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![The Wundt curve [Berlyne 1960].](profile/Qiong-Wu-11/publication/262245493/figure/fig1/AS:482499538231296@1492048262715/The-Wundt-curve-Berlyne-1960.png)
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![Fig. 1. The Wundt curve [Berlyne 1960].](profile/Qiong-Wu-11/publication/262245493/figure/fig1/AS:482499538231296@1492048262715/The-Wundt-curve-Berlyne-1960_Q320.jpg)
Literature in psychology has shown that curiosity is the intrinsic motivation for exploration, learning, and creativity. Various forms of computational curiosity have been developed to provide artificial beings with desirable functions, such as detecting and adapting to novel inputs, making decisions related to aesthetics, and achieving pedagogical...
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... They often manifest as a preference for objects that are bright and vivid and have attention-capturing qualities (Chen et al., 2022;Kidd & Hayden, 2015;Piccardi et al., 2020). Based on the nature of the content arousing it, curiosity can be categorized as epistemic (elicited by an information gap) or perceptual (aroused by exposure to novel stimuli) (Berlyne, 1960;Wu & Miao, 2013). Epistemic curiosity is seen as a higher cognitive capability shared by humans and some other animals (Buyalskaya & Camerer, 2020). ...
Curiosity, an innate and intrinsic motivation to explore, makes vital contributions to learning in individuals of various ages. Epistemic curiosity centers on the drive to close information gaps and can be classified a joyous exploration and interest (I) and deprivation sensitivity (D) types. Each subtype is associated with different academic achievements, personality traits, emotions, and aspects of creativity. Building on the concept of epistemic curiosity in adults, the I- and D-type Epistemic Curiosity in Young Children (I/D-YC) scale was developed. The purpose of the present study was to validate the Chinese I/D-YC scale for preschoolers. Exploratory factor and confirmatory factor analyses of data from 111 parents (sample 1) indicated that the Chinese I/D-YC replicated the two-factor structure and items of the original scale. The scales’ convergent validity and reliability were examined with data from 189 parents (sample 2) and 129 teachers (sample 3), as its test–retest reliability was examined with data from 45 parents (from sample 2). The results established the Chinese I/D-YC scale as a valid and reliable measure of epistemic curious behaviors in young Chinese children. Moreover, the cultivation of epistemic curiosity should weaken inhibition and this might enhance well-being, creativity and learning, especially the D type with lower SES.
... During the post-test phase, both the number of students who wrote questions and the total number of questions increased: 15 students generated 36 questions. (22) and post-interaction (36) question writing sessions. The percentages calculated relative to the total questions asked within that session (e.g., 9 22 = 40.9%). ...
Virtual, augmented, and mixed reality for humanrobot
interaction (VAM-HRI) is a new and rapidly growing
field of research. The field of socially assistive robot (SAR) has
made impactful advances in educational settings, but has not yet
benefited from VAM-HRI advances.We developed MoveToCode
- an open-source, embodied (i.e., kinesthetic) learning visual
programming language that aims to increase student (ages
8-12) curiosity during programming. MoveToCode uses an
augmented reality (AR) autonomous robot tutor named Kuri
that models the students’ kinesthetic curiosity and acts to
promote their curiosity in programming. MoveToCode design
was informed by pilot studies and tested in Los Angeles
elementary classrooms (n = 21). Results from main study
validated our design decisions compared to the pilot study
which was conducted in a real elementary school classroom
environment (n = 15), showing an improvement in perceived robot helpfulness (median +Δ1.25 out of 5) and number of
completed exercises (median +Δ1, maximum of 11). While no
significant changes were found in pre/post student curiosity
or intention to program later in life, students wrote more
open-ended questions post-study on topics related to robots,
programming, research, and if they would like to do the activity
again. This work demonstrates the potential of using VAMHRI
in a kinesthetic context for SAR tutors, and highlights the
existing conventions and new design considerations for creating
AR applications for SAR.
... To autonomously produce human-like behaviors in artificial agents, neuroscience has inspired various architectures and algorithms for machine learning (ML), which has obtained phenomenal success in various applications [42,62,152]. Apart from leveraging the structure of neurons, the capability of AI systems should also be enhanced with biological and psychological functionality or human-level cognition to inherit the merits of human-level intelligence such as quick adaption, high sample efficiency, and trustworthy interpretation [89,137,140]. As a basic element of cognition, curiosity organically provides an intrinsic motivation driving human beings to explore the world by discovering interesting and useful information [58,118,119]. ...
... Although the applications of CDL are vast and could not be exhaustively covered in a single work, Oudeyer and Kaplan [89] presented a typology of computational intrinsic motivation which summarized various approaches of intrinsic motivation in psychology as well as computational models in reinforcement learning. Wu and Miao [137] introduced a brief computational framework for measuring curiosity and provided some research areas for future studies. However, these works are outdated as more CDL methods have emerged and become increasingly popular since then. ...
... However, it is changeling to quantitatively measure the degree of curiosity in an integrative framework. As suggested in the survey work [137], the psychological curiosity theory presented by Berlyne [10] can be introduced as the baseline of measuring artificial curiosity computationally, by determining the stimulus (received from environments) intensity with one or more collative variables. In particular, different levels of stimulus intensity correspond to different levels of curiosity with preferences. ...
Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning as inspired by human cognitive development; meanwhile, it can bridge the existing gap between AI research and practical application scenarios, such as overfitting, poor generalization, limited training samples, high computational cost, etc. As a result, curiosity-driven learning (CDL) has become increasingly popular, where agents are self-motivated to learn novel knowledge. In this paper, we first present a comprehensive review on the psychological study of curiosity and summarize a unified framework for quantifying curiosity as well as its arousal mechanism. Based on the psychological principle, we further survey the literature of existing CDL methods in the fields of Reinforcement Learning, Recommendation, and Classification, where both advantages and disadvantages as well as future work are discussed. As a result, this work provides fruitful insights for future CDL research and yield possible directions for further improvement.
... The scope can be extended towards incorporation of other opportunistically available knowledge sources as explained in (Calma et al., 2017). This is closely related to the concept of curiosity, which is discussed in detail in (Wu and Miao, 2013). ...
Intelligent systems have the ability to improve their behaviour over time taking observations, experiences or explicit feedback into account. Traditional approaches separate the learning problem and make isolated use of techniques from different field of machine learning such as reinforcement learning, active learning, anomaly detection or transfer learning, for instance. In this context, the fundamental reinforcement learning approaches come with several drawbacks that hinder their application to real-world systems: trial-and-error, purely reactive behaviour or isolated problem handling. The idea of this article is to present a concept for alleviating these drawbacks by setting up a research agenda towards what we call "active reinforcement learning" in intelligent systems.
... For humans, the capacity for conflict adaptation plays a crucial role in learning and adapting to the environment. When human toddlers detect any conflict between the current environment and their prior knowledge, they will generate curiosity and be motivated to learn new knowledge or rules (Wu and Miao, 2013). Curiosity is also important for the trial and error learning of robots . ...
The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.
... Curiosity is the intrinsic motivation for exploration (Wu and Miao 2013). Especially in social scenario, users are curious about the new information about how other people behave, feel, and think (Wu et al. 2016). ...
Curiosity affects users' selections of items, and it motivates them to explore the items regardless of their interests. This phenomenon is particularly common in social networks. However, the existing social-based recommendation methods neglect such feature in social network, and it may cause the accuracy decease in recommendation. What's more, only focusing on simulating the users' preferences can lead to information cocoons. In order to tackle the problem, we propose a novel Curiosity Enhanced Bayesian Personalized Ranking (CBPR) model. Our model makes full use of the theories of psychology to model the users' curiosity aroused when facing different opinions. The experimental results on two public datasets demonstrate the advantages of our CBPR model over the existing models.
... In general, curiosity has been computationally modeled [42] using an appraisal process where the incoming stimuli are first evaluated for their potential to provide an appropriate stimulation level. Subsequently, the stimulation level is mapped to a non-linear emotion curve called the Wundt curve [43] for deriving the curiosity level. ...
Curiosity is a vital metacognitive skill in educational contexts, leading to creativity, and a love of learning. And while many school systems increasingly undercut curiosity by teaching to the test, teachers are increasingly interested in how to evoke curiosity in their students to prepare them for a world in which lifelong learning and reskilling will be more and more important. One aspect of curiosity that has received little attention, however, is the role of peers in eliciting curiosity. We present what we believe to be the first theoretical framework that articulates an integrated socio-cognitive account of curiosity that ties observable behaviors in peers to underlying curiosity states. We make a bipartite distinction between individual and interpersonal functions that contribute to curiosity, and multimodal behaviors that fulfill these functions. We validate the proposed framework by leveraging a longitudinal latent variable modeling approach. Findings confirm a positive predictive relationship between the latent variables of individual and interpersonal functions and curiosity, with the interpersonal functions exercising a comparatively stronger influence. Prominent behavioral realizations of these functions are also discovered in a data-driven manner. We instantiate the proposed theoretical framework in a set of strategies and tactics that can be incorporated into learning technologies to indicate, evoke, and scaffold curiosity. This work is a step towards designing learning technologies that can recognize and evoke moment-by-moment curiosity during learning in social contexts and towards a more complete multimodal learning analytics. The underlying rationale is applicable more generally for developing computer support for other metacognitive and socio-emotional skills.
... However, offers inspiration for researchers curious about why people learn and explore in the absence of obvious external rewards 3 . Literature in psychology has shown that curiosity is the intrinsic motivation for exploration, learning, and creativity 4 . The views, theories, and definitions about curiosity establish common grounds on the assumption that curiosity is to learn, explore, and surrender to interesting elements. ...
Background: Improving learning and instilling an investigative personality in the student is also one of the main elements of education Therefore, a relationship has been established between curiosity and inquisitiveness, and it can be seen that at the root of this relationship, inquisitiveness plays a differentiating role in the learning of both young people and children.
Aim: This study examines the relationship between university students' course-leisure conflict and curiosity levels based on different variables.
Methods: The sample group was determined through convenience sampling and consists of 764 university students, including 406 female and 358 male participants. Level of Welfare" of the curiosity scale of the participants, and a significant difference was found only according to the "Level of Welfare" variable in the course-leisure conflict scale. A significant positive correlation was found between the participants' curiosity levels and course-leisure conflict scores.
Result: It was concluded that the curiosity levels of the participants and the course-leisure conflict scale differed according to some variables, and the curiosity levels of the participants affected the course-leisure conflicts.
... For humans, the capacity for conflict adaptation plays a crucial role in learning and adapting to the environment. When human toddlers detect any conflict between the current environment and their prior knowledge, they will generate curiosity and be motivated to learn new knowledge or rules (Wu and Miao, 2013). Curiosity is also important for the trial and error learning of robots (Hafez et al., 2019). ...
Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for intelligent computational agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.
... In the field of computer science, research in curiosity agents [32] employed the Wundt Curve to estimate the effectiveness of interestingness [24] and novelty [20]. In [24], the Wundt Curve is formulated as the difference of two sigmoid functions, reward and punishment: ...
Video contents have become a critical tool for promoting products in E-commerce. However, the lack of automatic promotional video generation solutions makes large-scale video-based promotion campaigns infeasible. The first step of automatically producing promotional videos is to generate visual storylines, which is to select the building block footage and place them in an appropriate order. This task is related to the subjective viewing experience. It is hitherto performed by human experts and thus, hard to scale. To address this problem, we propose WundtBackpack, an algorithmic approach to generate storylines based on available visual materials, which can be video clips or images. It consists of two main parts, 1) the Learnable Wundt Curve to evaluate the perceived persuasiveness based on the stimulus intensity of a sequence of visual materials, which only requires a small volume of data to train; and 2) a clustering-based backpacking algorithm to generate persuasive sequences of visual materials while considering video length constraints. In this way, the proposed approach provides a dynamic structure to empower artificial intelligence (AI) to organize video footage in order to construct a sequence of visual stimuli with persuasive power. Extensive real-world experiments show that our approach achieves close to 10% higher perceived persuasiveness scores by human testers, and 12.5% higher expected revenue compared to the best performing state-of-the-art approach.
