical Mechanics ◽  
Information Dynamics ◽  
Statistical Field ◽  
The Rich

Abstract In the last two decades, network science has proven to be an invaluable tool for the analysis of empirical systems across a wide spectrum of disciplines, with applications to data structures admitting a representation in terms of complex networks. On the one hand, especially in the last decade, an increasing number of applications based on geometric deep learning have been developed to exploit, at the same time, the rich information content of a complex network and the learning power of deep architectures, highlighting the potential of techniques at the edge between applied math and computer science. On the other hand, studies at the edge of network science and quantum physics are gaining increasing attention, e.g., because of the potential applications to quantum networks for communications, such as the quantum Internet. In this work, we briefly review a novel framework grounded on statistical physics and techniques inspired by quantum statistical mechanics which have been successfully used for the analysis of a variety of complex systems. The advantage of this framework is that it allows one to define a set of information-theoretic tools which find widely used counterparts in machine learning and quantum information science, while providing a grounded physical interpretation in terms of a statistical field theory of information dynamics. We discuss the most salient theoretical features of this framework and selected applications to protein-protein interaction networks, neuronal systems, social and transportation networks, as well as potential novel applications for quantum network science and machine learning.

scholarly journals Hunters, busybodies, and the knowledge network building associated with curiosity

2019 ◽  
Author(s):  
David M. Lydon-Staley ◽  
Dale Zhou ◽  
Ann Sizemore Blevins ◽  
Perry Zurn ◽  
Danielle S Bassett
Keyword(s):  
Information Seeking ◽  
Sensation Seeking ◽  
Small World ◽  
Well Being ◽  
Knowledge Networks ◽  
Knowledge Network ◽  
Future Research ◽  
Network Growth ◽  
Network Building ◽  
Over Time

The information gained when practicing curiosity promotes well-being over extended timescales. The open-ended and internally driven nature of curiosity, however, makes characterizing the diverse styles of information seeking that accompany it a daunting endeavor. A recently developed historicophilosophical taxonomy of curious practice distinguishes between the collection of disparate, loosely connected pieces of information and the seeking of related, tightly connected pieces of information. With this taxonomy, we use a novel knowledge network building framework of curiosity to capture styles of curious information seeking in 149 participants as they explore Wikipedia for over 5 hours spanning 21 days. We create knowledge networks in which nodes consist of distinct concepts (unique Wikipedia pages) and edges represent the similarity between the content of Wikipedia pages. We quantify the tightness of each participants' knowledge networks using graph theoretical indices and use a generative model of network growth to explore mechanisms underlying the observed information seeking. We find that participants create knowledge networks with small-world and modular structure. Deprivation sensitivity, 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. We further show that there is substantial within-person variability in knowledge network building over time and that building looser networks than usual is linked with higher than usual sensation seeking. With this framework in hand, future research can quantify the information collected during curious practice and examine its association with well-being.

scholarly journals Beauty in artistic expressions through the eyes of networks and physics

2020 ◽  
Vol 17 (164) ◽  
pp. 20190686 ◽  
Author(s):  
Matjaž Perc
Keyword(s):  
Social Sciences ◽  
Cultural History ◽  
Statistical Physics ◽  
Large Scale ◽  
Network Science ◽  
Digital Data ◽  
The Social ◽  
The Creation ◽  
The Veil

Beauty is subjective, and as such it, of course, cannot be defined in absolute terms. But we all know or feel when something is beautiful to us personally. And in such instances, methods of statistical physics and network science can be used to quantify and to better understand what it is that evokes that pleasant feeling, be it when reading a book or looking at a painting. Indeed, recent large-scale explorations of digital data have lifted the veil on many aspects of our artistic expressions that would remain forever hidden in smaller samples. From the determination of complexity and entropy of art paintings to the creation of the flavour network and the principles of food pairing, fascinating research at the interface of art, physics and network science abounds. We here review the existing literature, focusing in particular on culinary, visual, musical and literary arts. We also touch upon cultural history and culturomics, as well as on the connections between physics and the social sciences in general. The review shows that the synergies between these fields yield highly entertaining results that can often be enjoyed by layman and experts alike. In addition to its wider appeal, the reviewed research also has many applications, ranging from improved recommendation to the detection of plagiarism.

Discussing Health Issues on the Internet

2011 ◽  
pp. 78-86
Author(s):  
Jane Moon
Keyword(s):  
Health Information ◽  
Web Sites ◽  
Information Seeking ◽  
Medical Information ◽  
Medical Knowledge ◽  
The Internet ◽  
Social Changes ◽  
Immediate Feedback ◽  
Health Information Seeking Behavior ◽  
Seeking Behavior

This article provides an overview of the trend in Internet usage; in particular, the trend that relates particularly to health-information-seeking behavior. It discusses a paradigm shift in patientdoctor relationships that has resulted from social changes; that is, lack of consultation time, thirst for medical knowledge, mass-media medical information and an explosion in the number of health Web sites. The Internet has become an important medium for bridging the gap in the patient-doctor relationship. Issues of Internet quality are explored. While the Internet can help consumers by providing immediate feedback as far as treatment and medication are concerned, without proper standards and quality assurance it can give rise to diabolical consequences (Crocco, Villasis-Keever, & Jadad, 2002). Ciolek describes information on the Internet as mediocre and argues that health information on the Internet is subject to “Multi Media Mediocrity” (MMM) (Ciolek, 1997).

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