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Convergence in Creativity Development for Mathematical Capacity
Convergence in Creativity Development for Mathematical Capacity Ai-Girl Tan Nanyang Technological University Singapore Bharath Sriraman University of Montana (Chapter to appear in edited volume: Creativity and giftedness: Interdisciplinary perspectives and beyond; Springer Science and Business, 2016) Abstract In this chapter, we highlight the role of convergence in developing creativity and mathematical capacity. We renew our understanding of creativity from the relations of three creativity mechanisms: Convergence in divergence for emergence, and three principles of experience: Continuity, interaction and complementarity. Convergence in the context of creativity development is an incidence of learning for capacity building and knowledge construction. Examples of convergent processes in learning are: setting a plan, having a structure, and possessing coordinated capacity to complete a task. To elaborate, we refer to theories of development and creativity on how people develop their capacity in convergence (e.g., collaboration), through mathematical learning (e.g., with coherence, congruence), and for creativity (e.g., imagination). We make reference to convergent creativity of an eminent mathematician Srinivasa Ramanujan (1887-1920) for a reflection on developing creativity and building capacity for good life. Keywords: Convergence, mathematics, collaboration, creativity. Introduction Scope of the Chapter This chapter comprises three parts. In the first part we present our assumptions, mechanisms and principles of creativity and creativity development. In the second part we review briefly contemporary views on creativity development and knowledge construction. We reflect on the role of convergent creativity in developing capacity through learning a subject matter. In the third part we draw preliminary conclusions that convergent creativity is essential for knowledge construction and for good life. -
Modularity As a Concept Modern Ideas About Mental Modularity Typically Use Fodor (1983) As a Key Touchstone
COGNITIVE PROCESSING International Quarterly of Cognitive Science Mental modularity, metaphors, and the marriage of evolutionary and cognitive sciences GARY L. BRASE University of Missouri – Columbia Abstract - As evolutionary approaches in the behavioral sciences become increasingly prominent, issues arising from the proposition that the mind is a collection of modular adaptations (the multi-modular mind thesis) become even more pressing. One purpose of this paper is to help clarify some valid issues raised by this thesis and clarify why other issues are not as critical. An aspect of the cognitive sciences that appears to both promote and impair progress on this issue (in different ways) is the use of metaphors for understand- ing the mind. Utilizing different metaphors can yield different perspectives and advancement in our understanding of the nature of the human mind. A second purpose of this paper is to outline the kindred natures of cognitive science and evolutionary psychology, both of which cut across traditional academic divisions and engage in functional analyses of problems. Key words: Evolutionary Theory, Cognitive Science, Modularity, Metaphors Evolutionary approaches in the behavioral sciences have begun to influence a wide range of fields, from cognitive neuroscience (e.g., Gazzaniga, 1998), to clini- cal psychology (e.g., Baron-Cohen, 1997; McGuire and Troisi, 1998), to literary theory (e.g., Carroll, 1999). At the same time, however, there are ongoing debates about the details of what exactly an evolutionary approach – often called evolu- tionary psychology— entails (Holcomb, 2001). Some of these debates are based on confusions of terminology, implicit arguments, or misunderstandings – things that can in principle be resolved by clarifying current ideas. -
Intelligent Cognitive Assistants (ICA) Workshop Summary and Research Needs Collaborative Machines to Enhance Human Capabilities
February 7, 2018 Intelligent Cognitive Assistants (ICA) Workshop Summary and Research Needs Collaborative Machines to Enhance Human Capabilities Abstract: The research needs and opportunities to create physico-cognitive systems which work collaboratively with people are presented. Workshop Dates and Venue: November 14-15, 2017, IBM Almaden Research Center, San Jose, CA Workshop Websites: https://www.src.org/program/ica https://www.nsf.gov/nano Oakley, John / SRC [email protected] [Feb 7, 2018] ICA-2: Intelligent Cognitive Assistants Workshop Summary and Research Needs Table of Contents Executive Summary .................................................................................................................... 2 Workshop Details ....................................................................................................................... 3 Organizing Committee ............................................................................................................ 3 Background ............................................................................................................................ 3 ICA-2 Workshop Outline ......................................................................................................... 6 Research Areas Presentations ................................................................................................. 7 Session 1: Cognitive Psychology ........................................................................................... 7 Session 2: Approaches to Artificial -
Potential of Cognitive Computing and Cognitive Systems Ahmed K
Old Dominion University ODU Digital Commons Modeling, Simulation & Visualization Engineering Modeling, Simulation & Visualization Engineering Faculty Publications 2015 Potential of Cognitive Computing and Cognitive Systems Ahmed K. Noor Old Dominion University, [email protected] Follow this and additional works at: https://digitalcommons.odu.edu/msve_fac_pubs Part of the Artificial Intelligence and Robotics Commons, Cognition and Perception Commons, and the Engineering Commons Repository Citation Noor, Ahmed K., "Potential of Cognitive Computing and Cognitive Systems" (2015). Modeling, Simulation & Visualization Engineering Faculty Publications. 18. https://digitalcommons.odu.edu/msve_fac_pubs/18 Original Publication Citation Noor, A. K. (2015). Potential of cognitive computing and cognitive systems. Open Engineering, 5(1), 75-88. doi:10.1515/ eng-2015-0008 This Article is brought to you for free and open access by the Modeling, Simulation & Visualization Engineering at ODU Digital Commons. It has been accepted for inclusion in Modeling, Simulation & Visualization Engineering Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. DE GRUYTER OPEN Open Eng. 2015; 5:75–88 Vision Article Open Access Ahmed K. Noor* Potential of Cognitive Computing and Cognitive Systems Abstract: Cognitive computing and cognitive technologies 1 Introduction are game changers for future engineering systems, as well as for engineering practice and training. They are ma- The history of computing can be divided into three eras jor drivers for knowledge automation work, and the cre- ([1, 2], and Figure 1). The first was the tabulating era, with ation of cognitive products with higher levels of intelli- the early 1900 calculators and tabulating machines made gence than current smart products. -
Metaphors in Cognitive and Neurosciences Which Impact Have
Metaphors in Cognitive and Neurosciences Which impact have metaphors on scientific theories and models? Juliana Goschler, Darmstadt ([email protected]) Abstract In this article, I am going to explain how the most frequent metaphor types used in cognitive and neurosciences – reification and spatial metaphors, personification, and technological metaphors – are connected to theoretical problems that occur in these scientific disciplines. These theoretical problems include the idea of memory as space, which is connected with the difficulty of a realistic description of the mental lexicon, the problem of free will, and the mind-body problem. Because of some obvious parallels between metaphorical language and arguments made in the scientific description of the mind, I will argue that certain metaphors used in scientific language are not merely linguistic problems but deeply rooted in scientific arguments. This holds for metaphors that scientists are aware of as well as for unconsciously used metaphors. My observations are intended to highlight some characteristics of meta- phors in science. Furthermore, they provide a possible approach to investigating the connec- tion between linguistic and conceptual metaphors in complex cases as scientific models. Ich werde in diesem Artikel beschreiben, wie die häufigsten Metapherntypen in den Kognitions- und Neurowissenschaften – Reifikation und Raummetaphern, Personifizierung und Technikmetaphern – mit theoretischen Problemen dieser Disziplinen verknüpft sind. Diese theoretischen Probleme -
Emerald Jrit Jrit628638 164..182
The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/2397-7604.htm JRIT&L 12,2 Facilitating success for people with mental health issues in a college through cognitive 164 remediation therapy and social Received 19 January 2019 Revised 22 April 2019 and emotional learning Accepted 6 May 2019 Jaswant Kaur Bajwa CPLS, George Brown College – Saint James Campus, Toronto, Canada Bobby Bajwa Department of Medical Sciences, University of Western Ontario, London, Canada, and Taras Gula CPLS, George Brown College – Saint James Campus, Toronto, Canada Abstract Purpose – The purpose of this paper is to describe the components, structure and theoretical underpinnings of a cognitive remediation intervention that was delivered within a supported education program for mental health survivors. Design/methodology/approach – In total, 21 participants enrolled in the course Strengthening Memory, Concentration and Learning (PREP 1033 at George Brown College (GBC)) with the diagnosis of depression, anxiety, PTSD, ED and substance use disorder were included in the research. After a baseline assessment, participants completed 14 week cognitive remediation training (CRT) protocol that included six essential components that were integrated and implemented within the course structure of the supported education program at GBC. This was followed by a post-training assessment. Findings – Analysis of the participants’ performance on CRT protocol using computerized games showed little significant progress. However, the research found a positive change in the self-esteem of the participants that was statistically significant and the findings also aligned with the social and emotional learning framework. Research limitations/implications – One of the limitations in the research was the use of computer- assisted cognitive remediation in the form of the HappyNeuron software. -
A Distributed Computational Cognitive Model for Object Recognition
SCIENCE CHINA xx xxxx Vol. xx No. x: 1{15 doi: A Distributed Computational Cognitive Model for Object Recognition Yong-Jin Liu1∗, Qiu-Fang Fu2, Ye Liu2 & Xiaolan Fu2 1Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China, 2State Key Lab of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China Received Month Day, Year; accepted Month Day, Year Abstract Based on cognitive functionalities in human vision processing, we propose a computational cognitive model for object recognition with detailed algorithmic descriptions. The contribution of this paper is of two folds. Firstly, we present a systematic review on psychological and neurophysiological studies, which provide collective evidence for a distributed representation of 3D objects in the human brain. Secondly, we present a computational model which simulates the distributed mechanism of object vision pathway. Experimental results show that the presented computational cognitive model outperforms five representative 3D object recognition algorithms in computer science research. Keywords distributed cognition, computational model, object recognition, human vision system 1 Introduction Object recognition is one of fundamental tasks in computer vision. Many recognition algorithms have been proposed in computer science area (e.g., [1, 2, 3, 4, 5]). While the CPU processing speed can now be reached at 109 Hz, the human brain has a limited speed of 100 Hz at which the neurons process their input. However, compared to state-of-the-art computational algorithms for object recognition, human brain has a distinct advantage in object recognition, i.e., human being can accurately recognize one from unlimited variety of objects within a fraction of a second, even if the object is partially occluded or contaminated by noises. -
Reflections on the Use of Conceptual Metaphor Theory in Premodern Chinese Texts
Dao (2019) 18:323–345 https://doi.org/10.1007/s11712-019-09669-0 Metaphors of Metaphors: Reflections on the Use of Conceptual Metaphor Theory in Premodern Chinese Texts Stefano Gandolfo1 Published online: 16 July 2019 # The Author(s) 2019 Abstract In this essay, I challenge the use of Conceptual Metaphor Theory (CMT) in the premodern Chinese setting. The dominant, implicit assumption in the literature is that conclusions reached by CMT on the ways in which cognition operates can be applied in toto and without qualification onto the makers of classical Chinese texts. I want to challenge this assumption and argue that textual evidence from premodern Chinese points to a different cognitive process. Differences in the use and conceptualization of image-based thinking as well as differences in the understanding of the relationship between words, images, and the world reveal a different cognitive process. However, I do not wish to argue for a total abandonment of the insights of CMT. Rather, I call for the need to properly qualify its findings in light of premodern Chinese use and conceptions of image-based language. Keywords Conceptual metaphor. Image-based language . Classical Chinese 1 Introduction Virtually all literature that applies Conceptual Metaphor Theory (CMT) on premodern Chinese texts does so uncritically. The dominant, implicit assumption is that the conclusions reached by CMT on the ways in which cognition operates can be applied in toto and without qualification onto the makers of classical Chinese texts. I want to challenge this assumption and argue that textual evidence from premodern Chinese points to a different cognitive process. -
Creativity Through the Eyes Arousal and the Prediction of Creative Task
CREATIVITY THROUGH THE EYES 1 Creativity through the eyes Arousal and the prediction of creative task performance by Locus Coeruleus-norepinephrine modes. Matthijs Dekker U1262091 / 991987 Master thesis Communication- and Information Sciences Corporate communication and digital media Faculty Humanities Tilburg University Supervisor: dr. K.A. de Rooij Second reader: H.K. Schraffenberger BEng MsC January, 2017 CREATIVITY THROUGH THE EYES 2 Abstract In order to develop creative ideas, which are original as well as effective, several cognitive processes are required involving divergent and convergent thinking. Previous research suggested that creative task performance is influenced the release of noradrenaline, causing arousal, in the Locus Coeruleus-norepinephrine system (LC-NE). This LC-NE system mediates the changes between exploitation and exploratory control states. Previous studies suggested that activity in the LC-NE system is indicated by pupil diameter. Tonic pupil sizes are associated with an exploratory control state, whereas phasic pupil are associated with an exploitative control state. This study measured the pupil diameter of participants while they performed a creative task. Different phases in the experiment are characterized by divergent and convergent thinking. It is examined whether tonic and phasic pupil sizes can predict the creativity of generated ideas during divergent and convergent thinking. It was found that (i) phasic pupil sizes are linear and quadratic predictors of effectiveness during divergent thinking; (ii) tonic pupil sizes are linear predictors of originality during divergent thinking. These findings suggest that creativity can be predicted during divergent thinking in a creative process. CREATIVITY THROUGH THE EYES 3 Creativity through the eyes Creativity among individuals and cultures can lead to happier, fuller and healthier lives (Richards, 2010). -
The Place of Modeling in Cognitive Science
The place of modeling in cognitive science James L. McClelland Department of Psychology and Center for Mind, Brain, and Computation Stanford University James L. McClelland Department of Psychology Stanford University Stanford, CA 94305 650-736-4278 (v) / 650-725-5699 (f) [email protected] Running Head: Modeling in cognitive science Keywords: Modeling frameworks, computer simulation, connectionist models, Bayesian approaches, dynamical systems, symbolic models of cognition, hybrid models, cognitive architectures Abstract I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. As explorations, simplification is essential – it is only through simplification that we can fully understand the implications of the ideas. This is not to say that simplification has no downsides; it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000-fold increase? 1. Introduction With the inauguration of a new journal for cognitive science, thirty years after the first meeting of the Cognitive Science Society, it seems essential to consider the role of computational modeling in our discipline. -
Neuroinflammation and Functional Connectivity in Alzheimer's Disease: Interactive Influences on Cognitive Performance
Research Articles: Neurobiology of Disease Neuroinflammation and functional connectivity in Alzheimer's disease: interactive influences on cognitive performance https://doi.org/10.1523/JNEUROSCI.2574-18.2019 Cite as: J. Neurosci 2019; 10.1523/JNEUROSCI.2574-18.2019 Received: 5 October 2018 Revised: 25 March 2019 Accepted: 11 April 2019 This Early Release article has been peer-reviewed and accepted, but has not been through the composition and copyediting processes. The final version may differ slightly in style or formatting and will contain links to any extended data. Alerts: Sign up at www.jneurosci.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Copyright © 2019 Passamonti et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. 1 Neuroinflammation and functional connectivity in Alzheimer’s disease: 2 interactive influences on cognitive performance 3 4 L. Passamonti1*, K.A. Tsvetanov1*, P.S. Jones1, W.R. Bevan-Jones2, R. Arnold2, R.J. Borchert1, 5 E. Mak2, L. Su2, J.T. O’Brien2#, J.B. Rowe1,3# 6 7 Joint *first and #last authorship 8 9 10 Authors’ addresses 11 1Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK 12 2Department of Psychiatry, University of Cambridge, Cambridge, UK 13 3Cognition and Brain Sciences Unit, Medical Research Council, Cambridge, -
Conceptual Modelling and Humanities
Joint Proceedings of Modellierung 2020 Short, Workshop and Tools & Demo Papers Modellierung 2020: Short Papers 13 Conceptual Modelling and Humanities Yannic Ole Kropp,1 Bernhard Thalheim2 Abstract: Humanities are becoming a hyping field of intensive research for computer researchers. It seems that conceptual models may be the basis for development of appropriate solutions of digitalisation problems in social sciences. At the same time, humanities and social sciences can fertilise conceptual modelling. The notion of conceptual models becomes enriched. The approaches to modelling in social sciences thus result in a deeper understanding of modelling. The main aim of this paper is to learn from social sciences for conceptual modelling and to fertilise the field of conceptual modelling. 1 The Value of Conceptual Modelling 1.1 Computer science is IT system-oriented Computer system development is a complex process and needs abstraction, separation of concerns, approaches for handling complexity and mature support for communication within development teams. Models are one of the main artefacts for abstraction and complexity reduction. Computer science uses more than 50 different kinds of modelling languages and modelling approaches. Models have thus been a means for system construction for a long time. Models are widely used as an universal instrument whenever humans are involved and an understanding of computer properties is essential. They are enhanced by commonly accepted concepts and thus become conceptual models. The main deployment scenario for models and conceptual models is still system construction (with description, prescription, and coding sub-scenarios) although other scenarios became popular, e.g. documentation, communication, negotiation, conceptualisation, and learning. 1.2 Learning from Digital Hunanities Digital humanities is becoming a hyping buzzword nowadays due to digitalisation and due to over-applying computer technology.