COMPUTATIONAL MODELING IN COGNITIVE SCIENCE CONFERENCE


Computational Modeling in Cognitive Science Conference is one of the leading research topics in the international research conference domain. Computational Modeling in Cognitive Science is a conference track under the Psychology Conference which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Psychology.

internationalconference.net provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of (Psychology).

Computational Modeling in Cognitive Science is not just a call for academic papers on the topic; it can also include a conference, event, symposium, scientific meeting, academic, or workshop.

You are welcome to SUBMIT your research paper or manuscript to Computational Modeling in Cognitive Science Conference Track will be held at “Psychology Conference in Barcelona, Spain in October 2021” - “Psychology Conference in San Francisco, United States in November 2021” - “Psychology Conference in Istanbul, Turkey in November 2021” - “Psychology Conference in Singapore, Singapore in November 2021” - “Psychology Conference in Bangkok, Thailand in December 2021” - “Psychology Conference in Paris, France in December 2021” .

Computational Modeling in Cognitive Science is also a leading research topic on Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, Elsevier, Scopus, Web of Science.

XXVIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 21 - 22, 2021
BARCELONA, SPAIN

XXIX. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 02 - 03, 2021
SAN FRANCISCO, UNITED STATES

XXX. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 12 - 13, 2021
ISTANBUL, TURKEY

XXXI. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 19 - 20, 2021
SINGAPORE, SINGAPORE

XXXII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 15 - 16, 2021
BANGKOK, THAILAND

XXXIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 28 - 29, 2021
PARIS, FRANCE

FINISHED

I. INTERNATIONAL PSYCHOLOGY CONFERENCE

MARCH 19 - 20, 2019
ISTANBUL, TURKEY

FINISHED

II. INTERNATIONAL PSYCHOLOGY CONFERENCE

JUNE 26 - 27, 2019
PARIS, FRANCE

FINISHED

III. INTERNATIONAL PSYCHOLOGY CONFERENCE

AUGUST 21 - 22, 2019
LONDON, UNITED KINGDOM

FINISHED

IV. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 08 - 09, 2019
NEW YORK, UNITED STATES

FINISHED

V. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 12 - 13, 2019
ROME, ITALY

FINISHED

VI. INTERNATIONAL PSYCHOLOGY CONFERENCE

FEBRUARY 13 - 14, 2020
LONDON, UNITED KINGDOM

FINISHED

VII. INTERNATIONAL PSYCHOLOGY CONFERENCE

APRIL 15 - 16, 2020
BARCELONA, SPAIN

FINISHED

VIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

MAY 11 - 12, 2020
ISTANBUL, TURKEY

FINISHED

IX. INTERNATIONAL PSYCHOLOGY CONFERENCE

JUNE 05 - 06, 2020
SAN FRANCISCO, UNITED STATES

FINISHED

X. INTERNATIONAL PSYCHOLOGY CONFERENCE

JULY 20 - 21, 2020
PARIS, FRANCE

FINISHED

XI. INTERNATIONAL PSYCHOLOGY CONFERENCE

AUGUST 10 - 11, 2020
NEW YORK, UNITED STATES

FINISHED

XII. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 10 - 11, 2020
TOKYO, JAPAN

FINISHED

XIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 16 - 17, 2020
ZÜRICH, SWITZERLAND

FINISHED

XIV. INTERNATIONAL PSYCHOLOGY CONFERENCE

OCTOBER 21 - 22, 2020
BARCELONA, SPAIN

FINISHED

XV. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 02 - 03, 2020
SAN FRANCISCO, UNITED STATES

FINISHED

XVI. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 12 - 13, 2020
ISTANBUL, TURKEY

FINISHED

XVII. INTERNATIONAL PSYCHOLOGY CONFERENCE

NOVEMBER 19 - 20, 2020
SINGAPORE, SINGAPORE

FINISHED

XVIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 15 - 16, 2020
BANGKOK, THAILAND

FINISHED

XIX. INTERNATIONAL PSYCHOLOGY CONFERENCE

DECEMBER 28 - 29, 2020
PARIS, FRANCE

FINISHED

XX. INTERNATIONAL PSYCHOLOGY CONFERENCE

FEBRUARY 13 - 14, 2021
LONDON, UNITED KINGDOM

FINISHED

XXI. INTERNATIONAL PSYCHOLOGY CONFERENCE

APRIL 15 - 16, 2021
BARCELONA, SPAIN

FINISHED

XXII. INTERNATIONAL PSYCHOLOGY CONFERENCE

MAY 11 - 12, 2021
ISTANBUL, TURKEY

FINISHED

XXIII. INTERNATIONAL PSYCHOLOGY CONFERENCE

JUNE 05 - 06, 2021
SAN FRANCISCO, UNITED STATES

FINISHED

XXIV. INTERNATIONAL PSYCHOLOGY CONFERENCE

JULY 20 - 21, 2021
PARIS, FRANCE

FINISHED

XXV. INTERNATIONAL PSYCHOLOGY CONFERENCE

AUGUST 10 - 11, 2021
NEW YORK, UNITED STATES

FINISHED

XXVI. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 10 - 11, 2021
TOKYO, JAPAN

FINISHED

XXVII. INTERNATIONAL PSYCHOLOGY CONFERENCE

SEPTEMBER 16 - 17, 2021
ZÜRICH, SWITZERLAND

Psychology Conference Call For Papers are listed below:

Previously Published Papers on "Computational Modeling in Cognitive Science Conference"

  • Scholar Index for Research Performance Evaluation Using Multiple Criteria Decision Making Analysis
    Authors: C. Ardil, Keywords: Multiple Criteria Decision Making Analysis, MCDMA, Research Performance Evaluation, Scholar Index, h index, Science Citation Index, Science Efficiency, Cumulative Citation Index, Sciencemetrics DOI:10.5281/zenodo. Abstract: This paper aims to present an objective quantitative methodology on how to evaluate individual’s scholarly research output using multiple criteria decision analysis. A multiple criteria decision making analysis (MCDMA) methodological process is adopted to build a multiple criteria evaluation model. With the introduction of the scholar index, which gives significant information about a researcher's productivity and the scholarly impact of his or her publications in a single number (s is the number of publications with at least s citations); cumulative research citation index; the scholar index is included in the citation databases to cover the multidimensional complexity of scholarly research performance and to undertake objective evaluations with scholar index. The scholar index, one of publication activity indexes, is analyzed by considering it to be the most appropriate sciencemetric indicator which allows to smooth over many drawbacks of scholarly output assessment by mere calculation of the number of publications (quantity) and citations (quality). Hence, this study includes a set of indicators-based scholar index to be used for evaluating scholarly researchers. Google Scholar open science database was used to assess and discuss scholarly productivity and impact of researchers. Based on the experiment of computing the scholar index, and its derivative indexes for a set of researchers on open research database platform, quantitative methods of assessing scholarly research output were successfully considered to rank researchers. The proposed methodology considers the ranking, and the selection of data on which a scholarly research performance evaluation was based, the analysis of the data, and the presentation of the multiple criteria analysis results.
  • Attribute Analysis of Quick Response Code Payment Users Using Discriminant Non-negative Matrix Factorization
    Authors: Hironori Karachi, Haruka Yamashita, Keywords: Data science, non-negative matrix factorization, missing data, quality of services. DOI:10.5281/zenodo. Abstract: Recently, the system of quick response (QR) code is getting popular. Many companies introduce new QR code payment services and the services are competing with each other to increase the number of users. For increasing the number of users, we should grasp the difference of feature of the demographic information, usage information, and value of users between services. In this study, we conduct an analysis of real-world data provided by Nomura Research Institute including the demographic data of users and information of users’ usages of two services; LINE Pay, and PayPay. For analyzing such data and interpret the feature of them, Nonnegative Matrix Factorization (NMF) is widely used; however, in case of the target data, there is a problem of the missing data. EM-algorithm NMF (EMNMF) to complete unknown values for understanding the feature of the given data presented by matrix shape. Moreover, for comparing the result of the NMF analysis of two matrices, there is Discriminant NMF (DNMF) shows the difference of users features between two matrices. In this study, we combine EMNMF and DNMF and also analyze the target data. As the interpretation, we show the difference of the features of users between LINE Pay and Paypay.
  • Searching for an Effective Marketing in the Food Supplement Industry in Japan
    Authors: Michiko Miyamoto, Keywords: Functional foods, dietary supplements, marketing strategy, structural equation modeling. DOI:10.5281/zenodo. Abstract: The market for "functional foods" and "foods with functional claims" that are effective in maintaining and improving health, has expanded year by year due to the entry of major food and beverage manufacturers following the introduction of the specified health food system in 1991 in Japan. To bring health claims related products or services to the market, it is necessary to let consumers to learn about these products or services; an effective marketing through advertising are important. This research proposes a framework for an effective advertisement medium for the food supplement industry by using survey data of 2,500 people.
  • Lexicon-Based Sentiment Analysis for Stock Movement Prediction
    Authors: Zane Turner, Kevin Labille, Susan Gauch, Keywords: Lexicon, sentiment analysis, stock movement prediction., computational finance. DOI:10.5281/zenodo. Abstract: Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.
  • Protection of Cultural Heritage against the Effects of Climate Change Using Autonomous Aerial Systems Combined with Automated Decision Support
    Authors: Artur Krukowski, Emmanouela Vogiatzaki, Keywords: 3D modeling, UAS, cultural heritage, preservation. DOI:10.5281/zenodo. Abstract: The article presents an ongoing work in research projects such as SCAN4RECO or ARCH, both funded by the European Commission under Horizon 2020 program. The former one concerns multimodal and multispectral scanning of Cultural Heritage assets for their digitization and conservation via spatiotemporal reconstruction and 3D printing, while the latter one aims to better preserve areas of cultural heritage from hazards and risks. It co-creates tools that would help pilot cities to save cultural heritage from the effects of climate change. It develops a disaster risk management framework for assessing and improving the resilience of historic areas to climate change and natural hazards. Tools and methodologies are designed for local authorities and practitioners, urban population, as well as national and international expert communities, aiding authorities in knowledge-aware decision making. In this article we focus on 3D modelling of object geometry using primarily photogrammetric methods to achieve very high model accuracy using consumer types of devices, attractive both to professions and hobbyists alike.
  • Modeling Exponential Growth Activity Using Technology: A Research with Bachelor of Business Administration Students
    Authors: V. Vargas-Alejo, L. E. Montero-Moguel, Keywords: Covariation reasoning, exponential function, modeling, representations. DOI:10.5281/zenodo. Abstract: Understanding the concept of function has been important in mathematics education for many years. In this study, the models built by a group of five business administration and accounting undergraduate students when carrying out a population growth activity are analyzed. The theoretical framework is the Models and Modeling Perspective. The results show how the students included tables, graphics, and algebraic representations in their models. Using technology was useful to interpret, describe, and predict the situation. The first model, the students built to describe the situation, was linear. After that, they modified and refined their ways of thinking; finally, they created exponential growth. Modeling the activity was useful to deep on mathematical concepts such as covariation, rate of change, and exponential function also to differentiate between linear and exponential growth.
  • A Constructivist Approach and Tool for Autonomous Agent Bottom-up Sequential Learning
    Authors: Jianyong Xue, Olivier L. Georgeon, Salima Hassas, Keywords: Cognitive development, constructivist learning, hierarchical sequential learning, self-adaptation. DOI:10.5281/zenodo. Abstract: During the initial phase of cognitive development, infants exhibit amazing abilities to generate novel behaviors in unfamiliar situations, and explore actively to learn the best while lacking extrinsic rewards from the environment. These abilities set them apart from even the most advanced autonomous robots. This work seeks to contribute to understand and replicate some of these abilities. We propose the Bottom-up hiErarchical sequential Learning algorithm with Constructivist pAradigm (BEL-CA) to design agents capable of learning autonomously and continuously through interactions. The algorithm implements no assumption about the semantics of input and output data. It does not rely upon a model of the world given a priori in the form of a set of states and transitions as well. Besides, we propose a toolkit to analyze the learning process at run time called GAIT (Generating and Analyzing Interaction Traces). We use GAIT to report and explain the detailed learning process and the structured behaviors that the agent has learned on each decision making. We report an experiment in which the agent learned to successfully interact with its environment and to avoid unfavorable interactions using regularities discovered through interaction.
  • The Mechanism Underlying Empathy-Related Helping Behavior: An Investigation of Empathy-Attitude- Action Model
    Authors: Wan-Ting Liao, Angela K. Tzeng, Keywords: Affective empathy, attitude, cognitive empathy, prosocial behavior, psychopathic traits. DOI:10.5281/zenodo. Abstract: Empathy has been an important issue in psychology, education, as well as cognitive neuroscience. Empathy has two major components: cognitive and emotional. Cognitive component refers to the ability to understand others’ perspectives, thoughts, and actions, whereas emotional component refers to understand how others feel. Empathy can be induced, attitude can then be changed, and with enough attitude change, helping behavior can occur. This finding leads us to two questions: is attitude change really necessary for prosocial behavior? And, what roles cognitive and affective empathy play? For the second question, participants with different psychopathic personality (PP) traits are critical because high PP people were found to suffer only affective empathy deficit. Their cognitive empathy shows no significant difference from the control group. 132 college students voluntarily participated in the current three-stage study. Stage 1 was to collect basic information including Interpersonal Reactivity Index (IRI), Psychopathic Personality Inventory-Revised (PPI-R), Attitude Scale, Visual Analogue Scale (VAS), and demographic data. Stage two was for empathy induction with three controversial scenarios, namely domestic violence, depression with a suicide attempt, and an ex-offender. Participants read all three stories and then rewrite the stories by one of two perspectives (empathetic vs. objective). They would then complete the VAS and Attitude Scale one more time for their post-attitude and emotional status. Three IVs were introduced for data analysis: PP (High vs. Low), Responsibility (whether or not the character is responsible for what happened), and Perspective-taking (Empathic vs. Objective). Stage 3 was for the action. Participants were instructed to freely use the 17 tokens they received as donations. They were debriefed and interviewed at the end of the experiment. The major findings were people with higher empathy tend to take more action in helping. Attitude change is not necessary for prosocial behavior. The controversy of the scenarios and how familiar participants are towards target groups play very important roles. Finally, people with high PP tend to show more public prosocial behavior due to their affective empathy deficit. Pre-existing value and belief as well as recent dramatic social events seem to have a big impact and possibly reduce the effect of the independent variables (IV) in our paradigm.
  • A Goal-Driven Crime Scripting Framework
    Authors: Hashem Dehghanniri, Keywords: Attack modeling, crime commission process, crime script, situational crime prevention. DOI:10.5281/zenodo. Abstract: Crime scripting is a simple and effective crime modeling technique that aims to improve understanding of security analysts about security and crime incidents. Low-quality scripts provide a wrong, incomplete, or sophisticated understanding of the crime commission process, which oppose the purpose of their application, e.g., identifying effective and cost-efficient situational crime prevention (SCP) measures. One important and overlooked factor in generating quality scripts is the crime scripting method. This study investigates the problems within the existing crime scripting practices and proposes a crime scripting approach that contributes to generating quality crime scripts. It was validated by experienced crime scripters. This framework helps analysts develop better crime scripts and contributes to their effective application, e.g., SCP measures identification or policy-making.
  • Investigation of the Physical Computing in Computational Thinking Practices, Computer Programming Concepts and Self-Efficacy for Crosscutting Ideas in STEM Content Environments
    Authors: Sarantos Psycharis, Keywords: STEM, computational thinking, physical computing, Arduino, Labview, self-efficacy. DOI:10.5281/zenodo. Abstract: Physical Computing, as an instructional model, is applied in the framework of the Engineering Pedagogy to teach “transversal/cross-cutting ideas” in a STEM content approach. Labview and Arduino were used in order to connect the physical world with real data in the framework of the so called Computational Experiment. Tertiary prospective engineering educators were engaged during their course and Computational Thinking (CT) concepts were registered before and after the intervention across didactic activities using validated questionnaires for the relationship between self-efficacy, computer programming, and CT concepts when STEM content epistemology is implemented in alignment with the Computational Pedagogy model. Results show a significant change in students’ responses for self-efficacy for CT before and after the instruction. Results also indicate a significant relation between the responses in the different CT concepts/practices. According to the findings, STEM content epistemology combined with Physical Computing should be a good candidate as a learning and teaching approach in university settings that enhances students’ engagement in CT concepts/practices.